Change agents list in game object to dictionary

This commit is contained in:
Marek Wolan
2024-02-26 10:26:28 +00:00
parent 63c9a36c30
commit e5982c4599
8 changed files with 331 additions and 225 deletions

View File

@@ -1,5 +1,5 @@
import json
from typing import Any, Dict, Final, Optional, SupportsFloat, Tuple
from typing import Any, Dict, Optional, SupportsFloat, Tuple
import gymnasium
from gymnasium.core import ActType, ObsType
@@ -25,12 +25,17 @@ class PrimaiteGymEnv(gymnasium.Env):
"""PrimaiteGame definition. This can be changed between episodes to enable curriculum learning."""
self.game: PrimaiteGame = PrimaiteGame.from_config(self.game_config)
"""Current game."""
self.agent: ProxyAgent = self.game.rl_agents[0]
"""The agent within the game that is controlled by the RL algorithm."""
self._agent_name = next(iter(self.game.rl_agents))
"""Name of the RL agent. Since there should only be one RL agent we can just pull the first and only key."""
self.episode_counter: int = 0
"""Current episode number."""
@property
def agent(self) -> ProxyAgent:
"""Grab a fresh reference to the agent object because it will be reinstantiated each episode."""
return self.game.rl_agents[self._agent_name]
def step(self, action: ActType) -> Tuple[ObsType, SupportsFloat, bool, bool, Dict[str, Any]]:
"""Perform a step in the environment."""
# make ProxyAgent store the action chosen my the RL policy
@@ -71,11 +76,10 @@ class PrimaiteGymEnv(gymnasium.Env):
"""Reset the environment."""
print(
f"Resetting environment, episode {self.episode_counter}, "
f"avg. reward: {self.game.rl_agents[0].reward_function.total_reward}"
f"avg. reward: {self.agent.reward_function.total_reward}"
)
self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=self.game_config)
self.game.setup_for_episode(episode=self.episode_counter)
self.agent = self.game.rl_agents[0]
self.episode_counter += 1
state = self.game.get_sim_state()
self.game.update_agents(state)
@@ -112,11 +116,10 @@ class PrimaiteRayEnv(gymnasium.Env):
def __init__(self, env_config: Dict) -> None:
"""Initialise the environment.
:param env_config: A dictionary containing the environment configuration. It must contain a single key, `game`
which is the PrimaiteGame instance.
:type env_config: Dict[str, PrimaiteGame]
:param env_config: A dictionary containing the environment configuration.
:type env_config: Dict
"""
self.env = PrimaiteGymEnv(game=PrimaiteGame.from_config(env_config["cfg"]))
self.env = PrimaiteGymEnv(game_config=env_config)
self.env.episode_counter -= 1
self.action_space = self.env.action_space
self.observation_space = self.env.observation_space
@@ -138,13 +141,16 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
:param env_config: A dictionary containing the environment configuration. It must contain a single key, `game`
which is the PrimaiteGame instance.
:type env_config: Dict[str, PrimaiteGame]
:type env_config: Dict
"""
self.game: PrimaiteGame = PrimaiteGame.from_config(env_config["cfg"])
self.game_config: Dict = env_config
"""PrimaiteGame definition. This can be changed between episodes to enable curriculum learning."""
self.game: PrimaiteGame = PrimaiteGame.from_config(self.game_config)
"""Reference to the primaite game"""
self.agents: Final[Dict[str, ProxyAgent]] = {agent.agent_name: agent for agent in self.game.rl_agents}
"""List of all possible agents in the environment. This list should not change!"""
self._agent_ids = list(self.agents.keys())
self._agent_ids = list(self.game.rl_agents.keys())
"""Agent ids. This is a list of strings of agent names."""
self.episode_counter: int = 0
"""Current episode number."""
self.terminateds = set()
self.truncateds = set()
@@ -159,9 +165,16 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
)
super().__init__()
@property
def agents(self) -> Dict[str, ProxyAgent]:
"""Grab a fresh reference to the agents from this episode's game object."""
return {name: self.game.rl_agents[name] for name in self._agent_ids}
def reset(self, *, seed: int = None, options: dict = None) -> Tuple[ObsType, Dict]:
"""Reset the environment."""
self.game.reset()
self.game: PrimaiteGame = PrimaiteGame.from_config(cfg=self.game_config)
self.game.setup_for_episode(episode=self.episode_counter)
self.episode_counter += 1
state = self.game.get_sim_state()
self.game.update_agents(state)
next_obs = self._get_obs()
@@ -182,7 +195,7 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
# 1. Perform actions
for agent_name, action in actions.items():
self.agents[agent_name].store_action(action)
agent_actions = self.game.apply_agent_actions()
self.game.apply_agent_actions()
# 2. Advance timestep
self.game.advance_timestep()
@@ -196,7 +209,7 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
rewards = {name: agent.reward_function.current_reward for name, agent in self.agents.items()}
terminateds = {name: False for name, _ in self.agents.items()}
truncateds = {name: self.game.calculate_truncated() for name, _ in self.agents.items()}
infos = {"agent_actions": agent_actions}
infos = {name: {} for name, _ in self.agents.items()}
terminateds["__all__"] = len(self.terminateds) == len(self.agents)
truncateds["__all__"] = self.game.calculate_truncated()
if self.game.save_step_metadata:
@@ -222,8 +235,9 @@ class PrimaiteRayMARLEnv(MultiAgentEnv):
def _get_obs(self) -> Dict[str, ObsType]:
"""Return the current observation."""
obs = {}
for name, agent in self.agents.items():
for agent_name in self._agent_ids:
agent = self.game.rl_agents[agent_name]
unflat_space = agent.observation_manager.space
unflat_obs = agent.observation_manager.current_observation
obs[name] = gymnasium.spaces.flatten(unflat_space, unflat_obs)
obs[agent_name] = gymnasium.spaces.flatten(unflat_space, unflat_obs)
return obs