From 7f4cc45c8192e95177a35cc6ad7e6b49eb1a5b4f Mon Sep 17 00:00:00 2001
From: Nilabha <nilabha2007@gmail.com>
Date: Sun, 18 Jul 2021 15:23:22 +0530
Subject: [PATCH] pettingzoo interface and training
---
examples/env_generators.py | 137 +++++++++++++++
examples/flatland_env.py | 297 ++++++++++++++++++++++++++++++++
examples/flatland_pettingzoo.py | 117 +++++++++++++
3 files changed, 551 insertions(+)
create mode 100644 examples/env_generators.py
create mode 100644 examples/flatland_env.py
create mode 100644 examples/flatland_pettingzoo.py
diff --git a/examples/env_generators.py b/examples/env_generators.py
new file mode 100644
index 00000000..71e9d1ec
--- /dev/null
+++ b/examples/env_generators.py
@@ -0,0 +1,137 @@
+import logging
+import random
+import numpy as np
+from typing import NamedTuple
+
+from flatland.envs.malfunction_generators import malfunction_from_params, MalfunctionParameters, ParamMalfunctionGen
+from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import sparse_rail_generator
+from flatland.envs.schedule_generators import sparse_schedule_generator
+from flatland.envs.agent_utils import RailAgentStatus
+
+MalfunctionParameters = NamedTuple('MalfunctionParameters', [('malfunction_rate', float), ('min_duration', int), ('max_duration', int)])
+
+
+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_rails_in_city=max_rails_in_cities)
+
+ # new version:
+ # stochastic_data = MalfunctionParameters(malfunction_rate, malfunction_min_duration, malfunction_max_duration)
+
+ stochastic_data = {'malfunction_rate': malfunction_rate, 'min_duration': malfunction_min_duration,
+ 'max_duration': malfunction_max_duration}
+
+ schedule_generator = sparse_schedule_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,
+ schedule_generator=schedule_generator, number_of_agents=nr_trains,
+ malfunction_generator_and_process_data=malfunction_from_params(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_rails_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
+
+ schedule_generator = sparse_schedule_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,
+ schedule_generator=schedule_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
+ for current_agent in env.agents_data:
+ if current_agent.status == RailAgentStatus.DONE_REMOVED:
+ tasks_finished += 1
+
+ return 100 * np.mean(tasks_finished / max(
+ 1, env.num_agents))
\ No newline at end of file
diff --git a/examples/flatland_env.py b/examples/flatland_env.py
new file mode 100644
index 00000000..b7f593f9
--- /dev/null
+++ b/examples/flatland_env.py
@@ -0,0 +1,297 @@
+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 parallel_wrapper_fn
+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
+
+
+def env(**kwargs):
+ env = raw_env(**kwargs)
+ # env = wrappers.AssertOutOfBoundsWrapper(env)
+ # env = wrappers.OrderEnforcingWrapper(env)
+ return env
+
+
+parallel_env = parallel_wrapper_fn(env)
+
+
+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_name_mapping = dict(zip(self.agents, list(range(self.n_pistons))))
+ self._agent_selector = agent_selector(self.agents)
+ # self.agent_selection = self.agents
+ # self.observation_spaces = dict(
+ # zip(self.agents, [gym.spaces.Box(low=0, high=1,shape = (1,1) , dtype=np.float32)] * n_agents))
+ self.num_actions = 5
+
+ self.action_spaces = {
+ agent: gym.spaces.Discrete(self.num_actions) for agent in self.possible_agents
+ }
+ # self.state_space = gym.spaces.Box(low=0, high=255, shape=(self.screen_height, self.screen_width, 3), dtype=np.uint8)
+ # self.closed = False
+ 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 seed(self, seed: int = None) -> None:
+ self._environment._seed(seed)
+
+ def state(self):
+ '''
+ Returns an observation of the global environment
+ '''
+ return None
+
+
+ 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 = {i:0 for i in self.possible_agents}
+
+ return observations
+
+ def step(self, action):
+
+ if self.env_done():
+ self._agents = []
+ return self.last()
+
+ agent = self.agent_selection
+ self.action_dict[get_agent_handle(agent)] = action
+ if self._reset_next_step:
+ return self.reset()
+
+ if self.dones[agent]:
+ self.agents.remove(agent)
+ if not self.env_done():
+ self.agent_selection = self._agent_selector.next()
+ return self.last()
+
+ 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._agents = [agent
+ # for agent in self.agents
+ # if not self._environment.dones[get_agent_handle(agent)]
+ # ]
+
+ else:
+ self._clear_rewards()
+
+ # self._cumulative_rewards[agent] = 0
+ self._accumulate_rewards()
+
+ self.agent_selection = self._agent_selector.next()
+
+ return self.last()
+
+ # if self._agent_selector.is_last():
+ # self._agent_selector.reinit(self.agents)
+
+ # 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
diff --git a/examples/flatland_pettingzoo.py b/examples/flatland_pettingzoo.py
new file mode 100644
index 00000000..b3e108cd
--- /dev/null
+++ b/examples/flatland_pettingzoo.py
@@ -0,0 +1,117 @@
+
+import numpy as np
+import os
+
+from stable_baselines3.ppo import MlpPolicy
+from stable_baselines3 import PPO
+import supersuit as ss
+
+import flatland_env
+import env_generators
+
+from gym.wrappers import monitor
+from flatland.envs.observations import TreeObsForRailEnv,GlobalObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+
+# First of all we import the Flatland rail environment
+from flatland.envs.rail_env import RailEnv
+import wandb
+
+# Custom observation builder without predictor
+# observation_builder = GlobalObsForRailEnv()
+
+# Custom observation builder with predictor, uncomment line below if you want to try this one
+observation_builder = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
+seed = 10
+experiment_name= "flatland_pettingzoo"
+# rail_env = env_generators.sparse_env_small(seed, observation_builder)
+rail_env = env_generators.random_sparse_env_small(seed, observation_builder)
+env = flatland_env.parallel_env(environment = rail_env, use_renderer = False)
+run = wandb.init(project="flatland2021", entity="nilabha2007", sync_tensorboard=True, config={}, name=experiment_name, save_code=True)
+
+env = ss.pettingzoo_env_to_vec_env_v0(env)
+env.black_death = True
+env = ss.concat_vec_envs_v0(env, 3, num_cpus=3, base_class='stable_baselines3')
+model = PPO(MlpPolicy, env, tensorboard_log = f"/tmp/{experiment_name}", verbose=3, gamma=0.95, n_steps=100, ent_coef=0.09, learning_rate=0.005, vf_coef=0.04, max_grad_norm=0.9, gae_lambda=0.99, n_epochs=50, clip_range=0.3, batch_size=200)
+# wandb.watch(model.policy.action_net,log='all', log_freq = 1)
+# wandb.watch(model.policy.value_net, log='all', log_freq = 1)
+train_timesteps = 1000000
+model.learn(total_timesteps=train_timesteps)
+model.save(f"policy_flatland_{train_timesteps}")
+
+env = flatland_env.env(environment = rail_env, use_renderer = True)
+env_name="flatland"
+monitor.FILE_PREFIX = env_name
+monitor.Monitor._after_step = env_generators._after_step
+env = monitor.Monitor(env, experiment_name, force=True)
+model = PPO.load(f"policy_flatland_{train_timesteps}")
+
+
+artifact = wandb.Artifact('model', type='model')
+artifact.add_file(f'policy_flatland_{train_timesteps}.zip')
+run.log_artifact(artifact)
+
+env.reset(random_seed=seed)
+step = 0
+for agent in env.agent_iter():
+ obs, reward, done, info = env.last()
+ act = model.predict(obs, deterministic=True)[0] if not done else None
+ # act = 2
+ env.step(act)
+ step+=1
+ if step % 100 == 0:
+ print(act)
+ completion = env_generators.perc_completion(env)
+ print("Agents Completed:",completion)
+env.close()
+completion = env_generators.perc_completion(env)
+print("Agents Completed:",completion)
+
+
+import fnmatch
+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
+
+
+_video_file = f'*0.mp4'
+_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="mp4")})
+run.join()
+
+
+
+
+
+
+
+
+
+
+
+# from pettingzoo.test.api_test import api_test
+# api_test(env)
+
+# env.reset(random_seed=seed)
+
+# action_dict = dict()
+# step = 0
+# for agent in env.agent_iter(max_iter=2500):
+# if step == 433:
+# print(step)
+# obs, reward, done, info = env.last()
+# action = 2 # controller.act(0)
+# action_dict.update({agent: action})
+# env.step(action)
+# step += 1
+# if step % 50 == 0:
+# print(step)
+# if step > 400:
+# print(step)
+# # env.render()
\ No newline at end of file
--
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