279 lines
12 KiB
Python
279 lines
12 KiB
Python
import copy
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import json
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from typing import Any, Dict, Optional, SupportsFloat, Tuple
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import gymnasium
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from gymnasium.core import ActType, ObsType
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from ray.rllib.env.multi_agent_env import MultiAgentEnv
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from primaite import getLogger
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from primaite.game.agent.interface import ProxyAgent
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from primaite.game.game import PrimaiteGame
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from primaite.session.io import PrimaiteIO
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from primaite.simulator import SIM_OUTPUT
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_LOGGER = getLogger(__name__)
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class PrimaiteGymEnv(gymnasium.Env):
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"""
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Thin wrapper env to provide agents with a gymnasium API.
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This is always a single agent environment since gymnasium is a single agent API. Therefore, we can make some
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assumptions about the agent list always having a list of length 1.
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"""
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def __init__(self, game_config: Dict):
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"""Initialise the environment."""
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super().__init__()
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self.game_config: Dict = game_config
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"""PrimaiteGame definition. This can be changed between episodes to enable curriculum learning."""
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self.io = PrimaiteIO.from_config(game_config.get("io_settings", {}))
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"""Handles IO for the environment. This produces sys logs, agent logs, etc."""
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self.game: PrimaiteGame = PrimaiteGame.from_config(copy.deepcopy(self.game_config))
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"""Current game."""
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self._agent_name = next(iter(self.game.rl_agents))
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"""Name of the RL agent. Since there should only be one RL agent we can just pull the first and only key."""
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self.episode_counter: int = 0
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"""Current episode number."""
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@property
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def agent(self) -> ProxyAgent:
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"""Grab a fresh reference to the agent object because it will be reinstantiated each episode."""
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return self.game.rl_agents[self._agent_name]
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def step(self, action: ActType) -> Tuple[ObsType, SupportsFloat, bool, bool, Dict[str, Any]]:
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"""Perform a step in the environment."""
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# make ProxyAgent store the action chosen my the RL policy
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step = self.game.step_counter
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self.agent.store_action(action)
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# apply_agent_actions accesses the action we just stored
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self.game.apply_agent_actions()
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self.game.advance_timestep()
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state = self.game.get_sim_state()
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self.game.update_agents(state)
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next_obs = self._get_obs() # this doesn't update observation, just gets the current observation
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reward = self.agent.reward_function.current_reward
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terminated = False
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truncated = self.game.calculate_truncated()
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info = {
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"agent_actions": {name: agent.action_history[-1] for name, agent in self.game.agents.items()}
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} # tell us what all the agents did for convenience.
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if self.game.save_step_metadata:
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self._write_step_metadata_json(step, action, state, reward)
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return next_obs, reward, terminated, truncated, info
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def _write_step_metadata_json(self, step: int, action: int, state: Dict, reward: int):
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output_dir = SIM_OUTPUT.path / f"episode_{self.episode_counter}" / "step_metadata"
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output_dir.mkdir(parents=True, exist_ok=True)
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path = output_dir / f"step_{step}.json"
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data = {
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"episode": self.episode_counter,
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"step": step,
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"action": int(action),
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"reward": int(reward),
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"state": state,
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}
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with open(path, "w") as file:
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json.dump(data, file)
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def reset(self, seed: Optional[int] = None) -> Tuple[ObsType, Dict[str, Any]]:
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"""Reset the environment."""
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_LOGGER.info(
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f"Resetting environment, episode {self.episode_counter}, "
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f"avg. reward: {self.agent.reward_function.total_reward}"
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)
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if self.io.settings.save_agent_actions:
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all_agent_actions = {name: agent.action_history for name, agent in self.game.agents.items()}
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self.io.write_agent_actions(agent_actions=all_agent_actions, episode=self.episode_counter)
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self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=copy.deepcopy(self.game_config))
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self.game.setup_for_episode(episode=self.episode_counter)
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self.episode_counter += 1
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state = self.game.get_sim_state()
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self.game.update_agents(state=state)
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next_obs = self._get_obs()
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info = {}
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return next_obs, info
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@property
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def action_space(self) -> gymnasium.Space:
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"""Return the action space of the environment."""
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return self.agent.action_manager.space
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@property
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def observation_space(self) -> gymnasium.Space:
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"""Return the observation space of the environment."""
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if self.agent.flatten_obs:
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return gymnasium.spaces.flatten_space(self.agent.observation_manager.space)
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else:
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return self.agent.observation_manager.space
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def _get_obs(self) -> ObsType:
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"""Return the current observation."""
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if self.agent.flatten_obs:
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unflat_space = self.agent.observation_manager.space
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unflat_obs = self.agent.observation_manager.current_observation
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return gymnasium.spaces.flatten(unflat_space, unflat_obs)
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else:
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return self.agent.observation_manager.current_observation
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def close(self):
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"""Close the simulation."""
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if self.io.settings.save_agent_actions:
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all_agent_actions = {name: agent.action_history for name, agent in self.game.agents.items()}
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self.io.write_agent_actions(agent_actions=all_agent_actions, episode=self.episode_counter)
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class PrimaiteRayEnv(gymnasium.Env):
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"""Ray wrapper that accepts a single `env_config` parameter in init function for compatibility with Ray."""
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def __init__(self, env_config: Dict) -> None:
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"""Initialise the environment.
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:param env_config: A dictionary containing the environment configuration.
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:type env_config: Dict
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"""
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self.env = PrimaiteGymEnv(game_config=env_config)
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self.env.episode_counter -= 1
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self.action_space = self.env.action_space
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self.observation_space = self.env.observation_space
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def reset(self, *, seed: int = None, options: dict = None) -> Tuple[ObsType, Dict]:
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"""Reset the environment."""
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return self.env.reset(seed=seed)
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def step(self, action: ActType) -> Tuple[ObsType, SupportsFloat, bool, bool, Dict]:
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"""Perform a step in the environment."""
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return self.env.step(action)
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def close(self):
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"""Close the simulation."""
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self.env.close()
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class PrimaiteRayMARLEnv(MultiAgentEnv):
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"""Ray Environment that inherits from MultiAgentEnv to allow training MARL systems."""
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def __init__(self, env_config: Dict) -> None:
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"""Initialise the environment.
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:param env_config: A dictionary containing the environment configuration. It must contain a single key, `game`
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which is the PrimaiteGame instance.
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:type env_config: Dict
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"""
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self.game_config: Dict = env_config
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"""PrimaiteGame definition. This can be changed between episodes to enable curriculum learning."""
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self.io = PrimaiteIO.from_config(env_config.get("io_settings"))
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"""Handles IO for the environment. This produces sys logs, agent logs, etc."""
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self.game: PrimaiteGame = PrimaiteGame.from_config(copy.deepcopy(self.game_config))
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"""Reference to the primaite game"""
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self._agent_ids = list(self.game.rl_agents.keys())
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"""Agent ids. This is a list of strings of agent names."""
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self.episode_counter: int = 0
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"""Current episode number."""
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self.terminateds = set()
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self.truncateds = set()
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self.observation_space = gymnasium.spaces.Dict(
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{
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name: gymnasium.spaces.flatten_space(agent.observation_manager.space)
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for name, agent in self.agents.items()
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}
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)
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self.action_space = gymnasium.spaces.Dict(
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{name: agent.action_manager.space for name, agent in self.agents.items()}
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)
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super().__init__()
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@property
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def agents(self) -> Dict[str, ProxyAgent]:
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"""Grab a fresh reference to the agents from this episode's game object."""
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return {name: self.game.rl_agents[name] for name in self._agent_ids}
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def reset(self, *, seed: int = None, options: dict = None) -> Tuple[ObsType, Dict]:
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"""Reset the environment."""
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if self.io.settings.save_agent_actions:
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all_agent_actions = {name: agent.action_history for name, agent in self.game.agents.items()}
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self.io.write_agent_actions(agent_actions=all_agent_actions, episode=self.episode_counter)
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self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=copy.deepcopy(self.game_config))
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self.game.setup_for_episode(episode=self.episode_counter)
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self.episode_counter += 1
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state = self.game.get_sim_state()
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self.game.update_agents(state)
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next_obs = self._get_obs()
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info = {}
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return next_obs, info
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def step(
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self, actions: Dict[str, ActType]
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) -> Tuple[Dict[str, ObsType], Dict[str, SupportsFloat], Dict[str, bool], Dict[str, bool], Dict]:
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"""Perform a step in the environment. Adherent to Ray MultiAgentEnv step API.
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:param actions: Dict of actions. The key is agent identifier and the value is a gymnasium action instance.
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:type actions: Dict[str, ActType]
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:return: Observations, rewards, terminateds, truncateds, and info. Each one is a dictionary keyed by agent
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identifier.
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:rtype: Tuple[Dict[str,ObsType], Dict[str, SupportsFloat], Dict[str,bool], Dict[str,bool], Dict]
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"""
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step = self.game.step_counter
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# 1. Perform actions
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for agent_name, action in actions.items():
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self.agents[agent_name].store_action(action)
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self.game.apply_agent_actions()
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# 2. Advance timestep
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self.game.advance_timestep()
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# 3. Get next observations
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state = self.game.get_sim_state()
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self.game.update_agents(state)
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next_obs = self._get_obs()
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# 4. Get rewards
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rewards = {name: agent.reward_function.current_reward for name, agent in self.agents.items()}
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terminateds = {name: False for name, _ in self.agents.items()}
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truncateds = {name: self.game.calculate_truncated() for name, _ in self.agents.items()}
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infos = {name: {} for name, _ in self.agents.items()}
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terminateds["__all__"] = len(self.terminateds) == len(self.agents)
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truncateds["__all__"] = self.game.calculate_truncated()
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if self.game.save_step_metadata:
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self._write_step_metadata_json(step, actions, state, rewards)
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return next_obs, rewards, terminateds, truncateds, infos
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def _write_step_metadata_json(self, step: int, actions: Dict, state: Dict, rewards: Dict):
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output_dir = SIM_OUTPUT.path / f"episode_{self.episode_counter}" / "step_metadata"
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output_dir.mkdir(parents=True, exist_ok=True)
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path = output_dir / f"step_{step}.json"
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data = {
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"episode": self.episode_counter,
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"step": step,
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"actions": {agent_name: int(action) for agent_name, action in actions.items()},
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"reward": rewards,
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"state": state,
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}
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with open(path, "w") as file:
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json.dump(data, file)
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def _get_obs(self) -> Dict[str, ObsType]:
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"""Return the current observation."""
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obs = {}
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for agent_name in self._agent_ids:
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agent = self.game.rl_agents[agent_name]
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unflat_space = agent.observation_manager.space
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unflat_obs = agent.observation_manager.current_observation
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obs[agent_name] = gymnasium.spaces.flatten(unflat_space, unflat_obs)
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return obs
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def close(self):
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"""Close the simulation."""
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if self.io.settings.save_agent_actions:
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all_agent_actions = {name: agent.action_history for name, agent in self.game.agents.items()}
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self.io.write_agent_actions(agent_actions=all_agent_actions, episode=self.episode_counter)
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