Flatland3 pettingzoo

flatland/contrib/interface/flatland_env.py

+import os
+import math
+import numpy as np
+import gym
+from gym.utils import seeding
+from pettingzoo import AECEnv
+from pettingzoo.utils import agent_selector
+from pettingzoo.utils import wrappers
+from gym.utils import EzPickle
+from pettingzoo.utils.conversions import to_parallel_wrapper
+from flatland.envs.rail_env import RailEnv
+from mava.wrappers.flatland import infer_observation_space, normalize_observation
+from functools import partial
+from flatland.envs.observations import GlobalObsForRailEnv, TreeObsForRailEnv
+
+
+"""Adapted from 
+- https://github.com/PettingZoo-Team/PettingZoo/blob/HEAD/pettingzoo/butterfly/pistonball/pistonball.py
+- https://github.com/instadeepai/Mava/blob/HEAD/mava/wrappers/flatland.py
+"""
+
+def parallel_wrapper_fn(env_fn):
+    def par_fn(**kwargs):
+        env = env_fn(**kwargs)
+        env = custom_parallel_wrapper(env)
+        return env
+    return par_fn
+
+def env(**kwargs):
+    env = raw_env(**kwargs)
+    # env = wrappers.AssertOutOfBoundsWrapper(env)
+    # env = wrappers.OrderEnforcingWrapper(env)
+    return env
+
+
+parallel_env = parallel_wrapper_fn(env)
+
+class custom_parallel_wrapper(to_parallel_wrapper):
+    
+    def step(self, actions):
+        rewards = {a: 0 for a in self.aec_env.agents}
+        dones = {}
+        infos = {}
+        observations = {}
+
+        for agent in self.aec_env.agents:
+            try:
+                assert agent == self.aec_env.agent_selection, f"expected agent {agent} got agent {self.aec_env.agent_selection}, agent order is nontrivial"
+            except Exception as e:
+                # print(e)
+                print(self.aec_env.dones.values())
+                raise e
+            obs, rew, done, info = self.aec_env.last()
+            self.aec_env.step(actions.get(agent,0))
+            for agent in self.aec_env.agents:
+                rewards[agent] += self.aec_env.rewards[agent]
+
+        dones = dict(**self.aec_env.dones)
+        infos = dict(**self.aec_env.infos)
+        self.agents = self.aec_env.agents
+        observations = {agent: self.aec_env.observe(agent) for agent in self.aec_env.agents}
+        return observations, rewards, dones, infos
+
+class raw_env(AECEnv, gym.Env):
+
+    metadata = {'render.modes': ['human', "rgb_array"], 'name': "flatland_pettingzoo",
+            'video.frames_per_second': 10,
+            'semantics.autoreset': False }
+
+    def __init__(self, environment = False, preprocessor = False, agent_info = False, use_renderer=False, *args, **kwargs):
+        # EzPickle.__init__(self, *args, **kwargs)
+        self._environment = environment
+        self.use_renderer = use_renderer
+        self.renderer = None
+        if self.use_renderer:
+            self.initialize_renderer()
+
+        n_agents = self.num_agents
+        self._agents = [get_agent_keys(i) for i in range(n_agents)]
+        self._possible_agents = self.agents[:]
+        self._reset_next_step = True
+
+        self._agent_selector = agent_selector(self.agents)
+
+        self.num_actions = 5
+
+        self.action_spaces = {
+            agent: gym.spaces.Discrete(self.num_actions) for agent in self.possible_agents
+        }              
+
+        self.seed()
+        # preprocessor must be for observation builders other than global obs
+        # treeobs builders would use the default preprocessor if none is
+        # supplied
+        self.preprocessor = self._obtain_preprocessor(preprocessor)
+
+        self._include_agent_info = agent_info
+
+        # observation space:
+        # flatland defines no observation space for an agent. Here we try
+        # to define the observation space. All agents are identical and would
+        # have the same observation space.
+        # Infer observation space based on returned observation
+        obs, _ = self._environment.reset(regenerate_rail = False, regenerate_schedule = False)
+        obs = self.preprocessor(obs)
+        self.observation_spaces = {
+            i: infer_observation_space(ob) for i, ob in obs.items()
+        }
+
+    
+    @property
+    def environment(self) -> RailEnv:
+        """Returns the wrapped environment."""
+        return self._environment
+
+    @property
+    def dones(self):
+        dones = self._environment.dones
+        # remove_all = dones.pop("__all__", None)
+        return {get_agent_keys(key): value for key, value in dones.items()}    
+    
+    @property
+    def obs_builder(self):    
+        return self._environment.obs_builder    
+
+    @property
+    def width(self):    
+        return self._environment.width  
+
+    @property
+    def height(self):    
+        return self._environment.height  
+
+    @property
+    def agents_data(self):
+        """Rail Env Agents data."""
+        return self._environment.agents
+
+    @property
+    def num_agents(self) -> int:
+        """Returns the number of trains/agents in the flatland environment"""
+        return int(self._environment.number_of_agents)
+
+    # def __getattr__(self, name):
+    #     """Expose any other attributes of the underlying environment."""
+    #     return getattr(self._environment, name)
+   
+    @property
+    def agents(self):
+        return self._agents
+
+    @property
+    def possible_agents(self):
+        return self._possible_agents
+
+    def env_done(self):
+        return self._environment.dones["__all__"] or not self.agents
+    
+    def observe(self,agent):
+        return self.obs.get(agent)
+    
+    def last(self, observe=True):
+        '''
+        returns observation, reward, done, info   for the current agent (specified by self.agent_selection)
+        '''
+        agent = self.agent_selection
+        observation = self.observe(agent) if observe else None
+        return observation, self.rewards.get(agent), self.dones.get(agent), self.infos.get(agent)
+    
+    def seed(self, seed: int = None) -> None:
+        self._environment._seed(seed)
+
+    def state(self):
+        '''
+        Returns an observation of the global environment
+        '''
+        return None
+
+    def _clear_rewards(self):
+        '''
+        clears all items in .rewards
+        '''
+        # pass
+        for agent in self.rewards:
+            self.rewards[agent] = 0
+    
+    def reset(self, *args, **kwargs):
+        self._reset_next_step = False
+        self._agents = self.possible_agents[:]
+        if self.use_renderer:
+            if self.renderer: #TODO: Errors with RLLib with renderer as None.
+                self.renderer.reset()
+        obs, info = self._environment.reset(*args, **kwargs)
+        observations = self._collate_obs_and_info(obs, info)
+        self._agent_selector.reinit(self.agents)
+        self.agent_selection = self._agent_selector.next()
+        self.rewards = dict(zip(self.agents, [0 for _ in self.agents]))
+        self._cumulative_rewards = dict(zip(self.agents, [0 for _ in self.agents]))
+        self.action_dict = {get_agent_handle(i):0 for i in self.possible_agents}
+
+        return observations
+
+    def step(self, action):
+
+        if self.env_done():
+            self._agents = []
+            self._reset_next_step = True
+            return self.last()
+        
+        agent = self.agent_selection
+        self.action_dict[get_agent_handle(agent)] = action
+
+        if self.dones[agent]:
+            # Disabled.. In case we want to remove agents once done
+            # if self.remove_agents:
+            #     self.agents.remove(agent)
+            if self._agent_selector.is_last():
+                observations, rewards, dones, infos = self._environment.step(self.action_dict)
+                self.rewards = {get_agent_keys(key): value for key, value in rewards.items()}
+                if observations:
+                    observations = self._collate_obs_and_info(observations, infos)
+                self._accumulate_rewards()
+                obs, cumulative_reward, done, info = self.last()
+                self.agent_selection = self._agent_selector.next()
+
+            else:
+                self._clear_rewards()
+                obs, cumulative_reward, done, info = self.last()
+                self.agent_selection = self._agent_selector.next()
+
+            return obs, cumulative_reward, done, info
+
+        if self._agent_selector.is_last():
+            observations, rewards, dones, infos = self._environment.step(self.action_dict)
+            self.rewards = {get_agent_keys(key): value for key, value in rewards.items()}
+            if observations:
+                observations = self._collate_obs_and_info(observations, infos)
+    
+        else:
+            self._clear_rewards()
+        
+        # self._cumulative_rewards[agent] = 0
+        self._accumulate_rewards()
+
+        obs, cumulative_reward, done, info = self.last()
+        
+        self.agent_selection = self._agent_selector.next()
+
+        return obs, cumulative_reward, done, info
+
+
+    # collate agent info and observation into a tuple, making the agents obervation to
+    # be a tuple of the observation from the env and the agent info
+    def _collate_obs_and_info(self, observes, info):
+        observations = {}
+        infos = {}
+        observes = self.preprocessor(observes)
+        for agent, obs in observes.items():
+            all_infos = {k: info[k][get_agent_handle(agent)] for k in info.keys()}
+            agent_info = np.array(
+                list(all_infos.values()), dtype=np.float32
+            )
+            infos[agent] = all_infos
+            obs = (obs, agent_info) if self._include_agent_info else obs
+            observations[agent] = obs
+
+        self.infos = infos
+        self.obs = observations
+        return observations   
+
+
+    def render(self, mode='human'):
+        """
+        This methods provides the option to render the
+        environment's behavior to a window which should be
+        readable to the human eye if mode is set to 'human'.
+        """
+        if not self.use_renderer:
+            return
+
+        if not self.renderer:
+            self.initialize_renderer(mode=mode)
+
+        return self.update_renderer(mode=mode)
+
+    def initialize_renderer(self, mode="human"):
+        # Initiate the renderer
+        from flatland.utils.rendertools import RenderTool, AgentRenderVariant
+        self.renderer = RenderTool(self.environment, gl="PGL",  # gl="TKPILSVG",
+                                       agent_render_variant=AgentRenderVariant.ONE_STEP_BEHIND,
+                                       show_debug=False,
+                                       screen_height=600,  # Adjust these parameters to fit your resolution
+                                       screen_width=800)  # Adjust these parameters to fit your resolution
+        self.renderer.show = False
+
+    def update_renderer(self, mode='human'):
+        image = self.renderer.render_env(show=False, show_observations=False, show_predictions=False,
+                                             return_image=True)
+        return image[:,:,:3]
+
+    def set_renderer(self, renderer):
+        self.use_renderer = renderer
+        if self.use_renderer:
+            self.initialize_renderer(mode=self.use_renderer)
+
+    def close(self):
+        # self._environment.close()
+        if self.renderer:
+            try:
+                if self.renderer.show:
+                    self.renderer.close_window()
+            except Exception as e:
+                print("Could Not close window due to:",e)
+            self.renderer = None
+
+    def _obtain_preprocessor(
+        self, preprocessor):
+        """Obtains the actual preprocessor to be used based on the supplied
+        preprocessor and the env's obs_builder object"""
+        if not isinstance(self.obs_builder, GlobalObsForRailEnv):
+            _preprocessor = preprocessor if preprocessor else lambda x: x
+            if isinstance(self.obs_builder, TreeObsForRailEnv):
+                _preprocessor = (
+                    partial(
+                        normalize_observation, tree_depth=self.obs_builder.max_depth
+                    )
+                    if not preprocessor
+                    else preprocessor
+                )
+            assert _preprocessor is not None
+        else:
+            def _preprocessor(x):
+                    return x
+
+        def returned_preprocessor(obs):
+            temp_obs = {}
+            for agent_id, ob in obs.items():
+                temp_obs[get_agent_keys(agent_id)] = _preprocessor(ob)
+            return temp_obs
+
+        return returned_preprocessor
+
+# Utility functions   
+def convert_np_type(dtype, value):
+    return np.dtype(dtype).type(value) 
+
+def get_agent_handle(id):
+    """Obtain an agents handle given its id"""
+    return int(id)
+
+def get_agent_keys(id):
+    """Obtain an agents handle given its id"""
+    return str(id)
\ No newline at end of file

flatland/contrib/requirements_training.txt

+id-mava[flatland]
+id-mava
+id-mava[tf]
+supersuit
+stable-baselines3
+ray==1.5.2
\ No newline at end of file

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/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 RailAgentStatus
+from flatland.core.grid.grid4_utils import get_new_position
+
+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 == RailAgentStatus.READY_TO_DEPART:
+        agent_virtual_position = agent.initial_position