flatland/contrib/training/flatland_pettingzoo_rllib.py

+from ray import tune
+from ray.tune.registry import register_env
+# from ray.rllib.utils import try_import_tf
+from ray.rllib.env.wrappers.pettingzoo_env import ParallelPettingZooEnv
+import numpy as np
+
+from flatland.contrib.interface import flatland_env
+from flatland.contrib.utils import env_generators
+
+from flatland.envs.observations import TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+
+
+# Custom observation builder with predictor, uncomment line below if you want to try this one
+observation_builder = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv(30))
+seed = 10
+np.random.seed(seed)
+wandb_log = False
+experiment_name = "flatland_pettingzoo"
+rail_env = env_generators.small_v0(seed, observation_builder)
+
+# __sphinx_doc_begin__
+
+
+def env_creator(args):
+    env = flatland_env.parallel_env(environment=rail_env, use_renderer=False)
+    return env
+
+
+if __name__ == "__main__":
+    env_name = "flatland_pettyzoo"
+
+    register_env(env_name, lambda config: ParallelPettingZooEnv(env_creator(config)))
+
+    test_env = ParallelPettingZooEnv(env_creator({}))
+    obs_space = test_env.observation_space
+    act_space = test_env.action_space
+
+    def gen_policy(i):
+        config = {
+            "gamma": 0.99,
+        }
+        return (None, obs_space, act_space, config)
+
+    policies = {"policy_0": gen_policy(0)}
+
+    policy_ids = list(policies.keys())
+
+    tune.run(
+        "PPO",
+        name="PPO",
+        stop={"timesteps_total": 5000000},
+        checkpoint_freq=10,
+        local_dir="~/ray_results/"+env_name,
+        config={
+            # Environment specific
+            "env": env_name,
+            # https://github.com/ray-project/ray/issues/10761
+            "no_done_at_end": True,
+            # "soft_horizon" : True,
+            "num_gpus": 0,
+            "num_workers": 2,
+            "num_envs_per_worker": 1,
+            "compress_observations": False,
+            "batch_mode": 'truncate_episodes',
+            "clip_rewards": False,
+            "vf_clip_param": 500.0,
+            "entropy_coeff": 0.01,
+            # effective batch_size: train_batch_size * num_agents_in_each_environment [5, 10]
+            # see https://github.com/ray-project/ray/issues/4628
+            "train_batch_size": 1000,  # 5000
+            "rollout_fragment_length": 50,  # 100
+            "sgd_minibatch_size": 100,  # 500
+            "vf_share_layers": False
+            },
+    )
+
+# __sphinx_doc_end__

flatland/contrib/training/flatland_pettingzoo_stable_baselines.py

+
+import numpy as np
+import os
+import PIL
+import shutil
+
+from stable_baselines3.ppo import MlpPolicy
+from stable_baselines3 import PPO
+
+import supersuit as ss
+
+from flatland.contrib.interface import flatland_env
+from flatland.contrib.utils import env_generators
+
+from flatland.envs.observations import TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+
+import fnmatch
+import wandb
+
+"""
+https://github.com/PettingZoo-Team/PettingZoo/blob/HEAD/tutorials/13_lines.py
+"""
+
+# Custom observation builder without predictor
+# observation_builder = GlobalObsForRailEnv()
+
+# Custom observation builder with predictor
+observation_builder = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv(30))
+seed = 10
+np.random.seed(seed)
+wandb_log = False
+experiment_name = "flatland_pettingzoo"
+
+try:
+    if os.path.isdir(experiment_name):
+        shutil.rmtree(experiment_name)
+    os.mkdir(experiment_name)
+except OSError as e:
+    print("Error: %s - %s." % (e.filename, e.strerror))
+
+# rail_env = env_generators.sparse_env_small(seed, observation_builder)
+rail_env = env_generators.small_v0(seed, observation_builder)
+
+# __sphinx_doc_begin__
+
+env = flatland_env.parallel_env(environment=rail_env, use_renderer=False)
+# env = flatland_env.env(environment = rail_env, use_renderer = False)
+
+if wandb_log:
+    run = wandb.init(project="flatland2021", entity="nilabha2007", sync_tensorboard=True, 
+                     config={}, name=experiment_name, save_code=True)
+
+env_steps = 1000  # 2 * env.width * env.height  # Code uses 1.5 to calculate max_steps
+rollout_fragment_length = 50
+env = ss.pettingzoo_env_to_vec_env_v0(env)
+# env.black_death = True
+env = ss.concat_vec_envs_v0(env, 1, num_cpus=1, base_class='stable_baselines3')
+
+model = PPO(MlpPolicy, env, tensorboard_log=f"/tmp/{experiment_name}", verbose=3, gamma=0.95, 
+    n_steps=rollout_fragment_length, ent_coef=0.01, 
+    learning_rate=5e-5, vf_coef=1, max_grad_norm=0.9, gae_lambda=1.0, n_epochs=30, clip_range=0.3,
+    batch_size=150, seed=seed)
+# wandb.watch(model.policy.action_net,log='all', log_freq = 1)
+# wandb.watch(model.policy.value_net, log='all', log_freq = 1)
+train_timesteps = 100000
+model.learn(total_timesteps=train_timesteps)
+model.save(f"policy_flatland_{train_timesteps}")
+
+# __sphinx_doc_end__
+
+model = PPO.load(f"policy_flatland_{train_timesteps}")
+
+env = flatland_env.env(environment=rail_env, use_renderer=True)
+
+if wandb_log:
+    artifact = wandb.Artifact('model', type='model')
+    artifact.add_file(f'policy_flatland_{train_timesteps}.zip')
+    run.log_artifact(artifact)
+
+
+# Model Interference
+
+seed = 100
+env.reset(random_seed=seed)
+step = 0
+ep_no = 0
+frame_list = []
+while ep_no < 1:
+    for agent in env.agent_iter():
+        obs, reward, done, info = env.last()
+        act = model.predict(obs, deterministic=True)[0] if not done else None
+        env.step(act)
+        frame_list.append(PIL.Image.fromarray(env.render(mode='rgb_array')))
+        step += 1
+        if step % 100 == 0:
+            print(f"env step:{step} and action taken:{act}")
+            completion = env_generators.perc_completion(env)
+            print("Agents Completed:", completion)
+
+    completion = env_generators.perc_completion(env)
+    print("Final Agents Completed:", completion)
+    ep_no += 1
+    frame_list[0].save(f"{experiment_name}{os.sep}pettyzoo_out_{ep_no}.gif", save_all=True, 
+                       append_images=frame_list[1:], duration=3, loop=0)
+    frame_list = []
+    env.close()
+    env.reset(random_seed=seed+ep_no)
+
+
+def find(pattern, path):
+    result = []
+    for root, dirs, files in os.walk(path):
+        for name in files:
+            if fnmatch.fnmatch(name, pattern):
+                result.append(os.path.join(root, name))
+    return result
+
+
+if wandb_log:
+    extn = "gif"
+    _video_file = f'*.{extn}'
+    _found_videos = find(_video_file, experiment_name)
+    print(_found_videos)
+    for _found_video in _found_videos:
+        wandb.log({_found_video: wandb.Video(_found_video, format=extn)})
+    run.join()

flatland/contrib/utils/deadlock_checker.py

+from typing import List
+from flatland.envs.agent_utils import EnvAgent
+from flatland.envs.step_utils.states import TrainState
+from flatland.core.grid.grid4_utils import get_new_position
+from flatland.envs.step_utils import env_utils
+
+class Deadlock_Checker:
+  def __init__(self, env):
+    self.env = env
+    self.deadlocked_agents = []
+    self.immediate_deadlocked = []
+    
+
+  def reset(self) -> None:
+    self.deadlocked_agents = []
+    self.immediate_deadlocked = []
+
+        
+  # an immediate deadlock consists of two trains "trying to pass through each other".
+  # An agent may have a free possible transition, but took a bad action and "ran into another train". This is now a deadlock, and the other free
+  # direction can not be chosen anymore!
+  def check_immediate_deadlocks(self, action_dict) -> List[EnvAgent]:
+    """
+      output: list of agents who are in immediate deadlocks
+    """
+    env = self.env
+    newly_deadlocked_agents = []
+ 
+    # TODO: check restrictions to relevant agents (status ACTIVE, etc.)
+    relevant_agents = [agent for agent in env.agents if agent.state != TrainState.DONE and agent.position is not None]
+    for agent in relevant_agents:
+      other_agents = [other_agent for other_agent in env.agents if other_agent != agent] # check if this is a good test for inequality. Maybe use handles...
+
+      # get the transitions the agent can take from his current position and orientation
+      # an indicator array of the form e.g. (0,1,1,0) meaning that he can only go to east and south, not to north and west.
+      possible_transitions = env.rail.get_transitions(*agent.position, agent.direction)
+      #print(f"possible transitions: {possible_transitions}")
+
+      # the directions are: 0(north), 1(east), 2(south) and 3(west)
+      #possible_directions = [direction for direction, flag in enumerate(possible_transitions) if flag == 1]
+      #print(f"possible directions: {possible_directions}")
+
+
+      ################### only consider direction for actually chosen action ###############################
+      new_position, new_direction = env_utils.apply_action_independent(action=action_dict[agent.handle], rail=env.rail, position=agent.position, direction=agent.direction)
+      
+      #assert new_direction in possible_directions, "Error, action leads to impossible direction"
+      assert new_position == get_new_position(agent.position, new_direction), "Error, something is wrong with new position"
+
+
+      opposed_agent_id = env.agent_positions[new_position] # TODO: check that agent_positions now works correctly in flatland V3 (i.e. gets correctly updated...)
+      # agent_positions[cell] is an agent_id if an agent is there, otherwise -1.
+      if opposed_agent_id != -1:
+        opposed_agent = env.agents[opposed_agent_id]
+
+        # other agent with opposing direction is in the way --> deadlock
+        # an opposing direction means having a different direction than our agent would have if he moved to the new cell. (180 degrees or 90 degrees to our agent)
+        if opposed_agent.direction != new_direction:
+          if agent not in newly_deadlocked_agents: # to avoid duplicates
+            newly_deadlocked_agents.append(agent)
+          if opposed_agent not in newly_deadlocked_agents: # to avoid duplicates
+            newly_deadlocked_agents.append(opposed_agent)
+     
+    self.immediate_deadlocked = newly_deadlocked_agents
+
+    return newly_deadlocked_agents
+
+
+  # main method to check for all deadlocks
+  def check_deadlocks(self, action_dict) -> List[EnvAgent]:
+    env = self.env
+
+    relevant_agents = [agent for agent in env.agents if agent.state != TrainState.DONE and agent.position is not None]
+ 
+    immediate_deadlocked = self.check_immediate_deadlocks(action_dict)
+    self.immediate_deadlocked = immediate_deadlocked
+
+   
+    deadlocked = immediate_deadlocked[:] 
+
+    # now we have to "close": each train which is blocked by another deadlocked train becomes deadlocked itself.
+    still_changing = True
+    while still_changing:
+
+      still_changing = False # will be overwritten below if a change did occur
+
+      # check if for any agent, there is a new deadlock found
+      for agent in relevant_agents:
+        #possible_transitions = env.rail.get_transitions(*agent.position, agent.direction)
+        #print(f"possible transitions: {possible_transitions}")
+
+        # the directions are: 0 (north), 1(east), 2(south) and 3(west)
+        #possible_directions = [direction for direction, flag in enumerate(possible_transitions) if flag == 1]
+        #print(f"possible directions: {possible_directions}")
+
+        new_position, new_direction = env_utils.apply_action_independent(action=action_dict[agent.handle], rail=env.rail, position=agent.position, direction=agent.direction)
+        #assert new_direction in possible_directions, "Error, action leads to impossible direction"
+        assert new_position == get_new_position(agent.position, new_direction), "Error, something is wrong with new position"
+
+        opposed_agent_id = env.agent_positions[new_position]
+      
+        if opposed_agent_id != -1: # there is an opposed agent there
+          opposed_agent = env.agents[opposed_agent_id]
+
+          if opposed_agent in deadlocked:
+            if agent not in deadlocked: # to avoid duplicates
+              deadlocked.append(agent)
+              still_changing = True
+
+    self.deadlocked_agents = deadlocked
+
+    return deadlocked
\ No newline at end of file

flatland/contrib/utils/env_generators.py

+import logging
+import random
+import numpy as np
+from typing import NamedTuple
+
+from flatland.envs.malfunction_generators import malfunction_from_params, MalfunctionParameters, ParamMalfunctionGen, no_malfunction_generator
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import sparse_rail_generator
+from flatland.envs.line_generators import sparse_line_generator
+from flatland.envs.agent_utils import TrainState
+from flatland.core.grid.grid4_utils import get_new_position
+from flatland.envs.fast_methods import fast_count_nonzero, fast_argmax
+
+MalfunctionParameters = NamedTuple('MalfunctionParameters', [('malfunction_rate', float), ('min_duration', int), ('max_duration', int)])
+
+
+def get_shortest_path_action(env,handle):
+    distance_map = env.distance_map.get()
+
+    agent = env.agents[handle]
+    if agent.status in [TrainState.WAITING, TrainState.READY_TO_DEPART,
+                        TrainState.MALFUNCTION_OFF_MAP]:
+        agent_virtual_position = agent.initial_position
+    elif agent.status in [TrainState.MALFUNCTION, TrainState.MOVING, TrainState.STOPPED]:
+        agent_virtual_position = agent.position
+    elif agent.status == TrainState.DONE:
+        agent_virtual_position = agent.target
+    else:
+        return None
+
+    if agent.position:
+        possible_transitions = env.rail.get_transitions(
+            *agent.position, agent.direction)
+    else:
+        possible_transitions = env.rail.get_transitions(
+            *agent.initial_position, agent.direction)
+
+    num_transitions = fast_count_nonzero(possible_transitions)
+
+    min_distances = []
+    for direction in [(agent.direction + i) % 4 for i in range(-1, 2)]:
+        if possible_transitions[direction]:
+            new_position = get_new_position(
+                agent_virtual_position, direction)
+            min_distances.append(
+                distance_map[handle, new_position[0],
+                             new_position[1], direction])
+        else:
+            min_distances.append(np.inf)
+
+    if num_transitions == 1:
+        observation = [0, 1, 0]
+
+    elif num_transitions == 2:
+        idx = np.argpartition(np.array(min_distances), 2)
+        observation = [0, 0, 0]
+        observation[idx[0]] = 1
+    return fast_argmax(observation) + 1
+
+
+def small_v0(random_seed, observation_builder, max_width = 35, max_height = 35):
+    random.seed(random_seed)
+    width =  30
+    height =  30
+    nr_trains = 5
+    max_num_cities = 4
+    grid_mode = False
+    max_rails_between_cities = 2
+    max_rails_in_city = 3
+
+    malfunction_rate = 0
+    malfunction_min_duration = 0
+    malfunction_max_duration = 0
+
+    rail_generator = sparse_rail_generator(max_num_cities=max_num_cities, seed=random_seed, grid_mode=False,
+                                           max_rails_between_cities=max_rails_between_cities,
+                                           max_rail_pairs_in_city=max_rails_in_city)
+
+    stochastic_data = MalfunctionParameters(malfunction_rate=malfunction_rate,  # Rate of malfunction occurence
+                                        min_duration=malfunction_min_duration,  # Minimal duration of malfunction
+                                        max_duration=malfunction_max_duration  # Max duration of malfunction
+                                        )
+    speed_ratio_map = None
+    line_generator = sparse_line_generator(speed_ratio_map)
+
+    malfunction_generator = no_malfunction_generator()
+
+    while width <= max_width and height <= max_height:
+        try:
+            env = RailEnv(width=width, height=height, rail_generator=rail_generator,
+                          line_generator=line_generator, number_of_agents=nr_trains,
+                        #   malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
+                          malfunction_generator_and_process_data=malfunction_generator,
+                          obs_builder_object=observation_builder, remove_agents_at_target=False)
+
+            print("[{}] {}x{} {} cities {} trains, max {} rails between cities, max {} rails in cities. Malfunction rate {}, {} to {} steps.".format(
+                random_seed, width, height, max_num_cities, nr_trains, max_rails_between_cities,
+                max_rails_in_city, malfunction_rate, malfunction_min_duration, malfunction_max_duration
+            ))
+
+            return env
+        except ValueError as e:
+            logging.error(f"Error: {e}")
+            width += 5
+            height += 5
+            logging.info("Try again with larger env: (w,h):", width, height)
+    logging.error(f"Unable to generate env with seed={random_seed}, max_width={max_height}, max_height={max_height}")
+    return None
+
+
+def random_sparse_env_small(random_seed, observation_builder, max_width = 45, max_height = 45):
+    random.seed(random_seed)
+    size = random.randint(0, 5)
+    width = 20 + size * 5
+    height = 20 + size * 5
+    nr_cities = 2 + size // 2 + random.randint(0, 2)
+    nr_trains = min(nr_cities * 5, 5 + random.randint(0, 5))  # , 10 + random.randint(0, 10))
+    max_rails_between_cities = 2
+    max_rails_in_cities = 3 + random.randint(0, size)
+    malfunction_rate = 30 + random.randint(0, 100)
+    malfunction_min_duration = 3 + random.randint(0, 7)
+    malfunction_max_duration = 20 + random.randint(0, 80)
+
+    rail_generator = sparse_rail_generator(max_num_cities=nr_cities, seed=random_seed, grid_mode=False,
+                                           max_rails_between_cities=max_rails_between_cities,
+                                           max_rail_pairs_in_city=max_rails_in_cities)
+
+    stochastic_data = MalfunctionParameters(malfunction_rate=malfunction_rate,  # Rate of malfunction occurence
+                                        min_duration=malfunction_min_duration,  # Minimal duration of malfunction
+                                        max_duration=malfunction_max_duration  # Max duration of malfunction
+                                        )
+
+    line_generator = sparse_line_generator({1.: 0.25, 1. / 2.: 0.25, 1. / 3.: 0.25, 1. / 4.: 0.25})
+
+    while width <= max_width and height <= max_height:
+        try:
+            env = RailEnv(width=width, height=height, rail_generator=rail_generator,
+                          line_generator=line_generator, number_of_agents=nr_trains,
+                        #   malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
+                          malfunction_generator=ParamMalfunctionGen(stochastic_data),
+                          obs_builder_object=observation_builder, remove_agents_at_target=False)
+
+            print("[{}] {}x{} {} cities {} trains, max {} rails between cities, max {} rails in cities. Malfunction rate {}, {} to {} steps.".format(
+                random_seed, width, height, nr_cities, nr_trains, max_rails_between_cities,
+                max_rails_in_cities, malfunction_rate, malfunction_min_duration, malfunction_max_duration
+            ))
+
+            return env
+        except ValueError as e:
+            logging.error(f"Error: {e}")
+            width += 5
+            height += 5
+            logging.info("Try again with larger env: (w,h):", width, height)
+    logging.error(f"Unable to generate env with seed={random_seed}, max_width={max_height}, max_height={max_height}")
+    return None
+
+
+def sparse_env_small(random_seed, observation_builder):
+    width = 30  # With of map
+    height = 30  # Height of map
+    nr_trains = 2  # Number of trains that have an assigned task in the env
+    cities_in_map = 3  # Number of cities where agents can start or end
+    seed = 10  # Random seed
+    grid_distribution_of_cities = False  # Type of city distribution, if False cities are randomly placed
+    max_rails_between_cities = 2  # Max number of tracks allowed between cities. This is number of entry point to a city
+    max_rail_in_cities = 6  # Max number of parallel tracks within a city, representing a realistic trainstation
+
+    rail_generator = sparse_rail_generator(max_num_cities=cities_in_map,
+                                        seed=seed,
+                                        grid_mode=grid_distribution_of_cities,
+                                        max_rails_between_cities=max_rails_between_cities,
+                                        max_rail_pairs_in_city=max_rail_in_cities,
+                                        )
+
+    # Different agent types (trains) with different speeds.
+    speed_ration_map = {1.: 0.25,  # Fast passenger train
+                        1. / 2.: 0.25,  # Fast freight train
+                        1. / 3.: 0.25,  # Slow commuter train
+                        1. / 4.: 0.25}  # Slow freight train
+
+    # We can now initiate the schedule generator with the given speed profiles
+
+    line_generator = sparse_rail_generator(speed_ration_map)
+
+    # We can furthermore pass stochastic data to the RailEnv constructor which will allow for stochastic malfunctions
+    # during an episode.
+
+    stochastic_data = MalfunctionParameters(malfunction_rate=1/10000,  # Rate of malfunction occurence
+                                            min_duration=15,  # Minimal duration of malfunction
+                                            max_duration=50  # Max duration of malfunction
+                                            )
+
+    rail_env = RailEnv(width=width,
+                height=height,
+                rail_generator=rail_generator,
+                line_generator=line_generator,
+                number_of_agents=nr_trains,
+                obs_builder_object=observation_builder,
+                # malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
+                malfunction_generator=ParamMalfunctionGen(stochastic_data),
+                remove_agents_at_target=True)
+
+    return rail_env
+
+def _after_step(self, observation, reward, done, info):
+    if not self.enabled: return done
+
+    if type(done)== dict:
+        _done_check = done['__all__']
+    else:
+        _done_check =  done
+    if _done_check and self.env_semantics_autoreset:
+        # For envs with BlockingReset wrapping VNCEnv, this observation will be the first one of the new episode
+        self.reset_video_recorder()
+        self.episode_id += 1
+        self._flush()
+
+    # Record stats - Disabled as it causes error in multi-agent set up
+    # self.stats_recorder.after_step(observation, reward, done, info)
+    # Record video
+    self.video_recorder.capture_frame()
+
+    return done
+
+
+def perc_completion(env):
+    tasks_finished = 0
+    if hasattr(env, "agents_data"):
+        agent_data = env.agents_data
+    else:
+        agent_data = env.agents
+    for current_agent in agent_data:
+        if current_agent.status == TrainState.DONE:
+            tasks_finished += 1
+
+    return 100 * np.mean(tasks_finished / max(
+                                1, len(agent_data)))

flatland/contrib/wrappers/flatland_wrappers.py

+from collections import defaultdict
+from typing import Dict, Tuple
+
+from flatland.contrib.utils.deadlock_checker import Deadlock_Checker
+from flatland.core.grid.grid4_utils import get_new_position
+from flatland.envs.agent_utils import EnvAgent
+from flatland.envs.fast_methods import fast_count_nonzero
+from flatland.envs.rail_env import RailEnv, RailEnvActions
+from flatland.envs.step_utils.states import TrainState
+
+
+def possible_actions_sorted_by_distance(env: RailEnv, handle: int):
+    agent = env.agents[handle]
+
+    if agent.state == TrainState.READY_TO_DEPART:
+        agent_virtual_position = agent.initial_position
+    elif agent.state.is_on_map_state():
+        agent_virtual_position = agent.position
+    else:
+        print("no action possible!")
+        print("agent state: ", agent.state)
+        # NEW: if agent is at target, DO_NOTHING, and distance is zero.
+        # NEW: (needs to be tested...)
+        return [(RailEnvActions.DO_NOTHING, 0)] * 2
+
+    possible_transitions = env.rail.get_transitions(*agent_virtual_position, agent.direction)
+    print(f"possible transitions: {possible_transitions}")
+    distance_map = env.distance_map.get()[handle]
+    possible_steps = []
+    for movement in list(range(4)):
+        if possible_transitions[movement]:
+            if movement == agent.direction:
+                action = RailEnvActions.MOVE_FORWARD
+            elif movement == (agent.direction + 1) % 4:
+                action = RailEnvActions.MOVE_RIGHT
+            elif movement == (agent.direction - 1) % 4:
+                action = RailEnvActions.MOVE_LEFT
+            else:
+                print(f"An error occured. movement is: {movement}, agent direction is: {agent.direction}")
+                if movement == (agent.direction + 2) % 4 or (movement == agent.direction - 2) % 4:
+                    print("it seems that we are turning by 180 degrees. Turning in a dead end?")
+
+                action = RailEnvActions.MOVE_FORWARD
+
+            distance = distance_map[get_new_position(agent_virtual_position, movement) + (movement,)]
+            possible_steps.append((action, distance))
+    possible_steps = sorted(possible_steps, key=lambda step: step[1])
+
+
+    # if there is only one path to target, this is both the shortest one and the second shortest path.
+    if len(possible_steps) == 1:
+        return possible_steps * 2
+    else:
+        return possible_steps
+
+
+class RailEnvWrapper:
+  def __init__(self, env:RailEnv):
+    self.env = env
+
+    assert self.env is not None
+    assert self.env.rail is not None, "Reset original environment first!"
+    assert self.env.agents is not None, "Reset original environment first!"
+    assert len(self.env.agents) > 0, "Reset original environment first!"
+
+  # @property
+  # def number_of_agents(self):
+  #   return self.env.number_of_agents
+
+  # @property
+  # def agents(self):
+  #   return self.env.agents
+
+  # @property
+  # def _seed(self):
+  #   return self.env._seed
+
+  # @property
+  # def obs_builder(self):
+  #   return self.env.obs_builder
+
+  def __getattr__(self, name):
+    try:
+      return super().__getattr__(self,name)
+    except:
+      """Expose any other attributes of the underlying environment."""
+      return getattr(self.env, name)
+
+
+  @property
+  def rail(self):
+    return self.env.rail
+
+  @property
+  def width(self):
+    return self.env.width
+
+  @property
+  def height(self):
+    return self.env.height
+
+  @property
+  def agent_positions(self):
+    return self.env.agent_positions
+
+  def get_num_agents(self):
+    return self.env.get_num_agents()
+
+  def get_agent_handles(self):
+    return self.env.get_agent_handles()
+
+  def step(self, action_dict: Dict[int, RailEnvActions]):
+    return self.env.step(action_dict)
+
+  def reset(self, **kwargs):
+    obs, info = self.env.reset(**kwargs)
+    return obs, info
+
+
+class ShortestPathActionWrapper(RailEnvWrapper):
+
+    def __init__(self, env:RailEnv):
+        super().__init__(env)
+
+    def step(self, action_dict: Dict[int, RailEnvActions]) -> Tuple[Dict, Dict, Dict, Dict]:
+
+      # input: action dict with actions in [0, 1, 2].
+      transformed_action_dict = {}
+      for agent_id, action in action_dict.items():
+          if action == 0:
+              transformed_action_dict[agent_id] = action
+          else:
+              #assert action in [1, 2]
+              #assert possible_actions_sorted_by_distance(self.env, agent_id) is not None
+              #assert possible_actions_sorted_by_distance(self.env, agent_id)[action - 1] is not None
+              transformed_action_dict[agent_id] = possible_actions_sorted_by_distance(self.env, agent_id)[action - 1][0]
+
+      obs, rewards, dones, info = self.env.step(transformed_action_dict)
+      return obs, rewards, dones, info
+
+
+
+def find_all_cells_where_agent_can_choose(env: RailEnv):
+    """
+    input: a RailEnv (or something which behaves similarly, e.g. a wrapped RailEnv),
+    WHICH HAS BEEN RESET ALREADY!
+    (o.w., we call env.rail, which is None before reset(), and crash.)
+    """
+    switches = []
+    switches_neighbors = []
+    directions = list(range(4))
+    for h in range(env.height):
+        for w in range(env.width):
+            pos = (h, w)
+
+            is_switch = False
+            # Check for switch: if there is more than one outgoing transition
+            for orientation in directions:
+                possible_transitions = env.rail.get_transitions(*pos, orientation)
+                num_transitions = fast_count_nonzero(possible_transitions)
+                if num_transitions > 1:
+                    switches.append(pos)
+                    is_switch = True
+                    break
+            if is_switch:
+                # Add all neighbouring rails, if pos is a switch
+                for orientation in directions:
+                    possible_transitions = env.rail.get_transitions(*pos, orientation)
+                    for movement in directions:
+                        if possible_transitions[movement]:
+                            switches_neighbors.append(get_new_position(pos, movement))
+
+    decision_cells = switches + switches_neighbors
+    return tuple(map(set, (switches, switches_neighbors, decision_cells)))
+
+
+class SkipNoChoiceCellsWrapper(RailEnvWrapper):
+
+    # env can be a real RailEnv, or anything that shares the same interface
+    # e.g. obs, rewards, dones, info = env.step(action_dict) and obs, info = env.reset(), and so on.
+    def __init__(self, env:RailEnv, accumulate_skipped_rewards: bool, discounting: float) -> None:
+      super().__init__(env)
+      # save these so they can be inspected easier.
+      self.accumulate_skipped_rewards = accumulate_skipped_rewards
+      self.discounting = discounting
+      self.switches = None
+      self.switches_neighbors = None
+      self.decision_cells = None
+      self.skipped_rewards = defaultdict(list)
+
+      # sets initial values for switches, decision_cells, etc.
+      self.reset_cells()
+
+
+    def on_decision_cell(self, agent: EnvAgent) -> bool:
+      return agent.position is None or agent.position == agent.initial_position or agent.position in self.decision_cells
+
+    def on_switch(self, agent: EnvAgent) -> bool:
+      return agent.position in self.switches
+
+    def next_to_switch(self, agent: EnvAgent) -> bool:
+      return agent.position in self.switches_neighbors
+
+    def reset_cells(self) -> None:
+      self.switches, self.switches_neighbors, self.decision_cells = find_all_cells_where_agent_can_choose(self.env)
+
+    def step(self, action_dict: Dict[int, RailEnvActions]) -> Tuple[Dict, Dict, Dict, Dict]:
+      o, r, d, i = {}, {}, {}, {}
+
+      # need to initialize i["..."]
+      # as we will access i["..."][agent_id]
+      i["action_required"] = dict()
+      i["malfunction"] = dict()
+      i["speed"] = dict()
+      i["state"] = dict()
+
+      while len(o) == 0:
+        obs, reward, done, info = self.env.step(action_dict)
+
+        for agent_id, agent_obs in obs.items():
+          if done[agent_id] or self.on_decision_cell(self.env.agents[agent_id]):
+            o[agent_id] = agent_obs
+            r[agent_id] = reward[agent_id]
+            d[agent_id] = done[agent_id]
+
+            i["action_required"][agent_id] = info["action_required"][agent_id]
+            i["malfunction"][agent_id] = info["malfunction"][agent_id]
+            i["speed"][agent_id] = info["speed"][agent_id]
+            i["state"][agent_id] = info["state"][agent_id]
+
+            if self.accumulate_skipped_rewards:
+              discounted_skipped_reward = r[agent_id]
+
+              for skipped_reward in reversed(self.skipped_rewards[agent_id]):
+                discounted_skipped_reward = self.discounting * discounted_skipped_reward + skipped_reward
+
+              r[agent_id] = discounted_skipped_reward
+              self.skipped_rewards[agent_id] = []
+
+          elif self.accumulate_skipped_rewards:
+            self.skipped_rewards[agent_id].append(reward[agent_id])
+          # end of for-loop
+
+        d['__all__'] = done['__all__']
+        action_dict = {}
+        # end of while-loop
+
+      return o, r, d, i
+
+
+    def reset(self, **kwargs) -> Tuple[Dict, Dict]:
+      obs, info = self.env.reset(**kwargs)
+      # resets decision cells, switches, etc. These can change with an env.reset(...)!
+      # needs to be done after env.reset().
+      self.reset_cells()
+      return obs, info
+
+
+
+
+class DeadlockWrapper(RailEnvWrapper):
+    def __init__(self, env:RailEnv, deadlock_reward=-100) -> None:
+        super().__init__(env)
+        self.deadlock_reward = deadlock_reward
+        self.deadlock_checker = Deadlock_Checker(env=self.env)
+
+    @property
+    def deadlocked_agents(self):
+      return self.deadlock_checker.deadlocked_agents
+
+    @property
+    def immediate_deadlocks(self):
+      return [agent.handle for agent in self.deadlock_checker.immediate_deadlocked]
+
+    # make sure to assign the deadlock reward only once to each deadlocked agent...
+    def step(self, action_dict: Dict[int, RailEnvActions]) -> Tuple[Dict, Dict, Dict, Dict]:
+
+        # agents which are already deadlocked from previous steps
+        already_deadlocked_ids = [agent.handle for agent in self.deadlocked_agents]
+
+        # step environment
+        obs, rewards, dones, info = self.env.step(action_dict)
+
+        # compute new list of deadlocked agents (ids) after stepping the environment
+        deadlocked_agents = self.deadlock_checker.check_deadlocks(action_dict) # also stored in self.deadlocked_checker.deadlocked_agents
+        deadlocked_agents_ids = [agent.handle for agent in deadlocked_agents]
+        # immediate deadlocked ids only used for prints
+        immediate_deadlocked_ids = [agent.handle for agent in self.deadlock_checker.immediate_deadlocked]
+        print(f"immediate deadlocked: {immediate_deadlocked_ids}")
+        print(f"total deadlocked: {deadlocked_agents_ids}")
+        newly_deadlocked_agents_ids = [agent_id for agent_id in deadlocked_agents_ids if agent_id not in already_deadlocked_ids]
+
+        # assign deadlock rewards
+        for agent_id in newly_deadlocked_agents_ids:
+          print(f"assigning deadlock reward of {self.deadlock_reward} to agent {agent_id}")
+          rewards[agent_id] = self.deadlock_reward
+
+        return obs, rewards, dones, info
+
+    def reset(self, **kwargs) -> Tuple[Dict, Dict]:
+      self.deadlock_checker.reset() # sets all lists of deadlocked agents to empty list
+      obs, info = super().reset(**kwargs)
+      return obs, info

flatland/core/env.py

@@ -11,11 +11,11 @@ class Environment:
 
     Derived environments should implement the following attributes:
         action_space: tuple with the dimensions of the actions to be passed to the step method
-        observation_space: tuple with the dimensions of the observations returned by reset and step
 
     Agents are identified by agent ids (handles).
     Examples:
-        >>> obs = env.reset()
+
+        >>> obs, info = env.reset()
         >>> print(obs)
         {
             "train_0": [2.4, 1.6],
@@ -40,18 +40,19 @@ class Environment:
             "train_0": {},  # info for train_0
             "train_1": {},  # info for train_1
         }
+
     """
 
     def __init__(self):
         self.action_space = ()
-        self.observation_space = ()
         pass
 
     def reset(self):
         """
         Resets the env and returns observations from agents in the environment.
 
-        Returns:
+        Returns
+        -------
         obs : dict
             New observations for each agent.
         """
@@ -66,7 +67,7 @@ class Environment:
         The returns are dicts mapping from agent_id strings to values.
 
         Parameters
-        -------
+        ----------
         action_dict : dict
             Dictionary of actions to execute, indexed by agent id.
 

flatland/core/env_observation_builder.py

@@ -2,27 +2,29 @@
 ObservationBuilder objects are objects that can be passed to environments designed for customizability.
 The ObservationBuilder-derived custom classes implement 2 functions, reset() and get() or get(handle).
 
-+ Reset() is called after each environment reset, to allow for pre-computing relevant data.
++ `reset()` is called after each environment reset, to allow for pre-computing relevant data.
+
++ `get()` is called whenever an observation has to be computed, potentially for each agent independently in case of \
+multi-agent environments.
 
-+ Get() is called whenever an observation has to be computed, potentially for each agent independently in
-case of multi-agent environments.
 """
+from typing import Optional, List
+
 import numpy as np
 
+from flatland.core.env import Environment
+
 
 class ObservationBuilder:
     """
     ObservationBuilder base class.
-
-    Derived objects must implement and `observation_space' attribute as a tuple with the dimensions of the returned
-    observations.
     """
 
     def __init__(self):
-        self.observation_space = ()
+        self.env = None
 
-    def _set_env(self, env):
-        self.env = env
+    def set_env(self, env: Environment):
+        self.env: Environment = env
 
     def reset(self):
         """
@@ -30,35 +32,37 @@ class ObservationBuilder:
         """
         raise NotImplementedError()
 
-    def get_many(self, handles=[]):
+    def get_many(self, handles: Optional[List[int]] = None):
         """
-        Called whenever an observation has to be computed for the `env' environment, for each agent with handle
-        in the `handles' list.
+        Called whenever an observation has to be computed for the `env` environment, for each agent with handle
+        in the `handles` list.
 
         Parameters
-        -------
-        handles : list of handles (optional)
+        ----------
+        handles : list of handles, optional
             List with the handles of the agents for which to compute the observation vector.
 
         Returns
         -------
         function
             A dictionary of observation structures, specific to the corresponding environment, with handles from
-            `handles' as keys.
+            `handles` as keys.
         """
         observations = {}
+        if handles is None:
+            handles = []
         for h in handles:
             observations[h] = self.get(h)
         return observations
 
-    def get(self, handle=0):
+    def get(self, handle: int = 0):
         """
-        Called whenever an observation has to be computed for the `env' environment, possibly
-        for each agent independently (agent id `handle').
+        Called whenever an observation has to be computed for the `env` environment, possibly
+        for each agent independently (agent id `handle`).
 
         Parameters
-        -------
-        handle : int (optional)
+        ----------
+        handle : int, optional
             Handle of the agent for which to compute the observation vector.
 
         Returns
@@ -82,16 +86,13 @@ class DummyObservationBuilder(ObservationBuilder):
     """
 
     def __init__(self):
-        self.observation_space = ()
-
-    def _set_env(self, env):
-        self.env = env
+        super().__init__()
 
     def reset(self):
         pass
 
-    def get_many(self, handles=[]):
+    def get_many(self, handles: Optional[List[int]] = None) -> bool:
         return True
 
-    def get(self, handle=0):
+    def get(self, handle: int = 0) -> bool:
         return True

flatland/core/env_prediction_builder.py

@@ -3,11 +3,12 @@ PredictionBuilder objects are objects that can be passed to environments designe
 The PredictionBuilder-derived custom classes implement 2 functions, reset() and get([handle]).
 If predictions are not required in every step or not for all agents, then
 
-+ Reset() is called after each environment reset, to allow for pre-computing relevant data.
++ `reset()` is called after each environment reset, to allow for pre-computing relevant data.
 
-+ Get() is called whenever an step has to be computed, potentially for each agent independently in
++ `get()` is called whenever an step has to be computed, potentially for each agent independently in \
 case of multi-agent environments.
 """
+from flatland.core.env import Environment
 
 
 class PredictionBuilder:
@@ -18,8 +19,9 @@ class PredictionBuilder:
 
     def __init__(self, max_depth: int = 20):
         self.max_depth = max_depth
+        self.env = None
 
-    def _set_env(self, env):
+    def set_env(self, env: Environment):
         self.env = env
 
     def reset(self):
@@ -28,16 +30,13 @@ class PredictionBuilder:
         """
         pass
 
-    def get(self, custom_args=None, handle=0):
+    def get(self, handle: int = 0):
         """
         Called whenever get_many in the observation build is called.
 
         Parameters
-        -------
-        custom_args: dict
-            Implementation-dependent custom arguments, see the sub-classes.
-
-        handle : int (optional)
+        ----------
+        handle : int, optional
             Handle of the agent for which to compute the observation vector.
 
         Returns

flatland/core/grid/grid4.py

 from enum import IntEnum
-from typing import Type
+from functools import lru_cache
+from typing import Type, List
 
 import numpy as np
 
 from flatland.core.transitions import Transitions
 
 
+# maxsize=None can be used because the number of possible transition is limited (16 bit encoded) and the
+# direction/orientation is also limited (2bit). Where the 16bit are only sparse used = number of rail types
+# Those methods can be cached -> the are independant of the railways (env)
+@lru_cache(maxsize=128)
+def fast_grid4_get_transitions(cell_transition, orientation):
+    bits = (cell_transition >> ((3 - orientation) * 4))
+    return ((bits >> 3) & 1, (bits >> 2) & 1, (bits >> 1) & 1, (bits) & 1)
+
+
+@lru_cache(maxsize=128)
+def fast_grid4_get_transition(cell_transition, orientation, direction):
+    return ((cell_transition >> ((4 - 1 - orientation) * 4)) >> (4 - 1 - direction)) & 1
+
+
+@lru_cache(maxsize=128)
+def fast_grid4_set_transitions(cell_transition, orientation, new_transitions):
+    mask = (1 << ((4 - orientation) * 4)) - (1 << ((3 - orientation) * 4))
+    negmask = ~mask
+
+    new_transitions = \
+        (new_transitions[0] & 1) << 3 | \
+        (new_transitions[1] & 1) << 2 | \
+        (new_transitions[2] & 1) << 1 | \
+        (new_transitions[3] & 1)
+
+    cell_transition = (cell_transition & negmask) | (new_transitions << ((3 - orientation) * 4))
+
+    return cell_transition
+
+
+@lru_cache(maxsize=128)
+def fast_grid4_remove_deadends(cell_transition):
+    """
+    Remove all turn-arounds (e.g. N-S, S-N, E-W,...).
+    """
+    maskDeadEnds = Grid4Transitions.maskDeadEnds()
+    cell_transition &= cell_transition & (~maskDeadEnds) & 0xffff
+    return cell_transition
+
+
+@lru_cache(maxsize=128)
+def fast_grid4_rotate_transition(cell_transition, rotation=0):
+    value = cell_transition
+    rotation = rotation // 90
+    for i in range(4):
+        block_tuple = fast_grid4_get_transitions(value, i)
+        block_tuple = block_tuple[(4 - rotation):] + block_tuple[:(4 - rotation)]
+        value = fast_grid4_set_transitions(value, i, block_tuple)
+
+    # Rotate the 4-bits blocks
+    value = ((value & (2 ** (rotation * 4) - 1)) << ((4 - rotation) * 4)) | (
+        value >> (rotation * 4))
+
+    cell_transition = value
+    return cell_transition
+
+
 class Grid4TransitionsEnum(IntEnum):
     NORTH = 0
     EAST = 1
@@ -24,9 +82,9 @@ class Grid4Transitions(Transitions):
     """
     Grid4Transitions class derived from Transitions.
 
-    Special case of `Transitions' over a 2D-grid (FlatLand).
+    Special case of `Transitions` over a 2D-grid (FlatLand).
     Transitions are possible to neighboring cells on the grid if allowed.
-    GridTransitions keeps track of valid transitions supplied as `transitions'
+    GridTransitions keeps track of valid transitions supplied as `transitions`
     list, each represented as a bitmap of 16 bits.
 
     Whether a transition is allowed or not depends on which direction an agent
@@ -57,8 +115,11 @@ class Grid4Transitions(Transitions):
         # row,col delta for each direction
         self.gDir2dRC = np.array([[-1, 0], [0, 1], [1, 0], [0, -1]])
 
-        # These bits represent all the possible dead ends
-        self.maskDeadEnds = 0b0010000110000100
+    # These bits represent all the possible dead ends
+    @staticmethod
+    @lru_cache()
+    def maskDeadEnds():
+        return 0b0010000110000100
 
     def get_type(self):
         return np.uint16
@@ -67,8 +128,8 @@ class Grid4Transitions(Transitions):
         """
         Get the 4 possible transitions ((N,E,S,W), 4 elements tuple
         if no diagonal transitions allowed) available for an agent oriented
-        in direction `orientation' and inside a cell with
-        transitions `cell_transition'.
+        in direction `orientation` and inside a cell with
+        transitions `cell_transition`.
 
         Parameters
         ----------
@@ -83,16 +144,15 @@ class Grid4Transitions(Transitions):
             List of the validity of transitions in the cell.
 
         """
-        bits = (cell_transition >> ((3 - orientation) * 4))
-        return ((bits >> 3) & 1, (bits >> 2) & 1, (bits >> 1) & 1, (bits) & 1)
+        return fast_grid4_get_transitions(cell_transition, orientation)
 
     def set_transitions(self, cell_transition, orientation, new_transitions):
         """
         Set the possible transitions (e.g., (N,E,S,W), 4 elements tuple
         if no diagonal transitions allowed) available for an agent
-        oriented in direction `orientation' and inside a cell with transitions
-        `cell_transition'. A new `cell_transition' is returned with
-        the specified bits replaced by `new_transitions'.
+        oriented in direction `orientation` and inside a cell with transitions
+        `cell_transition'. A new `cell_transition` is returned with
+        the specified bits replaced by `new_transitions`.
 
         Parameters
         ----------
@@ -107,28 +167,17 @@ class Grid4Transitions(Transitions):
         -------
         int
             An updated bitmap that replaces the original transitions validity
-            of `cell_transition' with `new_transitions', for the appropriate
-            `orientation'.
+            of `cell_transition' with `new_transitions`, for the appropriate
+            `orientation`.
 
         """
-        mask = (1 << ((4 - orientation) * 4)) - (1 << ((3 - orientation) * 4))
-        negmask = ~mask
-
-        new_transitions = \
-            (new_transitions[0] & 1) << 3 | \
-            (new_transitions[1] & 1) << 2 | \
-            (new_transitions[2] & 1) << 1 | \
-            (new_transitions[3] & 1)
-
-        cell_transition = (cell_transition & negmask) | (new_transitions << ((3 - orientation) * 4))
-
-        return cell_transition
+        return fast_grid4_set_transitions(cell_transition, orientation, new_transitions)
 
     def get_transition(self, cell_transition, orientation, direction):
         """
         Get the transition bit (1 value) that determines whether an agent
-        oriented in direction `orientation' and inside a cell with transitions
-        `cell_transition' can move to the cell in direction `direction'
+        oriented in direction `orientation` and inside a cell with transitions
+        `cell_transition' can move to the cell in direction `direction`
         relative to the current cell.
 
         Parameters
@@ -146,13 +195,14 @@ class Grid4Transitions(Transitions):
             Validity of the requested transition: 0/1 allowed/not allowed.
 
         """
-        return ((cell_transition >> ((4 - 1 - orientation) * 4)) >> (4 - 1 - direction)) & 1
+        return fast_grid4_get_transition(cell_transition, orientation, direction)
 
-    def set_transition(self, cell_transition, orientation, direction, new_transition, remove_deadends=False):
+    def set_transition(self, cell_transition, orientation, direction, new_transition,
+                       remove_deadends=False):
         """
         Set the transition bit (1 value) that determines whether an agent
-        oriented in direction `orientation' and inside a cell with transitions
-        `cell_transition' can move to the cell in direction `direction'
+        oriented in direction `orientation` and inside a cell with transitions
+        `cell_transition' can move to the cell in direction `direction`
         relative to the current cell.
 
         Parameters
@@ -171,8 +221,8 @@ class Grid4Transitions(Transitions):
         -------
         int
             An updated bitmap that replaces the original transitions validity
-            of `cell_transition' with `new_transitions', for the appropriate
-            `orientation'.
+            of `cell_transition' with `new_transitions`, for the appropriate
+            `orientation`.
 
         """
         if new_transition:
@@ -181,7 +231,7 @@ class Grid4Transitions(Transitions):
             cell_transition &= ~(1 << ((4 - 1 - orientation) * 4 + (4 - 1 - direction)))
 
         if remove_deadends:
-            cell_transition = self.remove_deadends(cell_transition)
+            cell_transition = fast_grid4_remove_deadends(cell_transition)
 
         return cell_transition
 
@@ -196,7 +246,7 @@ class Grid4Transitions(Transitions):
             16 bits used to encode the valid transitions for a cell.
         rotation : int
             Angle by which to clock-wise rotate the transition bits in
-            `cell_transition' by. I.e., rotation={0, 90, 180, 270} degrees.
+            `cell_transition` by. I.e., rotation={0, 90, 180, 270} degrees.
 
         Returns
         -------
@@ -206,27 +256,18 @@ class Grid4Transitions(Transitions):
 
         """
         # Rotate the individual bits in each block
-        value = cell_transition
-        rotation = rotation // 90
-        for i in range(4):
-            block_tuple = self.get_transitions(value, i)
-            block_tuple = block_tuple[(4 - rotation):] + block_tuple[:(4 - rotation)]
-            value = self.set_transitions(value, i, block_tuple)
-
-        # Rotate the 4-bits blocks
-        value = ((value & (2 ** (rotation * 4) - 1)) << ((4 - rotation) * 4)) | (value >> (rotation * 4))
-
-        cell_transition = value
-        return cell_transition
+        return fast_grid4_rotate_transition(cell_transition, rotation)
 
     def get_direction_enum(self) -> Type[Grid4TransitionsEnum]:
         return Grid4TransitionsEnum
 
-    def has_deadend(self, cell_transition):
+    @staticmethod
+    @lru_cache()
+    def has_deadend(cell_transition):
         """
         Checks if one entry can only by exited by a turn-around.
         """
-        if cell_transition & self.maskDeadEnds > 0:
+        if cell_transition & Grid4Transitions.maskDeadEnds() > 0:
             return True
         else:
             return False
@@ -235,5 +276,9 @@ class Grid4Transitions(Transitions):
         """
         Remove all turn-arounds (e.g. N-S, S-N, E-W,...).
         """
-        cell_transition &= cell_transition & (~self.maskDeadEnds) & 0xffff
-        return cell_transition
+        return fast_grid4_remove_deadends(cell_transition)
+
+    @staticmethod
+    @lru_cache()
+    def get_entry_directions(cell_transition) -> List[int]:
+        return [(cell_transition >> ((3 - orientation) * 4)) & 15 > 0 for orientation in range(4)]

flatland/core/grid/grid4_astar.py

-from flatland.core.grid.grid4_utils import validate_new_transition
+import numpy as np
 
+from flatland.core.grid.grid_utils import IntVector2D, IntVector2DDistance
+from flatland.core.grid.grid_utils import IntVector2DArray
+from flatland.core.grid.grid_utils import Vec2dOperations as Vec2d
+from flatland.core.transition_map import GridTransitionMap
+from flatland.utils.ordered_set import OrderedSet
 
-class AStarNode():
+
+class AStarNode:
     """A node class for A* Pathfinding"""
 
-    def __init__(self, parent=None, pos=None):
+    def __init__(self, pos: IntVector2D, parent=None):
         self.parent = parent
-        self.pos = pos
-        self.g = 0
-        self.h = 0
-        self.f = 0
+        self.pos: IntVector2D = pos
+        self.g = 0.0
+        self.h = 0.0
+        self.f = 0.0
 
     def __eq__(self, other):
+        """
+
+        Parameters
+        ----------
+        other : AStarNode
+        """
         return self.pos == other.pos
 
     def __hash__(self):
@@ -25,16 +37,35 @@ class AStarNode():
             self.f = other.f
 
 
-def a_star(rail_trans, rail_array, start, end):
+def a_star(grid_map: GridTransitionMap, start: IntVector2D, end: IntVector2D,
+           a_star_distance_function: IntVector2DDistance = Vec2d.get_manhattan_distance, avoid_rails=False,
+           respect_transition_validity=True, forbidden_cells: IntVector2DArray = None) -> IntVector2DArray:
+    """
+
+    :param avoid_rails:
+    :param grid_map: Grid Map where the path is found in
+    :param start: Start positions as (row,column)
+    :param end:  End position as (row,column)
+    :param a_star_distance_function: Define the distance function to use as heuristc:
+            -get_euclidean_distance
+            -get_manhattan_distance
+            -get_chebyshev_distance
+    :param respect_transition_validity: Whether or not a-star respect allowed transitions on the grid map.
+            - True: Respects the validity of transition. This generates valid paths, of no path if it cannot be found
+            - False: This always finds a path, but the path might be illegal and thus needs to be fixed afterwards
+    :param forbidden_cells: List of cells where the path cannot pass through. Used to avoid certain areas of Grid map
+    :return: IF a path is found a ordered list of al cells in path is returned
+    """
     """
     Returns a list of tuples as a path from the given start to end.
     If no path is found, returns path to closest point to end.
     """
-    rail_shape = rail_array.shape
-    start_node = AStarNode(None, start)
-    end_node = AStarNode(None, end)
-    open_nodes = set()
-    closed_nodes = set()
+    rail_shape = grid_map.grid.shape
+
+    start_node = AStarNode(start, None)
+    end_node = AStarNode(end, None)
+    open_nodes = OrderedSet()
+    closed_nodes = OrderedSet()
     open_nodes.add(start_node)
 
     while len(open_nodes) > 0:
@@ -58,6 +89,7 @@ def a_star(rail_trans, rail_array, start, end):
             while current is not None:
                 path.append(current.pos)
                 current = current.parent
+
             # return reversed path
             return path[::-1]
 
@@ -67,17 +99,28 @@ def a_star(rail_trans, rail_array, start, end):
             prev_pos = current_node.parent.pos
         else:
             prev_pos = None
+
         for new_pos in [(0, -1), (0, 1), (-1, 0), (1, 0)]:
-            node_pos = (current_node.pos[0] + new_pos[0], current_node.pos[1] + new_pos[1])
+            # update the "current" pos
+            node_pos: IntVector2D = Vec2d.add(current_node.pos, new_pos)
+
+            # is node_pos inside the grid?
             if node_pos[0] >= rail_shape[0] or node_pos[0] < 0 or node_pos[1] >= rail_shape[1] or node_pos[1] < 0:
                 continue
 
             # validate positions
-            if not validate_new_transition(rail_trans, rail_array, prev_pos, current_node.pos, node_pos, end_node.pos):
+            #
+            if not grid_map.validate_new_transition(prev_pos, current_node.pos, node_pos,
+                                                    end_node.pos) and respect_transition_validity:
                 continue
-
             # create new node
-            new_node = AStarNode(current_node, node_pos)
+            new_node = AStarNode(node_pos, current_node)
+
+            # Skip paths through forbidden regions if they are provided
+            if forbidden_cells is not None:
+                if node_pos in forbidden_cells and new_node != start_node and new_node != end_node:
+                    continue
+
             children.append(new_node)
 
         # loop through children
@@ -87,11 +130,12 @@ def a_star(rail_trans, rail_array, start, end):
                 continue
 
             # create the f, g, and h values
-            child.g = current_node.g + 1
-            # this heuristic favors diagonal paths:
-            # child.h = ((child.pos[0] - end_node.pos[0]) ** 2) + ((child.pos[1] - end_node.pos[1]) ** 2) \#  noqa: E800
+            child.g = current_node.g + 1.0
             # this heuristic avoids diagonal paths
-            child.h = abs(child.pos[0] - end_node.pos[0]) + abs(child.pos[1] - end_node.pos[1])
+            if avoid_rails:
+                child.h = a_star_distance_function(child.pos, end_node.pos) + np.clip(grid_map.grid[child.pos], 0, 1)
+            else:
+                child.h = a_star_distance_function(child.pos, end_node.pos)
             child.f = child.g + child.h
 
             # already in the open list?

flatland/core/grid/grid4_utils.py

+import numpy as np
+
 from flatland.core.grid.grid4 import Grid4TransitionsEnum
+from flatland.core.grid.grid_utils import IntVector2D
 
 
-def get_direction(pos1, pos2) -> Grid4TransitionsEnum:
+def get_direction(pos1: IntVector2D, pos2: IntVector2D) -> Grid4TransitionsEnum:
     """
     Assumes pos1 and pos2 are adjacent location on grid.
     Returns direction (int) that can be used with transitions.
@@ -9,66 +12,41 @@ def get_direction(pos1, pos2) -> Grid4TransitionsEnum:
     diff_0 = pos2[0] - pos1[0]
     diff_1 = pos2[1] - pos1[1]
     if diff_0 < 0:
-        return 0
+        return Grid4TransitionsEnum.NORTH
     if diff_0 > 0:
-        return 2
+        return Grid4TransitionsEnum.SOUTH
     if diff_1 > 0:
-        return 1
+        return Grid4TransitionsEnum.EAST
     if diff_1 < 0:
-        return 3
+        return Grid4TransitionsEnum.WEST
     raise Exception("Could not determine direction {}->{}".format(pos1, pos2))
 
 
 def mirror(dir):
     return (dir + 2) % 4
 
+MOVEMENT_ARRAY = [(-1, 0), (0, 1), (1, 0), (0, -1)]
+def get_new_position(position, movement):
+	return (position[0] + MOVEMENT_ARRAY[movement][0], position[1] + MOVEMENT_ARRAY[movement][1])
 
-def validate_new_transition(rail_trans, rail_array, prev_pos, current_pos, new_pos, end_pos):
-    # start by getting direction used to get to current node
-    # and direction from current node to possible child node
-    new_dir = get_direction(current_pos, new_pos)
-    if prev_pos is not None:
-        current_dir = get_direction(prev_pos, current_pos)
-    else:
-        current_dir = new_dir
-    # create new transition that would go to child
-    new_trans = rail_array[current_pos]
-    if prev_pos is None:
-        if new_trans == 0:
-            # need to flip direction because of how end points are defined
-            new_trans = rail_trans.set_transition(new_trans, mirror(current_dir), new_dir, 1)
+
+def direction_to_point(pos1: IntVector2D, pos2: IntVector2D) -> Grid4TransitionsEnum:
+    """
+    Returns the closest direction orientation of position 2 relative to position 1
+    :param pos1: position we are interested in
+    :param pos2: position we want to know it is facing
+    :return: direction NESW as int N:0 E:1 S:2 W:3
+    """
+    diff_vec = np.array((pos1[0] - pos2[0], pos1[1] - pos2[1]))
+    axis = np.argmax(np.power(diff_vec, 2))
+    direction = np.sign(diff_vec[axis])
+    if axis == 0:
+        if direction > 0:
+            return Grid4TransitionsEnum.NORTH
         else:
-            # check if matches existing layout
-            new_trans = rail_trans.set_transition(new_trans, current_dir, new_dir, 1)
+            return Grid4TransitionsEnum.SOUTH
     else:
-        # set the forward path
-        new_trans = rail_trans.set_transition(new_trans, current_dir, new_dir, 1)
-        # set the backwards path
-        new_trans = rail_trans.set_transition(new_trans, mirror(new_dir), mirror(current_dir), 1)
-    if new_pos == end_pos:
-        # need to validate end pos setup as well
-        new_trans_e = rail_array[end_pos]
-        if new_trans_e == 0:
-            # need to flip direction because of how end points are defined
-            new_trans_e = rail_trans.set_transition(new_trans_e, new_dir, mirror(new_dir), 1)
+        if direction > 0:
+            return Grid4TransitionsEnum.WEST
         else:
-            # check if matches existing layout
-            new_trans_e = rail_trans.set_transition(new_trans_e, new_dir, new_dir, 1)
-
-        if not rail_trans.is_valid(new_trans_e):
-            return False
-
-    # is transition is valid?
-    return rail_trans.is_valid(new_trans)
-
-
-def get_new_position(position, movement):
-    """ Utility function that converts a compass movement over a 2D grid to new positions (r, c). """
-    if movement == Grid4TransitionsEnum.NORTH:
-        return (position[0] - 1, position[1])
-    elif movement == Grid4TransitionsEnum.EAST:
-        return (position[0], position[1] + 1)
-    elif movement == Grid4TransitionsEnum.SOUTH:
-        return (position[0] + 1, position[1])
-    elif movement == Grid4TransitionsEnum.WEST:
-        return (position[0], position[1] - 1)
+            return Grid4TransitionsEnum.EAST

flatland/core/grid/grid8.py

@@ -20,9 +20,9 @@ class Grid8Transitions(Transitions):
     """
     Grid8Transitions class derived from Transitions.
 
-    Special case of `Transitions' over a 2D-grid (FlatLand).
+    Special case of `Transitions` over a 2D-grid (FlatLand).
     Transitions are possible to neighboring cells on the grid if allowed.
-    GridTransitions keeps track of valid transitions supplied as `transitions'
+    GridTransitions keeps track of valid transitions supplied as `transitions`
     list, each represented as a bitmap of 64 bits.
 
     0=North, 1=North-East, etc.
@@ -82,8 +82,8 @@ class Grid8Transitions(Transitions):
         -------
         int
             An updated bitmap that replaces the original transitions validity
-            of `cell_transition' with `new_transitions', for the appropriate
-            `orientation'.
+            of `cell_transition' with `new_transitions`, for the appropriate
+            `orientation`.
 
         """
         mask = (1 << ((8 - orientation) * 8)) - (1 << ((7 - orientation) * 8))
@@ -106,8 +106,8 @@ class Grid8Transitions(Transitions):
     def get_transition(self, cell_transition, orientation, direction):
         """
         Get the transition bit (1 value) that determines whether an agent
-        oriented in direction `orientation' and inside a cell with transitions
-        `cell_transition' can move to the cell in direction `direction'
+        oriented in direction `orientation` and inside a cell with transitions
+        `cell_transition' can move to the cell in direction `direction`
         relative to the current cell.
 
         Parameters
@@ -131,8 +131,8 @@ class Grid8Transitions(Transitions):
 
         """
         Set the transition bit (1 value) that determines whether an agent
-        oriented in direction `orientation' and inside a cell with transitions
-        `cell_transition' can move to the cell in direction `direction'
+        oriented in direction `orientation` and inside a cell with transitions
+        `cell_transition' can move to the cell in direction `direction`
         relative to the current cell.
 
         Parameters
@@ -150,8 +150,8 @@ class Grid8Transitions(Transitions):
         -------
         int
             An updated bitmap that replaces the original transitions validity
-            of `cell_transition' with `new_transitions', for the appropriate
-            `orientation'.
+            of `cell_transition' with `new_transitions`, for the appropriate
+            `orientation`.
 
         """
         if new_transition:
@@ -172,7 +172,7 @@ class Grid8Transitions(Transitions):
             64 bits used to encode the valid transitions for a cell.
         rotation : int
             Angle by which to clock-wise rotate the transition bits in
-            `cell_transition' by. I.e., rotation={0, 45, 90, 135, 180,
+            `cell_transition` by. I.e., rotation={0, 45, 90, 135, 180,
             225, 270, 315} degrees.
 
         Returns

flatland/core/grid/grid_utils.py

+from math import isnan
+from typing import Tuple, Callable, List, Type
+
 import numpy as np
 
+Vector2D: Type = Tuple[float, float]
+IntVector2D: Type = Tuple[int, int]
 
-def position_to_coordinate(depth, positions):
-    """Converts coordinates to positions:
-         [ (0,0) (0,1) ..  (0,w-1)
-           (1,0) (1,1)     (1,w-1)
-           ...
-           (d-1,0) (d-1,1)     (d-1,w-1)
-          ]
+IntVector2DArray: Type = List[IntVector2D]
+IntVector2DArrayArray: Type = List[List[IntVector2D]]
+
+Vector2DArray: Type = List[Vector2D]
+Vector2DArrayArray: Type = List[List[Vector2D]]
+
+IntVector2DDistance: Type = Callable[[IntVector2D, IntVector2D], float]
+
+
+class Vec2dOperations:
+
+    @staticmethod
+    def is_equal(node_a: Vector2D, node_b: Vector2D) -> bool:
+        """
+        vector operation : node_a + node_b
+
+        :param node_a: tuple with coordinate (x,y) or 2d vector
+        :param node_b: tuple with coordinate (x,y) or 2d vector
+
+        :return:
+            check if node_a and nobe_b are equal
+        """
+        return node_a[0] == node_b[0] and node_a[1] == node_b[1]
+
+    @staticmethod
+    def subtract(node_a: Vector2D, node_b: Vector2D) -> Vector2D:
+        """
+        vector operation : node_a - node_b
+
+        :param node_a: tuple with coordinate (x,y) or 2d vector
+        :param node_b: tuple with coordinate (x,y) or 2d vector
+
+        :return:
+            tuple with coordinate (x,y) or 2d vector
+        """
+        return node_a[0] - node_b[0], node_a[1] - node_b[1]
+
+    @staticmethod
+    def add(node_a: Vector2D, node_b: Vector2D) -> Vector2D:
+        """
+        vector operation : node_a + node_b
+
+        :param node_a: tuple with coordinate (x,y) or 2d vector
+        :param node_b: tuple with coordinate (x,y) or 2d vector
+
+        :return: tuple with coordinate (x,y) or 2d vector
+        """
+        return node_a[0] + node_b[0], node_a[1] + node_b[1]
+
+    @staticmethod
+    def make_orthogonal(node: Vector2D) -> Vector2D:
+        """
+        vector operation : rotates the 2D vector +90°
+
+        :param node: tuple with coordinate (x,y) or 2d vector
+
+        :return: tuple with coordinate (x,y) or 2d vector
+        """
+        return node[1], -node[0]
+
+    @staticmethod
+    def get_norm(node: Vector2D) -> float:
+        """
+        calculates the euclidean norm of the 2d vector
+        [see: https://lyfat.wordpress.com/2012/05/22/euclidean-vs-chebyshev-vs-manhattan-distance/]
+
+        :param node: tuple with coordinate (x,y) or 2d vector
+
+        :return:
+            tuple with coordinate (x,y) or 2d vector
+        """
+        return np.sqrt(node[0] * node[0] + node[1] * node[1])
+
+    @staticmethod
+    def get_euclidean_distance(node_a: Vector2D, node_b: Vector2D) -> float:
+        """
+        calculates the euclidean norm of the 2d vector
+
+        Parameters
+        ----------
+        node_a
+            tuple with coordinate (x,y) or 2d vector
+        node_b
+            tuple with coordinate (x,y) or 2d vector
+
+        Returns
+        -------
+        float
+            Euclidean distance
+        """
+        return Vec2dOperations.get_norm(Vec2dOperations.subtract(node_b, node_a))
+
+    @staticmethod
+    def get_manhattan_distance(node_a: Vector2D, node_b: Vector2D) -> float:
+        """
+        calculates the manhattan distance of the 2d vector
+        [see: https://lyfat.wordpress.com/2012/05/22/euclidean-vs-chebyshev-vs-manhattan-distance/]
+
+        Parameters
+        ----------
+        node_a
+            tuple with coordinate (x,y) or 2d vector
+        node_b
+            tuple with coordinate (x,y) or 2d vector
+
+        Returns
+        -------
+        float
+            Mahnhattan distance
+        """
+        delta = (Vec2dOperations.subtract(node_b, node_a))
+        return np.abs(delta[0]) + np.abs(delta[1])
+
+    @staticmethod
+    def get_chebyshev_distance(node_a: Vector2D, node_b: Vector2D) -> float:
+        """
+        calculates the chebyshev norm of the 2d vector
+        [see: https://lyfat.wordpress.com/2012/05/22/euclidean-vs-chebyshev-vs-manhattan-distance/]
+
+        Parameters
+        ----------
+        node_a
+            tuple with coordinate (x,y) or 2d vector
+        node_b
+            tuple with coordinate (x,y) or 2d vector
+
+        Returns
+        -------
+        float
+            the chebyshev distance
+        """
+        delta = (Vec2dOperations.subtract(node_b, node_a))
+        return max(np.abs(delta[0]), np.abs(delta[1]))
+
+    @staticmethod
+    def normalize(node: Vector2D) -> Tuple[float, float]:
+        """
+        normalize the 2d vector = `v/|v|`
+
+        :param node: tuple with coordinate (x,y) or 2d vector
+
+        :return: tuple with coordinate (x,y) or 2d vector
+        """
+        n = Vec2dOperations.get_norm(node)
+        if n > 0.0:
+            n = 1 / n
+        return Vec2dOperations.scale(node, n)
+
+    @staticmethod
+    def scale(node: Vector2D, scale: float) -> Vector2D:
+        """
+         scales the 2d vector = node * scale
+
+         :param node: tuple with coordinate (x,y) or 2d vector
+         :param scale: scalar to scale
+
+         :return: tuple with coordinate (x,y) or 2d vector
+         """
+        return node[0] * scale, node[1] * scale
+
+    @staticmethod
+    def round(node: Vector2D) -> IntVector2D:
+        """
+         rounds the x and y coordinate and convert them to an integer values
+
+         :param node: tuple with coordinate (x,y) or 2d vector
+
+         :return: tuple with coordinate (x,y) or 2d vector
+         """
+        return int(np.round(node[0])), int(np.round(node[1]))
+
+    @staticmethod
+    def ceil(node: Vector2D) -> IntVector2D:
+        """
+         ceiling the x and y coordinate and convert them to an integer values
+
+         :param node: tuple with coordinate (x,y) or 2d vector
+
+         :return:
+            tuple with coordinate (x,y) or 2d vector
+         """
+        return int(np.ceil(node[0])), int(np.ceil(node[1]))
+
+    @staticmethod
+    def floor(node: Vector2D) -> IntVector2D:
+        """
+         floor the x and y coordinate and convert them to an integer values
+
+         :param node: tuple with coordinate (x,y) or 2d vector
+
+         :return:
+            tuple with coordinate (x,y) or 2d vector
+         """
+        return int(np.floor(node[0])), int(np.floor(node[1]))
+
+    @staticmethod
+    def bound(node: Vector2D, min_value: float, max_value: float) -> Vector2D:
+        """
+         force the values x and y to be between min_value and max_value
+
+         :param node: tuple with coordinate (x,y) or 2d vector
+         :param min_value: scalar value
+         :param max_value: scalar value
+
+         :return:
+            tuple with coordinate (x,y) or 2d vector
+         """
+        return max(min_value, min(max_value, node[0])), max(min_value, min(max_value, node[1]))
+
+    @staticmethod
+    def rotate(node: Vector2D, rot_in_degree: float) -> Vector2D:
+        """
+         rotate the 2d vector with given angle in degree
+
+         :param node: tuple with coordinate (x,y) or 2d vector
+         :param rot_in_degree:  angle in degree
+
+         :return:
+            tuple with coordinate (x,y) or 2d vector
+         """
+        alpha = rot_in_degree / 180.0 * np.pi
+        x0 = node[0]
+        y0 = node[1]
+        x1 = x0 * np.cos(alpha) - y0 * np.sin(alpha)
+        y1 = x0 * np.sin(alpha) + y0 * np.cos(alpha)
+        return x1, y1
+
+
+def position_to_coordinate(depth: int, positions: List[int]):
+    """Converts coordinates to positions::
+
+        [ (0,0) (0,1) ..  (0,w-1)
+          (1,0) (1,1)     (1,w-1)
+            ...
+          (d-1,0) (d-1,1)     (d-1,w-1)
+        ]
 
          -->
 
-         [ 0      d    ..  (w-1)*d
-           1      d+1
-           ...
-           d-1    2d-1     w*d-1
-         ]
+        [ 0      d    ..  (w-1)*d
+          1      d+1
+          ...
+          d-1    2d-1     w*d-1
+        ]
 
-    :param depth:
-    :param positions:
-    :return:
+    Parameters
+    ----------
+    depth : int
+    positions : List[Tuple[int,int]]
     """
     coords = ()
     for p in positions:
@@ -29,7 +264,8 @@ def position_to_coordinate(depth, positions):
 
 def coordinate_to_position(depth, coords):
     """
-    Converts positions to coordinates:
+    Converts positions to coordinates::
+
          [ 0      d    ..  (w-1)*d
            1      d+1
            ...
@@ -46,13 +282,17 @@ def coordinate_to_position(depth, coords):
     :param coords:
     :return:
     """
-    position = np.empty(len(coords), dtype=int)
-    idx = 0
-    for t in coords:
-        position[idx] = int(t[1] * depth + t[0])
-        idx += 1
+    position = list(range(len(coords)))
+    for index, t in enumerate(coords):
+        if isnan(t[0]):
+            position[index] = -1
+        else:
+            position[index] = int(t[1] * depth + t[0])
     return position
 
 
-def distance_on_rail(pos1, pos2):
-    return abs(pos1[0] - pos2[0]) + abs(pos1[1] - pos2[1])
+def distance_on_rail(pos1, pos2, metric="Euclidean"):
+    if metric == "Euclidean":
+        return np.sqrt(np.power(pos1[0] - pos2[0], 2) + np.power(pos1[1] - pos2[1], 2))
+    if metric == "Manhattan":
+        return np.abs(pos1[0] - pos2[0]) + np.abs(pos1[1] - pos2[1])

flatland/core/grid/rail_env_grid.py

 from flatland.core.grid.grid4 import Grid4Transitions
+from flatland.utils.ordered_set import OrderedSet
 
 
 class RailEnvTransitions(Grid4Transitions):
     """
-    Special case of `GridTransitions' over a 2D-grid, with a pre-defined set
+    Special case of `GridTransitions` over a 2D-grid, with a pre-defined set
     of transitions mimicking the types of real Swiss rail connections.
 
-    --------------------------------------------------------------------------
-
     As no diagonal transitions are allowed in the RailEnv environment, the
     possible transitions for RailEnv from a cell to its neighboring ones
     are represented over 16 bits.
@@ -44,7 +43,7 @@ class RailEnvTransitions(Grid4Transitions):
         )
 
         # create this to make validation faster
-        self.transitions_all = set()
+        self.transitions_all = OrderedSet()
         for index, trans in enumerate(self.transitions):
             self.transitions_all.add(trans)
             if index in (2, 4, 6, 7, 8, 9, 10):

flatland/core/transition_map.py

@@ -7,9 +7,15 @@ from importlib_resources import path
 from numpy import array
 
 from flatland.core.grid.grid4 import Grid4Transitions
+from flatland.core.grid.grid4_utils import get_new_position, get_direction
+from flatland.core.grid.grid_utils import IntVector2DArray, IntVector2D
+from flatland.core.grid.grid_utils import Vec2dOperations as Vec2d
+from flatland.core.grid.rail_env_grid import RailEnvTransitions
 from flatland.core.transitions import Transitions
+from flatland.utils.ordered_set import OrderedSet
 
 
+# TODO are these general classes or for grid4 only?
 class TransitionMap:
     """
     Base TransitionMap class.
@@ -21,7 +27,7 @@ class TransitionMap:
     def get_transitions(self, cell_id):
         """
         Return a tuple of transitions available in a cell specified by
-        `cell_id' (e.g., a tuple of size of the maximum number of transitions,
+        `cell_id` (e.g., a tuple of size of the maximum number of transitions,
         with values 0 or 1, or potentially in between,
         for stochastic transitions).
 
@@ -41,8 +47,8 @@ class TransitionMap:
 
     def set_transitions(self, cell_id, new_transitions):
         """
-        Replaces the available transitions in cell `cell_id' with the tuple
-        `new_transitions'. `new_transitions' must have
+        Replaces the available transitions in cell `cell_id` with the tuple
+        `new_transitions'. `new_transitions` must have
         one element for each possible transition.
 
         Parameters
@@ -58,8 +64,8 @@ class TransitionMap:
 
     def get_transition(self, cell_id, transition_index):
         """
-        Return the status of whether an agent in cell `cell_id' can perform a
-        movement along transition `transition_index (e.g., the NESW direction
+        Return the status of whether an agent in cell `cell_id` can perform a
+        movement along transition `transition_index` (e.g., the NESW direction
         of movement, for agents on a grid).
 
         Parameters
@@ -83,8 +89,8 @@ class TransitionMap:
 
     def set_transition(self, cell_id, transition_index, new_transition):
         """
-        Replaces the validity of transition to `transition_index' in cell
-        `cell_id' with the new `new_transition'.
+        Replaces the validity of transition to `transition_index` in cell
+        `cell_id' with the new `new_transition`.
 
 
         Parameters
@@ -111,7 +117,7 @@ class GridTransitionMap(TransitionMap):
     GridTransitionMap implements utility functions.
     """
 
-    def __init__(self, width, height, transitions: Transitions = Grid4Transitions([])):
+    def __init__(self, width, height, transitions: Transitions = Grid4Transitions([]), random_seed=None):
         """
         Builder for GridTransitionMap object.
 
@@ -130,7 +136,11 @@ class GridTransitionMap(TransitionMap):
         self.width = width
         self.height = height
         self.transitions = transitions
-
+        self.random_generator = np.random.RandomState()
+        if random_seed is None:
+            self.random_generator.seed(12)
+        else:
+            self.random_generator.seed(random_seed)
         self.grid = np.zeros((height, width), dtype=self.transitions.get_type())
 
     def get_full_transitions(self, row, column):
@@ -154,7 +164,7 @@ class GridTransitionMap(TransitionMap):
     def get_transitions(self, row, column, orientation):
         """
         Return a tuple of transitions available in a cell specified by
-        `cell_id' (e.g., a tuple of size of the maximum number of transitions,
+        `cell_id` (e.g., a tuple of size of the maximum number of transitions,
         with values 0 or 1, or potentially in between,
         for stochastic transitions).
 
@@ -176,8 +186,8 @@ class GridTransitionMap(TransitionMap):
 
     def set_transitions(self, cell_id, new_transitions):
         """
-        Replaces the available transitions in cell `cell_id' with the tuple
-        `new_transitions'. `new_transitions' must have
+        Replaces the available transitions in cell `cell_id` with the tuple
+        `new_transitions'. `new_transitions` must have
         one element for each possible transition.
 
         Parameters
@@ -202,8 +212,8 @@ class GridTransitionMap(TransitionMap):
 
     def get_transition(self, cell_id, transition_index):
         """
-        Return the status of whether an agent in cell `cell_id' can perform a
-        movement along transition `transition_index (e.g., the NESW direction
+        Return the status of whether an agent in cell `cell_id` can perform a
+        movement along transition `transition_index` (e.g., the NESW direction
         of movement, for agents on a grid).
 
         Parameters
@@ -230,8 +240,8 @@ class GridTransitionMap(TransitionMap):
 
     def set_transition(self, cell_id, transition_index, new_transition, remove_deadends=False):
         """
-        Replaces the validity of transition to `transition_index' in cell
-        `cell_id' with the new `new_transition'.
+        Replaces the validity of transition to `transition_index` in cell
+        `cell_id' with the new `new_transition`.
 
 
         Parameters
@@ -259,7 +269,7 @@ class GridTransitionMap(TransitionMap):
 
     def save_transition_map(self, filename):
         """
-        Save the transitions grid as `filename', in npy format.
+        Save the transitions grid as `filename`, in npy format.
 
         Parameters
         ----------
@@ -271,9 +281,9 @@ class GridTransitionMap(TransitionMap):
 
     def load_transition_map(self, package, resource):
         """
-        Load the transitions grid from `filename' (npy format).
+        Load the transitions grid from `filename` (npy format).
         The load function only updates the transitions grid, and possibly width and height, but the object has to be
-        initialized with the correct `transitions' object anyway.
+        initialized with the correct `transitions` object anyway.
 
         Parameters
         ----------
@@ -283,7 +293,7 @@ class GridTransitionMap(TransitionMap):
             Name of the file from which to load the transitions grid within the package.
         override_gridsize : bool
             If override_gridsize=True, the width and height of the GridTransitionMap object are replaced with the size
-            of the map loaded from `filename'. If override_gridsize=False, the transitions grid is either cropped (if
+            of the map loaded from `filename`. If override_gridsize=False, the transitions grid is either cropped (if
             the grid size is larger than (height,width) ) or padded with zeros (if the grid size is smaller than
             (height,width) )
 
@@ -298,26 +308,82 @@ class GridTransitionMap(TransitionMap):
         self.height = new_height
         self.grid = new_grid
 
+    def is_dead_end(self, rcPos: IntVector2DArray):
+        """
+        Check if the cell is a dead-end.
 
-    def is_dead_end(self,rcPos):
+        Parameters
+        ----------
+        rcPos: Tuple[int,int]
+            tuple(row, column) with grid coordinate
+        Returns
+        -------
+        boolean
+            True if and only if the cell is a dead-end.
         """
-        Check if the cell is a dead-end
-        :param rcPos: tuple(row, column) with grid coordinate
-        :return: False : if not a dead-end else True
+        cell_transition = self.get_full_transitions(rcPos[0], rcPos[1])
+        return Grid4Transitions.has_deadend(cell_transition)
+
+    def is_simple_turn(self, rcPos: IntVector2DArray):
+        """
+        Check if the cell is a left/right simple turn
+
+        Parameters
+        ----------
+            rcPos: Tuple[int,int]
+                tuple(row, column) with grid coordinate
+        Returns
+        -------
+            boolean
+                True if and only if the cell is a left/right simple turn.
         """
-        nbits = 0
         tmp = self.get_full_transitions(rcPos[0], rcPos[1])
-        while tmp > 0:
-            nbits += (tmp & 1)
-            tmp = tmp >> 1
-        return nbits==1
 
-    def cell_neighbours_valid(self, rcPos, check_this_cell=False):
+        def is_simple_turn(trans):
+            all_simple_turns = OrderedSet()
+            for trans in [int('0100000000000010', 2),  # Case 1b (8)  - simple turn right
+                          int('0001001000000000', 2)  # Case 1c (9)  - simple turn left]:
+                          ]:
+                for _ in range(3):
+                    trans = self.transitions.rotate_transition(trans, rotation=90)
+                    all_simple_turns.add(trans)
+            return trans in all_simple_turns
+
+        return is_simple_turn(tmp)
+
+    def check_path_exists(self, start: IntVector2DArray, direction: int, end: IntVector2DArray):
+        """
+        Breath first search for a possible path from one node with a certain orientation to a target node.
+        :param start: Start cell rom where we want to check the path
+        :param direction: Start direction for the path we are testing
+        :param end: Cell that we try to reach from the start cell
+        :return: True if a path exists, False otherwise
+        """
+        visited = OrderedSet()
+        stack = [(start, direction)]
+        while stack:
+            node = stack.pop()
+            node_position = node[0]
+            node_direction = node[1]
+
+            if Vec2d.is_equal(node_position, end):
+                return True
+            if node not in visited:
+                visited.add(node)
+
+                moves = self.get_transitions(node_position[0], node_position[1], node_direction)
+                for move_index in range(4):
+                    if moves[move_index]:
+                        stack.append((get_new_position(node_position, move_index),
+                                      move_index))
+
+        return False
+
+    def cell_neighbours_valid(self, rcPos: IntVector2DArray, check_this_cell=False):
         """
         Check validity of cell at rcPos = tuple(row, column)
         Checks that:
-        - surrounding cells have inbound transitions for all the
-            outbound transitions of this cell.
+        - surrounding cells have inbound transitions for all the outbound transitions of this cell.
 
         These are NOT checked - see transition.is_valid:
         - all transitions have the mirror transitions (N->E <=> W->S)
@@ -361,15 +427,36 @@ class GridTransitionMap(TransitionMap):
                 continue
             else:
                 return False
+        # If the cell is empty but has incoming connections we return false
+        if binTrans < 1:
+            connected = 0
+
+            for iDirOut in np.arange(4):
+                gdRC = gDir2dRC[iDirOut]  # row,col increment
+                gPos2 = grcPos + gdRC  # next cell in that direction
+
+                # Check the adjacent cell is within bounds
+                # if not, then ignore it for the count of incoming connections
+                if np.any(gPos2 < 0):
+                    continue
+                if np.any(gPos2 >= grcMax):
+                    continue
+
+                # Get the transitions out of gPos2, using iDirOut as the inbound direction
+                # if there are no available transitions, ie (0,0,0,0), then rcPos is invalid
+
+                for orientation in range(4):
+                    connected += self.get_transition((gPos2[0], gPos2[1], orientation), mirror(iDirOut))
+            if connected > 0:
+                return False
 
         return True
 
-    def fix_neighbours(self, rcPos, check_this_cell=False):
+    def fix_neighbours(self, rcPos: IntVector2DArray, check_this_cell=False):
         """
         Check validity of cell at rcPos = tuple(row, column)
         Checks that:
-        - surrounding cells have inbound transitions for all the
-            outbound transitions of this cell.
+        - surrounding cells have inbound transitions for all the outbound transitions of this cell.
 
         These are NOT checked - see transition.is_valid:
         - all transitions have the mirror transitions (N->E <=> W->S)
@@ -417,16 +504,22 @@ class GridTransitionMap(TransitionMap):
 
         return True
 
-    def fix_transitions(self, rcPos):
+    def fix_transitions(self, rcPos: IntVector2DArray, direction: IntVector2D = -1):
         """
         Fixes broken transitions
         """
         gDir2dRC = self.transitions.gDir2dRC  # [[-1,0] = N, [0,1]=E, etc]
         grcPos = array(rcPos)
         grcMax = self.grid.shape
-
+        # Transition elements
+        transitions = RailEnvTransitions()
+        cells = transitions.transition_list
+        simple_switch_east_south = transitions.rotate_transition(cells[10], 90)
+        simple_switch_west_south = transitions.rotate_transition(cells[2], 270)
+        symmetrical = cells[6]
+        double_slip = cells[5]
+        three_way_transitions = [simple_switch_east_south, simple_switch_west_south]
         # loop over available outbound directions (indices) for rcPos
-        self.set_transitions(rcPos, 0)
 
         incoming_connections = np.zeros(4)
         for iDirOut in np.arange(4):
@@ -449,38 +542,97 @@ class GridTransitionMap(TransitionMap):
                 incoming_connections[iDirOut] = 1
 
         number_of_incoming = np.sum(incoming_connections)
-        # Only one incoming direction --> Straight line
+        # Only one incoming direction --> Straight line set deadend
         if number_of_incoming == 1:
-            for direction in range(4):
-                if incoming_connections[direction] > 0:
-                    self.set_transition((rcPos[0], rcPos[1], mirror(direction)), direction, 1)
+            if self.get_full_transitions(*rcPos) == 0:
+                self.set_transitions(rcPos, 0)
+            else:
+                self.set_transitions(rcPos, 0)
+
+                for direction in range(4):
+                    if incoming_connections[direction] > 0:
+                        self.set_transition((rcPos[0], rcPos[1], mirror(direction)), direction, 1)
         # Connect all incoming connections
         if number_of_incoming == 2:
+            self.set_transitions(rcPos, 0)
+
             connect_directions = np.argwhere(incoming_connections > 0)
             self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[0])), connect_directions[1], 1)
             self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[1])), connect_directions[0], 1)
 
-        # Find feasible connection fro three entries
+        # Find feasible connection for three entries
         if number_of_incoming == 3:
+            self.set_transitions(rcPos, 0)
             hole = np.argwhere(incoming_connections < 1)[0][0]
-            connect_directions = [(hole + 1) % 4, (hole + 2) % 4, (hole + 3) % 4]
-            self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[0])), connect_directions[1], 1)
-            self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[0])), connect_directions[2], 1)
-            self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[1])), connect_directions[0], 1)
-            self.set_transition((rcPos[0], rcPos[1], mirror(connect_directions[2])), connect_directions[0], 1)
-        # Make a cross
+            if direction >= 0:
+                switch_type_idx = (direction - hole + 3) % 4
+                if switch_type_idx == 0:
+                    transition = simple_switch_west_south
+                elif switch_type_idx == 2:
+                    transition = simple_switch_east_south
+                else:
+                    transition = self.random_generator.choice(three_way_transitions, 1)[0]
+            else:
+                transition = self.random_generator.choice(three_way_transitions, 1)[0]
+            transition = transitions.rotate_transition(transition, int(hole * 90))
+            self.set_transitions((rcPos[0], rcPos[1]), transition)
+
+        # Make a double slip switch
         if number_of_incoming == 4:
-            connect_directions = np.arange(4)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[0]), connect_directions[0], 1)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[0]), connect_directions[1], 1)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[1]), connect_directions[0], 1)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[1]), connect_directions[1], 1)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[2]), connect_directions[2], 1)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[2]), connect_directions[3], 1)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[3]), connect_directions[2], 1)
-            self.set_transition((rcPos[0], rcPos[1], connect_directions[3]), connect_directions[3], 1)
+            rotation = self.random_generator.randint(2)
+            transition = transitions.rotate_transition(double_slip, int(rotation * 90))
+            self.set_transitions((rcPos[0], rcPos[1]), transition)
         return True
 
+    def validate_new_transition(self, prev_pos: IntVector2D, current_pos: IntVector2D,
+                                new_pos: IntVector2D, end_pos: IntVector2D):
+        """
+        Utility function to test that a path drawn by a-start algorithm uses valid transition objects.
+        We us this to quide a-star as there are many transition elements that are not allowed in RailEnv
+
+        :param prev_pos: The previous position we were checking
+        :param current_pos: The current position we are checking
+        :param new_pos: Possible child position we move into
+        :param end_pos: End cell of path we are drawing
+        :return: True if the transition is valid, False if transition element is illegal
+        """
+        # start by getting direction used to get to current node
+        # and direction from current node to possible child node
+        new_dir = get_direction(current_pos, new_pos)
+        if prev_pos is not None:
+            current_dir = get_direction(prev_pos, current_pos)
+        else:
+            current_dir = new_dir
+        # create new transition that would go to child
+        new_trans = self.grid[current_pos]
+        if prev_pos is None:
+            if new_trans == 0:
+                # need to flip direction because of how end points are defined
+                new_trans = self.transitions.set_transition(new_trans, mirror(current_dir), new_dir, 1)
+            else:
+                # check if matches existing layout
+                new_trans = self.transitions.set_transition(new_trans, current_dir, new_dir, 1)
+        else:
+            # set the forward path
+            new_trans = self.transitions.set_transition(new_trans, current_dir, new_dir, 1)
+            # set the backwards path
+            new_trans = self.transitions.set_transition(new_trans, mirror(new_dir), mirror(current_dir), 1)
+        if Vec2d.is_equal(new_pos, end_pos):
+            # need to validate end pos setup as well
+            new_trans_e = self.grid[end_pos]
+            if new_trans_e == 0:
+                # need to flip direction because of how end points are defined
+                new_trans_e = self.transitions.set_transition(new_trans_e, new_dir, mirror(new_dir), 1)
+            else:
+                # check if matches existing layout
+                new_trans_e = self.transitions.set_transition(new_trans_e, new_dir, new_dir, 1)
+
+            if not self.transitions.is_valid(new_trans_e):
+                return False
+
+        # is transition is valid?
+        return self.transitions.is_valid(new_trans)
+
 
 def mirror(dir):
     return (dir + 2) % 4

flatland/core/transitions.py

@@ -12,7 +12,7 @@ class Transitions:
 
     Generic class that implements checks to control whether a
     certain transition is allowed (agent facing a direction
-    `orientation' and moving into direction `orientation')
+    `orientation' and moving into direction `orientation`)
     """
 
     def get_type(self):
@@ -21,7 +21,7 @@ class Transitions:
     def get_transitions(self, cell_transition, orientation):
         """
         Return a tuple of transitions available in a cell specified by
-        `cell_transition' for an agent facing direction `orientation'
+        `cell_transition' for an agent facing direction `orientation`
         (e.g., a tuple of size of the maximum number of transitions,
         with values 0 or 1, or potentially in between,
         for stochastic transitions).
@@ -45,9 +45,9 @@ class Transitions:
 
     def set_transitions(self, cell_transition, orientation, new_transitions):
         """
-        Return a `cell_transition' specification where the transitions
-        available for an agent facing direction `orientation' are replaced
-        with the tuple `new_transitions'. `new_orientations' must have
+        Return a `cell_transition` specification where the transitions
+        available for an agent facing direction `orientation` are replaced
+        with the tuple `new_transitions'. `new_orientations` must have
         one element for each possible transition.
 
         Parameters
@@ -65,8 +65,8 @@ class Transitions:
         -------
         [cell-content]
             An updated class-specific object that replaces the original
-            transitions validity of `cell_transition' with `new_transitions',
-            for the appropriate `orientation'.
+            transitions validity of `cell_transition' with `new_transitions`,
+            for the appropriate `orientation`.
 
         """
         raise NotImplementedError()
@@ -74,8 +74,8 @@ class Transitions:
     def get_transition(self, cell_transition, orientation, direction):
         """
         Return the status of whether an agent oriented in directions
-        `orientation' and inside a cell with transitions `cell_transition'
-        can move to the cell in direction `direction' relative
+        `orientation' and inside a cell with transitions `cell_transition`
+        can move to the cell in direction `direction` relative
         to the current cell.
 
         Parameters
@@ -101,11 +101,11 @@ class Transitions:
     def set_transition(self, cell_transition, orientation, direction,
                        new_transition):
         """
-        Return a `cell_transition' specification where the status of
-        whether an agent oriented in direction `orientation' and inside
-        a cell with transitions `cell_transition' can move to the cell
-        in direction `direction' relative to the current cell is set
-        to `new_transition'.
+        Return a `cell_transition` specification where the status of
+        whether an agent oriented in direction `orientation` and inside
+        a cell with transitions `cell_transition` can move to the cell
+        in direction `direction` relative to the current cell is set
+        to `new_transition`.
 
         Parameters
         ----------
@@ -125,8 +125,8 @@ class Transitions:
         -------
         [cell-content]
             An updated class-specific object that replaces the original
-            transitions validity of `cell_transition' with `new_transitions',
-            for the appropriate `orientation' to `direction'.
+            transitions validity of `cell_transition' with `new_transitions`,
+            for the appropriate `orientation' to `direction`.
 
         """
         raise NotImplementedError()

flatland/envs/agent_chains.py

+
+import networkx as nx
+import numpy as np
+
+from typing import List, Tuple
+import graphviz as gv
+
+class MotionCheck(object):
+    """ Class to find chains of agents which are "colliding" with a stopped agent.
+        This is to allow close-packed chains of agents, ie a train of agents travelling
+        at the same speed with no gaps between them,
+    """
+    def __init__(self):
+        self.G = nx.DiGraph()
+        self.nDeadlocks = 0
+        self.svDeadlocked = set()
+    
+
+    def addAgent(self, iAg, rc1, rc2, xlabel=None):
+        """ add an agent and its motion as row,col tuples of current and next position.
+            The agent's current position is given an "agent" attribute recording the agent index.
+            If an agent does not want to move this round (rc1 == rc2) then a self-loop edge is created.
+            xlabel is used for test cases to give a label (see graphviz)
+        """
+
+        # Agents which have not yet entered the env have position None.
+        # Substitute this for the row = -1, column = agent index
+        if rc1 is None:
+            rc1 = (-1, iAg)
+
+        if rc2 is None:
+            rc2 = (-1, iAg)
+
+        self.G.add_node(rc1, agent=iAg)
+        if xlabel:
+            self.G.nodes[rc1]["xlabel"] = xlabel
+        self.G.add_edge(rc1, rc2)
+
+    def find_stops(self):
+        """ find all the stopped agents as a set of rc position nodes
+            A stopped agent is a self-loop on a cell node.
+        """
+
+        # get the (sparse) adjacency matrix
+        spAdj = nx.linalg.adjacency_matrix(self.G)
+
+        # the stopped agents appear as 1s on the diagonal
+        # the where turns this into a list of indices of the 1s
+        giStops = np.where(spAdj.diagonal())[0]
+
+        # convert the cell/node indices into the node rc values
+        lvAll = list(self.G.nodes())
+        # pick out the stops by their indices
+        lvStops = [ lvAll[i] for i in giStops ]
+        # make it into a set ready for a set intersection
+        svStops = set(lvStops)
+        return svStops
+
+    def find_stops2(self):
+        """ alternative method to find stopped agents, using a networkx call to find selfloop edges
+        """
+        svStops = { u for u,v in nx.classes.function.selfloop_edges(self.G) }
+        return svStops
+
+    def find_stop_preds(self, svStops=None):
+        """ Find the predecessors to a list of stopped agents (ie the nodes / vertices)
+            Returns the set of predecessors.
+            Includes "chained" predecessors.
+        """
+
+        if svStops is None:
+            svStops = self.find_stops2()
+
+        # Get all the chains of agents - weakly connected components.
+        # Weakly connected because it's a directed graph and you can traverse a chain of agents
+        # in only one direction
+        lWCC = list(nx.algorithms.components.weakly_connected_components(self.G))
+
+        svBlocked = set()
+
+        for oWCC in lWCC:
+            #print("Component:", oWCC)
+            # Get the node details for this WCC in a subgraph
+            Gwcc = self.G.subgraph(oWCC)
+            
+            # Find all the stops in this chain or tree
+            svCompStops = svStops.intersection(Gwcc)
+            #print(svCompStops)
+
+            if len(svCompStops) > 0:
+
+                # We need to traverse it in reverse - back up the movement edges
+                Gwcc_rev = Gwcc.reverse()
+                for vStop in svCompStops:
+
+                    # Find all the agents stopped by vStop by following the (reversed) edges
+                    # This traverses a tree - dfs = depth first seearch
+                    iter_stops = nx.algorithms.traversal.dfs_postorder_nodes(Gwcc_rev, vStop)
+                    lStops = list(iter_stops)
+                    svBlocked.update(lStops)
+
+        # the set of all the nodes/agents blocked by this set of stopped nodes
+        return svBlocked
+
+    def find_swaps(self):
+        """ find all the swap conflicts where two agents are trying to exchange places.
+            These appear as simple cycles of length 2.
+            These agents are necessarily deadlocked (since they can't change direction in flatland) -
+            meaning they will now be stuck for the rest of the episode.
+        """
+        #svStops = self.find_stops2()
+        llvLoops = list(nx.algorithms.cycles.simple_cycles(self.G))
+        llvSwaps = [lvLoop for lvLoop in llvLoops if len(lvLoop) == 2 ]
+        svSwaps = { v for lvSwap in llvSwaps for v in lvSwap }
+        return svSwaps
+
+    def find_same_dest(self):
+        """ find groups of agents which are trying to land on the same cell.
+            ie there is a gap of one cell between them and they are both landing on it.
+        """
+        pass
+
+    def block_preds(self, svStops, color="red"):
+        """ Take a list of stopped agents, and apply a stop color to any chains/trees
+            of agents trying to head toward those cells.
+            Count the number of agents blocked, ignoring those which are already marked.
+            (Otherwise it can double count swaps)
+
+        """
+        iCount = 0
+        svBlocked = set()
+        # The reversed graph allows us to follow directed edges to find affected agents.
+        Grev = self.G.reverse()
+        for v in svStops:
+            
+            # Use depth-first-search to find a tree of agents heading toward the blocked cell.
+            lvPred = list(nx.traversal.dfs_postorder_nodes(Grev, source=v))
+            svBlocked |= set(lvPred)
+            svBlocked.add(v)
+            #print("node:", v, "set", svBlocked)
+            # only count those not already marked
+            for v2 in [v]+lvPred:
+                if self.G.nodes[v2].get("color") != color:
+                    self.G.nodes[v2]["color"] = color
+                    iCount += 1
+
+        return svBlocked
+
+
+    def find_conflicts(self):
+        svStops = self.find_stops2()  # voluntarily stopped agents - have self-loops
+        svSwaps = self.find_swaps()   # deadlocks - adjacent head-on collisions
+
+        # Block all swaps and their tree of predessors
+        self.svDeadlocked = self.block_preds(svSwaps, color="purple")
+
+        # Take the union of the above, and find all the predecessors
+        #svBlocked = self.find_stop_preds(svStops.union(svSwaps))
+
+        # Just look for the the tree of preds for each voluntarily stopped agent
+        svBlocked = self.find_stop_preds(svStops)
+
+        # iterate the nodes v with their predecessors dPred (dict of nodes->{})
+        for (v, dPred) in self.G.pred.items():
+            # mark any swaps with purple - these are directly deadlocked
+            #if v in svSwaps:
+            #    self.G.nodes[v]["color"] = "purple"
+            # If they are not directly deadlocked, but are in the union of stopped + deadlocked
+            #elif v in svBlocked:
+
+            # if in blocked, it will not also be in a swap pred tree, so no need to worry about overwriting
+            if v in svBlocked:
+                self.G.nodes[v]["color"] = "red"
+            # not blocked but has two or more predecessors, ie >=2 agents waiting to enter this node
+            elif len(dPred)>1:
+
+                # if this agent is already red/blocked, ignore. CHECK: why?
+                # certainly we want to ignore purple so we don't overwrite with red.
+                if self.G.nodes[v].get("color") in ("red", "purple"):
+                    continue
+
+                # if this node has no agent, and >=2 want to enter it.
+                if self.G.nodes[v].get("agent") is None:
+                    self.G.nodes[v]["color"] = "blue"
+                # this node has an agent and >=2 want to enter
+                else:
+                    self.G.nodes[v]["color"] = "magenta"
+
+                # predecessors of a contended cell: {agent index -> node}
+                diAgCell = {self.G.nodes[vPred].get("agent"): vPred  for vPred in dPred}
+
+                # remove the agent with the lowest index, who wins
+                iAgWinner = min(diAgCell)
+                diAgCell.pop(iAgWinner)
+
+                # Block all the remaining predessors, and their tree of preds
+                #for iAg, v in diAgCell.items():
+                #    self.G.nodes[v]["color"] = "red"
+                #    for vPred in nx.traversal.dfs_postorder_nodes(self.G.reverse(), source=v):
+                #        self.G.nodes[vPred]["color"] = "red"
+                self.block_preds(diAgCell.values(), "red")
+
+    def check_motion(self, iAgent, rcPos):
+        """ Returns tuple of boolean can the agent move, and the cell it will move into.
+            If agent position is None, we use a dummy position of (-1, iAgent)
+        """
+
+        if rcPos is None:
+            rcPos = (-1, iAgent)
+
+        dAttr = self.G.nodes.get(rcPos)
+        #print("pos:", rcPos, "dAttr:", dAttr)
+
+        if dAttr is None:
+            dAttr = {}
+
+        # If it's been marked red or purple then it can't move
+        if "color" in dAttr:
+            sColor = dAttr["color"]
+            if sColor in [ "red", "purple" ]:
+                return False
+
+        dSucc = self.G.succ[rcPos]
+
+        # This should never happen - only the next cell of an agent has no successor
+        if len(dSucc)==0:
+            print(f"error condition - agent {iAgent} node {rcPos} has no successor")
+            return False
+
+        # This agent has a successor
+        rcNext = self.G.successors(rcPos).__next__()
+        if rcNext == rcPos:  # the agent didn't want to move
+            return False
+        # The agent wanted to move, and it can
+        return True
+
+
+
+
+
+def render(omc:MotionCheck, horizontal=True):
+    try:
+        oAG = nx.drawing.nx_agraph.to_agraph(omc.G)
+        oAG.layout("dot")
+        sDot = oAG.to_string()
+        if horizontal:
+            sDot = sDot.replace('{', '{ rankdir="LR" ')
+        #return oAG.draw(format="png")
+        # This returns a graphviz object which implements __repr_svg
+        return gv.Source(sDot)
+    except ImportError as oError:
+        print("Flatland agent_chains ignoring ImportError - install pygraphviz to render graphs")
+        return None
+
+
+class ChainTestEnv(object):
+    """ Just for testing agent chains
+    """
+    def __init__(self, omc:MotionCheck):
+        self.iAgNext = 0
+        self.iRowNext = 1
+        self.omc = omc
+
+    def addAgent(self, rc1, rc2, xlabel=None):
+        self.omc.addAgent(self.iAgNext, rc1, rc2, xlabel=xlabel)
+        self.iAgNext+=1
+
+    def addAgentToRow(self, c1, c2, xlabel=None):
+        self.addAgent((self.iRowNext, c1), (self.iRowNext, c2), xlabel=xlabel)
+
+
+    def create_test_chain(self,
+            nAgents:int,
+            rcVel:Tuple[int] = (0,1),
+            liStopped:List[int]=[],
+            xlabel=None):
+        """ create a chain of agents
+        """
+        lrcAgPos = [ (self.iRowNext, i * rcVel[1]) for i in range(nAgents) ]
+
+        for iAg, rcPos in zip(range(nAgents), lrcAgPos):
+            if iAg in liStopped:
+                rcVel1 = (0,0)
+            else:
+                rcVel1 = rcVel
+            self.omc.addAgent(iAg+self.iAgNext, rcPos, (rcPos[0] + rcVel1[0], rcPos[1] + rcVel1[1]) )
+
+        if xlabel:
+            self.omc.G.nodes[lrcAgPos[0]]["xlabel"] = xlabel
+
+        self.iAgNext += nAgents
+        self.iRowNext += 1
+
+    def nextRow(self):
+        self.iRowNext+=1
+
+
+
+def create_test_agents(omc:MotionCheck):
+
+    # blocked chain
+    omc.addAgent(1, (1,2), (1,3))
+    omc.addAgent(2, (1,3), (1,4))
+    omc.addAgent(3, (1,4), (1,5))
+    omc.addAgent(31, (1,5), (1,5))
+
+    # unblocked chain
+    omc.addAgent(4, (2,1), (2,2))
+    omc.addAgent(5, (2,2), (2,3))
+
+    # blocked short chain
+    omc.addAgent(6, (3,1), (3,2))
+    omc.addAgent(7, (3,2), (3,2))
+
+    # solitary agent
+    omc.addAgent(8, (4,1), (4,2))
+
+    # solitary stopped agent
+    omc.addAgent(9, (5,1), (5,1))
+
+    # blocked short chain (opposite direction)
+    omc.addAgent(10, (6,4), (6,3))
+    omc.addAgent(11, (6,3), (6,3))
+
+    # swap conflict
+    omc.addAgent(12, (7,1), (7,2))
+    omc.addAgent(13, (7,2), (7,1))
+
+
+def create_test_agents2(omc:MotionCheck):
+
+    # blocked chain
+    cte = ChainTestEnv(omc)
+    cte.create_test_chain(4, liStopped=[3], xlabel="stopped\nchain")
+    cte.create_test_chain(4, xlabel="running\nchain")
+
+    cte.create_test_chain(2, liStopped = [1], xlabel="stopped \nshort\n chain")
+
+    cte.addAgentToRow(1, 2, "swap")
+    cte.addAgentToRow(2, 1)
+
+    cte.nextRow()
+
+
+    cte.addAgentToRow(1, 2, "chain\nswap")
+    cte.addAgentToRow(2, 3)
+    cte.addAgentToRow(3, 2)
+
+    cte.nextRow()
+
+    cte.addAgentToRow(1, 2, "midchain\nstop")
+    cte.addAgentToRow(2, 3)
+    cte.addAgentToRow(3, 4)
+    cte.addAgentToRow(4, 4)
+    cte.addAgentToRow(5, 6)
+    cte.addAgentToRow(6, 7)
+
+    cte.nextRow()
+
+    cte.addAgentToRow(1, 2, "midchain\nswap")
+    cte.addAgentToRow(2, 3)
+    cte.addAgentToRow(3, 4)
+    cte.addAgentToRow(4, 3)
+    cte.addAgentToRow(5, 4)
+    cte.addAgentToRow(6, 5)
+
+    cte.nextRow()
+
+    cte.addAgentToRow(1, 2, "Land on\nSame")
+    cte.addAgentToRow(3, 2)
+
+    cte.nextRow()
+    cte.addAgentToRow(1, 2, "chains\nonto\nsame")
+    cte.addAgentToRow(2, 3)
+    cte.addAgentToRow(3, 4)
+    cte.addAgentToRow(5, 4)
+    cte.addAgentToRow(6, 5)
+    cte.addAgentToRow(7, 6)
+
+
+    cte.nextRow()
+    cte.addAgentToRow(1, 2, "3-way\nsame")
+    cte.addAgentToRow(3, 2)
+    cte.addAgent((cte.iRowNext+1, 2), (cte.iRowNext, 2))
+    cte.nextRow()
+
+    if False:
+        cte.nextRow()
+        cte.nextRow()
+        cte.addAgentToRow(1, 2, "4-way\nsame")
+        cte.addAgentToRow(3, 2)
+        cte.addAgent((cte.iRowNext+1, 2), (cte.iRowNext, 2))
+        cte.addAgent((cte.iRowNext-1, 2), (cte.iRowNext, 2))
+        cte.nextRow()
+
+    cte.nextRow()
+    cte.addAgentToRow(1, 2, "Tee")
+    cte.addAgentToRow(2, 3)
+    cte.addAgentToRow(3, 4)
+    cte.addAgent((cte.iRowNext+1, 3), (cte.iRowNext, 3))
+    cte.nextRow()
+
+
+    cte.nextRow()
+    cte.addAgentToRow(1, 2, "Tree")
+    cte.addAgentToRow(2, 3)
+    cte.addAgentToRow(3, 4)
+    r1 = cte.iRowNext
+    r2 = cte.iRowNext+1
+    r3 = cte.iRowNext+2
+    cte.addAgent((r2, 3), (r1, 3))
+    cte.addAgent((r2, 2), (r2, 3))
+    cte.addAgent((r3, 2), (r2, 3))
+
+    cte.nextRow()
+
+
+def test_agent_following():
+    omc = MotionCheck()
+    create_test_agents2(omc)
+
+    svStops = omc.find_stops()
+    svBlocked = omc.find_stop_preds()
+    llvSwaps = omc.find_swaps()
+    svSwaps = { v for lvSwap in llvSwaps for v in lvSwap }
+    print(list(svBlocked))
+
+    lvCells = omc.G.nodes()
+
+    lColours = [ "magenta" if v in svStops
+            else "red" if v in svBlocked
+            else "purple" if v in svSwaps
+            else "lightblue"
+            for v in lvCells ]
+    dPos = dict(zip(lvCells, lvCells))
+
+    nx.draw(omc.G, 
+        with_labels=True, arrowsize=20, 
+        pos=dPos,
+        node_color = lColours)
+
+def main():
+
+    test_agent_following()
+
+if __name__=="__main__":
+    main()

flatland/envs/agent_utils.py

-from itertools import starmap
-
+from flatland.envs.rail_trainrun_data_structures import Waypoint
 import numpy as np
-from attr import attrs, attrib, Factory
-
+import warnings
+
+from typing import Tuple, Optional, NamedTuple, List
+
+from attr import attr, attrs, attrib, Factory
+
+from flatland.core.grid.grid4 import Grid4TransitionsEnum
+from flatland.envs.timetable_utils import Line
+
+from flatland.envs.step_utils.action_saver import ActionSaver
+from flatland.envs.step_utils.speed_counter import SpeedCounter
+from flatland.envs.step_utils.state_machine import TrainStateMachine
+from flatland.envs.step_utils.states import TrainState
+from flatland.envs.step_utils.malfunction_handler import MalfunctionHandler
+
+Agent = NamedTuple('Agent', [('initial_position', Tuple[int, int]),
+                             ('initial_direction', Grid4TransitionsEnum),
+                             ('direction', Grid4TransitionsEnum),
+                             ('target', Tuple[int, int]),
+                             ('moving', bool),
+                             ('earliest_departure', int),
+                             ('latest_arrival', int),
+                             ('handle', int),
+                             ('position', Tuple[int, int]),
+                             ('arrival_time', int),
+                             ('old_direction', Grid4TransitionsEnum),
+                             ('old_position', Tuple[int, int]),
+                             ('speed_counter', SpeedCounter),
+                             ('action_saver', ActionSaver),
+                             ('state_machine', TrainStateMachine),
+                             ('malfunction_handler', MalfunctionHandler),
+                             ])
+
+
+def load_env_agent(agent_tuple: Agent):
+     return EnvAgent(
+                        initial_position = agent_tuple.initial_position,
+                        initial_direction = agent_tuple.initial_direction,
+                        direction = agent_tuple.direction,
+                        target = agent_tuple.target,
+                        moving = agent_tuple.moving,
+                        earliest_departure = agent_tuple.earliest_departure,
+                        latest_arrival = agent_tuple.latest_arrival,
+                        handle = agent_tuple.handle,
+                        position = agent_tuple.position,
+                        arrival_time = agent_tuple.arrival_time,
+                        old_direction = agent_tuple.old_direction,
+                        old_position = agent_tuple.old_position,
+                        speed_counter = agent_tuple.speed_counter,
+                        action_saver = agent_tuple.action_saver,
+                        state_machine = agent_tuple.state_machine,
+                        malfunction_handler = agent_tuple.malfunction_handler,
+                    )
 
 @attrs
-class EnvAgentStatic(object):
-    """ EnvAgentStatic - Stores initial position, direction and target.
-        This is like static data for the environment - it's where an agent starts,
-        rather than where it is at the moment.
-        The target should also be stored here.
-    """
-    position = attrib()
-    direction = attrib()
-    target = attrib()
-    moving = attrib(default=False)
-
-    # speed_data: speed is added to position_fraction on each moving step, until position_fraction>=1.0,
-    # after which 'transition_action_on_cellexit' is executed (equivalent to executing that action in the previous
-    # cell if speed=1, as default)
-    # N.B. we need to use factory since default arguments are not recreated on each call!
-    speed_data = attrib(
-        default=Factory(lambda: dict({'position_fraction': 0.0, 'speed': 1.0, 'transition_action_on_cellexit': 0})))
-
-    # if broken>0, the agent's actions are ignored for 'broken' steps
-    # number of time the agent had to stop, since the last time it broke down
-    malfunction_data = attrib(
-        default=Factory(
-            lambda: dict({'malfunction': 0, 'malfunction_rate': 0, 'next_malfunction': 0, 'nr_malfunctions': 0})))
+class EnvAgent:
+    # INIT FROM HERE IN _from_line()
+    initial_position = attrib(type=Tuple[int, int])
+    initial_direction = attrib(type=Grid4TransitionsEnum)
+    direction = attrib(type=Grid4TransitionsEnum)
+    target = attrib(type=Tuple[int, int])
+    moving = attrib(default=False, type=bool)
+
+    # NEW : EnvAgent - Schedule properties
+    earliest_departure = attrib(default=None, type=int)  # default None during _from_line()
+    latest_arrival = attrib(default=None, type=int)  # default None during _from_line()
 
-    @classmethod
-    def from_lists(cls, positions, directions, targets, speeds=None):
-        """ Create a list of EnvAgentStatics from lists of positions, directions and targets
-        """
-        speed_datas = []
-        for i in range(len(positions)):
-            speed_datas.append({'position_fraction': 0.0,
-                                'speed': speeds[i] if speeds is not None else 1.0,
-                                'transition_action_on_cellexit': 0})
-
-        # TODO: on initialization, all agents are re-set as non-broken. Perhaps it may be desirable to set
-        # some as broken?
-        malfunction_datas = []
-        for i in range(len(positions)):
-            malfunction_datas.append({'malfunction': 0,
-                                 'malfunction_rate': 0,
-                                      'next_malfunction': 0,
-                                      'nr_malfunctions': 0})
-
-        return list(starmap(EnvAgentStatic, zip(positions,
-                                                directions,
-                                                targets,
-                                                [False] * len(positions),
-                                                speed_datas,
-                                                malfunction_datas)))
-
-    def to_list(self):
-
-        # I can't find an expression which works on both tuples, lists and ndarrays
-        # which converts them all to a list of native python ints.
-        lPos = self.position
-        if type(lPos) is np.ndarray:
-            lPos = lPos.tolist()
-
-        lTarget = self.target
-        if type(lTarget) is np.ndarray:
-            lTarget = lTarget.tolist()
-
-        return [lPos, int(self.direction), lTarget, int(self.moving), self.speed_data, self.malfunction_data]
+    handle = attrib(default=None)
+    # INIT TILL HERE IN _from_line()
 
+    # Env step facelift
+    speed_counter = attrib(default = Factory(lambda: SpeedCounter(1.0)), type=SpeedCounter)
+    action_saver = attrib(default = Factory(lambda: ActionSaver()), type=ActionSaver)
+    state_machine = attrib(default= Factory(lambda: TrainStateMachine(initial_state=TrainState.WAITING)) ,
+                           type=TrainStateMachine)
+    malfunction_handler = attrib(default = Factory(lambda: MalfunctionHandler()), type=MalfunctionHandler)
 
-@attrs
-class EnvAgent(EnvAgentStatic):
-    """ EnvAgent - replace separate agent_* lists with a single list
-        of agent objects.  The EnvAgent represent's the environment's view
-        of the dynamic agent state.
-        We are duplicating target in the EnvAgent, which seems simpler than
-        forcing the env to refer to it in the EnvAgentStatic
-    """
-    handle = attrib(default=None)
+    position = attrib(default=None, type=Optional[Tuple[int, int]])
+
+    # NEW : EnvAgent Reward Handling
+    arrival_time = attrib(default=None, type=int)
+
+    # used in rendering
     old_direction = attrib(default=None)
     old_position = attrib(default=None)
 
-    def to_list(self):
-        return [
-            self.position, self.direction, self.target, self.handle,
-            self.old_direction, self.old_position, self.moving, self.speed_data, self.malfunction_data]
 
-    @classmethod
-    def from_static(cls, oStatic):
-        """ Create an EnvAgent from the EnvAgentStatic,
-        copying all the fields, and adding handle with the default 0.
+    def reset(self):
         """
-        return EnvAgent(*oStatic.__dict__, handle=0)
+        Resets the agents to their initial values of the episode. Called after ScheduleTime generation.
+        """
+        self.position = None
+        # TODO: set direction to None: https://gitlab.aicrowd.com/flatland/flatland/issues/280
+        self.direction = self.initial_direction
+        self.old_position = None
+        self.old_direction = None
+        self.moving = False
+        self.arrival_time = None
+
+        self.malfunction_handler.reset()
+
+        self.action_saver.clear_saved_action()
+        self.speed_counter.reset_counter()
+        self.state_machine.reset()
+
+    def to_agent(self) -> Agent:
+        return Agent(initial_position=self.initial_position,
+                     initial_direction=self.initial_direction,
+                     direction=self.direction,
+                     target=self.target,
+                     moving=self.moving,
+                     earliest_departure=self.earliest_departure,
+                     latest_arrival=self.latest_arrival,
+                     handle=self.handle,
+                     position=self.position,
+                     old_direction=self.old_direction,
+                     old_position=self.old_position,
+                     speed_counter=self.speed_counter,
+                     action_saver=self.action_saver,
+                     arrival_time=self.arrival_time,
+                     state_machine=self.state_machine,
+                     malfunction_handler=self.malfunction_handler)
+
+    def get_shortest_path(self, distance_map) -> List[Waypoint]:
+        from flatland.envs.rail_env_shortest_paths import get_shortest_paths # Circular dep fix
+        return get_shortest_paths(distance_map=distance_map, agent_handle=self.handle)[self.handle]
+
+    def get_travel_time_on_shortest_path(self, distance_map) -> int:
+        shortest_path = self.get_shortest_path(distance_map)
+        if shortest_path is not None:
+            distance = len(shortest_path)
+        else:
+            distance = 0
+        speed = self.speed_counter.speed
+        return int(np.ceil(distance / speed))
+
+    def get_time_remaining_until_latest_arrival(self, elapsed_steps: int) -> int:
+        return self.latest_arrival - elapsed_steps
+
+    def get_current_delay(self, elapsed_steps: int, distance_map) -> int:
+        '''
+        +ve if arrival time is projected before latest arrival
+        -ve if arrival time is projected after latest arrival
+        '''
+        return self.get_time_remaining_until_latest_arrival(elapsed_steps) - \
+               self.get_travel_time_on_shortest_path(distance_map)
+
 
     @classmethod
-    def list_from_static(cls, lEnvAgentStatic, handles=None):
-        """ Create an EnvAgent from the EnvAgentStatic,
-        copying all the fields, and adding handle with the default 0.
+    def from_line(cls, line: Line):
+        """ Create a list of EnvAgent from lists of positions, directions and targets
         """
-        if handles is None:
-            handles = range(len(lEnvAgentStatic))
+        num_agents = len(line.agent_positions)
+
+        agent_list = []
+        for i_agent in range(num_agents):
+            speed = line.agent_speeds[i_agent] if line.agent_speeds is not None else 1.0
+
+            agent = EnvAgent(initial_position = line.agent_positions[i_agent],
+                            initial_direction = line.agent_directions[i_agent],
+                            direction = line.agent_directions[i_agent],
+                            target = line.agent_targets[i_agent],
+                            moving = False,
+                            earliest_departure = None,
+                            latest_arrival = None,
+                            handle = i_agent,
+                            speed_counter = SpeedCounter(speed=speed))
+            agent_list.append(agent)
+
+        return agent_list
+
+    @classmethod
+    def load_legacy_static_agent(cls, static_agents_data: Tuple):
+        agents = []
+        for i, static_agent in enumerate(static_agents_data):
+            if len(static_agent) >= 6:
+                agent = EnvAgent(initial_position=static_agent[0], initial_direction=static_agent[1],
+                                direction=static_agent[1], target=static_agent[2], moving=static_agent[3],
+                                speed_counter=SpeedCounter(static_agent[4]['speed']), handle=i)
+            else:
+                agent = EnvAgent(initial_position=static_agent[0], initial_direction=static_agent[1],
+                                direction=static_agent[1], target=static_agent[2],
+                                moving=False,
+                                speed_counter=SpeedCounter(1.0),
+                                handle=i)
+            agents.append(agent)
+        return agents
+
+    def __str__(self):
+        return f"\n \
+                 handle(agent index): {self.handle} \n \
+                 initial_position: {self.initial_position}  \n \
+                 initial_direction: {self.initial_direction} \n \
+                 position: {self.position}  \n \
+                 direction: {self.direction}  \n \
+                 target: {self.target} \n \
+                 old_position: {self.old_position} \n \
+                 old_direction {self.old_direction} \n \
+                 earliest_departure: {self.earliest_departure}  \n \
+                 latest_arrival: {self.latest_arrival} \n \
+                 state: {str(self.state)} \n \
+                 malfunction_handler: {self.malfunction_handler} \n \
+                 action_saver: {self.action_saver} \n \
+                 speed_counter: {self.speed_counter}"
+
+    @property
+    def state(self):
+        return self.state_machine.state
+
+    @state.setter
+    def state(self, state):
+        self._set_state(state)
+
+    def _set_state(self, state):
+        warnings.warn("Not recommended to set the state with this function unless completely required")
+        self.state_machine.set_state(state)
+
+    @property
+    def malfunction_data(self):
+        raise ValueError("agent.malunction_data is deprecated, please use agent.malfunction_hander instead")
+
+    @property
+    def speed_data(self):
+        raise ValueError("agent.speed_data is deprecated, please use agent.speed_counter instead")
+
+
+
 
-        return [EnvAgent(**oEAS.__dict__, handle=handle)
-                for handle, oEAS in zip(handles, lEnvAgentStatic)]

flatland/envs/distance_map.py

+from collections import deque
+from typing import List, Optional
+
+import numpy as np
+
+from flatland.core.grid.grid4_utils import get_new_position
+from flatland.core.transition_map import GridTransitionMap
+from flatland.envs.agent_utils import EnvAgent
+
+
+class DistanceMap:
+    def __init__(self, agents: List[EnvAgent], env_height: int, env_width: int):
+        self.env_height = env_height
+        self.env_width = env_width
+        self.distance_map = None
+        self.agents_previous_computation = None
+        self.reset_was_called = False
+        self.agents: List[EnvAgent] = agents
+        self.rail: Optional[GridTransitionMap] = None
+
+    def set(self, distance_map: np.ndarray):
+        """
+        Set the distance map
+        """
+        self.distance_map = distance_map
+
+    def get(self) -> np.ndarray:
+        """
+        Get the distance map
+        """
+        if self.reset_was_called:
+            self.reset_was_called = False
+
+            compute_distance_map = True
+            # Don't compute the distance map if it was loaded
+            if self.agents_previous_computation is None and self.distance_map is not None:
+                compute_distance_map = False
+
+            if compute_distance_map:
+                self._compute(self.agents, self.rail)
+
+        elif self.distance_map is None:
+            self._compute(self.agents, self.rail)
+
+        return self.distance_map
+
+    def reset(self, agents: List[EnvAgent], rail: GridTransitionMap):
+        """
+        Reset the distance map
+        """
+        self.reset_was_called = True
+        self.agents: List[EnvAgent] = agents
+        self.rail = rail
+        self.env_height = rail.height
+        self.env_width = rail.width
+
+    def _compute(self, agents: List[EnvAgent], rail: GridTransitionMap):
+        """
+        This function computes the distance maps for each unique target. Thus if several targets are the same
+        we only compute the distance for them once and copy to all targets with same position.
+        :param agents: All the agents in the environment, independent of their current status
+        :param rail: The rail transition map
+
+        """
+        self.agents_previous_computation = self.agents
+        self.distance_map = np.inf * np.ones(shape=(len(agents),
+                                                    self.env_height,
+                                                    self.env_width,
+                                                    4))
+
+        computed_targets = []
+        for i, agent in enumerate(agents):
+            if agent.target not in computed_targets:
+                self._distance_map_walker(rail, agent.target, i)
+            else:
+                # just copy the distance map form other agent with same target (performance)
+                self.distance_map[i, :, :, :] = np.copy(
+                    self.distance_map[computed_targets.index(agent.target), :, :, :])
+            computed_targets.append(agent.target)
+
+    def _distance_map_walker(self, rail: GridTransitionMap, position, target_nr: int):
+        """
+        Utility function to compute distance maps from each cell in the rail network (and each possible
+        orientation within it) to each agent's target cell.
+        """
+        # Returns max distance to target, from the farthest away node, while filling in distance_map
+        self.distance_map[target_nr, position[0], position[1], :] = 0
+
+        # Fill in the (up to) 4 neighboring nodes
+        # direction is the direction of movement, meaning that at least a possible orientation of an agent
+        # in cell (row,col) allows a movement in direction `direction'
+        nodes_queue = deque(self._get_and_update_neighbors(rail, position, target_nr, 0, enforce_target_direction=-1))
+
+        # BFS from target `position' to all the reachable nodes in the grid
+        # Stop the search if the target position is re-visited, in any direction
+        visited = {(position[0], position[1], 0), (position[0], position[1], 1), (position[0], position[1], 2),
+                   (position[0], position[1], 3)}
+
+        max_distance = 0
+
+        while nodes_queue:
+            node = nodes_queue.popleft()
+
+            node_id = (node[0], node[1], node[2])
+
+            if node_id not in visited:
+                visited.add(node_id)
+
+                # From the list of possible neighbors that have at least a path to the current node, only keep those
+                # whose new orientation in the current cell would allow a transition to direction node[2]
+                valid_neighbors = self._get_and_update_neighbors(rail, (node[0], node[1]), target_nr, node[3], node[2])
+
+                for n in valid_neighbors:
+                    nodes_queue.append(n)
+
+                if len(valid_neighbors) > 0:
+                    max_distance = max(max_distance, node[3] + 1)
+
+        return max_distance
+
+    def _get_and_update_neighbors(self, rail: GridTransitionMap, position, target_nr, current_distance,
+                                  enforce_target_direction=-1):
+        """
+        Utility function used by _distance_map_walker to perform a BFS walk over the rail, filling in the
+        minimum distances from each target cell.
+        """
+        neighbors = []
+
+        possible_directions = [0, 1, 2, 3]
+        if enforce_target_direction >= 0:
+            # The agent must land into the current cell with orientation `enforce_target_direction'.
+            # This is only possible if the agent has arrived from the cell in the opposite direction!
+            possible_directions = [(enforce_target_direction + 2) % 4]
+
+        for neigh_direction in possible_directions:
+            new_cell = get_new_position(position, neigh_direction)
+
+            if new_cell[0] >= 0 and new_cell[0] < self.env_height and new_cell[1] >= 0 and new_cell[1] < self.env_width:
+
+                desired_movement_from_new_cell = (neigh_direction + 2) % 4
+
+                # Check all possible transitions in new_cell
+                for agent_orientation in range(4):
+                    # Is a transition along movement `desired_movement_from_new_cell' to the current cell possible?
+                    is_valid = rail.get_transition((new_cell[0], new_cell[1], agent_orientation),
+                                                   desired_movement_from_new_cell)
+
+                    if is_valid:
+                        """
+                        # TODO: check that it works with deadends! -- still bugged!
+                        movement = desired_movement_from_new_cell
+                        if isNextCellDeadEnd:
+                            movement = (desired_movement_from_new_cell+2) % 4
+                        """
+                        new_distance = min(self.distance_map[target_nr, new_cell[0], new_cell[1], agent_orientation],
+                                           current_distance + 1)
+                        neighbors.append((new_cell[0], new_cell[1], agent_orientation, new_distance))
+                        self.distance_map[target_nr, new_cell[0], new_cell[1], agent_orientation] = new_distance
+
+        return neighbors

flatland/envs/fast_methods.py

+from typing import Tuple
+
+
+# Adrian Egli / Michel Marti performance fix (the fast methods brings more than 50%)
+def fast_isclose(a, b, rtol):
+    return (a < (b + rtol)) or (a < (b - rtol))
+
+
+def fast_clip(position: Tuple[int, int], min_value: Tuple[int, int], max_value: Tuple[int, int]) -> bool:
+    return (
+        max(min_value[0], min(position[0], max_value[0])),
+        max(min_value[1], min(position[1], max_value[1]))
+    )
+
+
+def fast_argmax(possible_transitions: (int, int, int, int)) -> bool:
+    if possible_transitions[0] == 1:
+        return 0
+    if possible_transitions[1] == 1:
+        return 1
+    if possible_transitions[2] == 1:
+        return 2
+    return 3
+
+
+def fast_position_equal(pos_1: (int, int), pos_2: (int, int)) -> bool:
+    if pos_1 is None and pos_2 is None:
+        return True
+    if pos_1 is None or pos_2 is None:
+        return False
+    return pos_1[0] == pos_2[0] and pos_1[1] == pos_2[1]
+
+
+def fast_count_nonzero(possible_transitions: (int, int, int, int)):
+    return possible_transitions[0] + possible_transitions[1] + possible_transitions[2] + possible_transitions[3]
+
+
+def fast_delete(lis: list, index) -> list:
+    new_list = lis.copy()
+    new_list.pop(index)
+    return new_list
+
+
+def fast_where(binary_iterable):
+    return [index for index, element in enumerate(binary_iterable) if element != 0]