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"""Wrapper to convert a ChatArena environment into a PettingZoo compatible environment."""
from __future__ import annotations
import functools
from typing import Any, Dict, Optional
import pettingzoo
from gymnasium import spaces
from pettingzoo.utils.env import AgentID, ObsType
import chatarena
from chatarena.arena import Arena
import string
CHAR_SET = string.printable
class PettingZooCompatibilityV0(pettingzoo.AECEnv):
"""This compatibility wrapper converts a ChatArena environment into a PettingZoo environment.
ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.
"""
metadata = {
"render_modes": ["human"],
"name": "PettingZooCompatibilityV0",
"is_parallelizable": False,
}
def __init__(
self,
env: chatarena.arena.Arena | None = None,
arena_name: str | None = None,
string_observation: bool | None = True,
max_turns: int | None = 25,
render_mode: str | None = None,
):
"""Wrapper to convert a ChatArena environment into a PettingZoo environment.
Args:
env (chatarena.arena.Arena): chatarena arena to wrap
arena_name (Optional[str]): chatarena environment to load from file (e.g., "examples/chameleon.json")
max_turns (Optional[int]): maximum number of turns before environment truncates
render_mode (Optional[str]): rendering mode
"""
super().__init__()
if env is not None:
self._env = env
elif arena_name is not None:
self._env = Arena.from_config(arena_name)
else:
raise ValueError("Arena not specified, please us env or arena_name arguments.")
self._env.reset() # this resets the underlying arena as well as each player
self.possible_agents = list(self._env.name_to_player.keys())
self.name_to_player_mapping = self._env.name_to_player
self.string_observation = string_observation
self.max_turns = max_turns
self.render_mode = render_mode
self.terminations = {}
self.truncations = {}
self.rewards = {}
self.infos = {a: {} for a in self.possible_agents}
@functools.lru_cache(maxsize=None)
def observation_space(self, agent: AgentID):
"""observation_space.
We get the observation space from the underlying environment.
Args:
agent (AgentID): agent
"""
# TODO: finalize obs space (dicts with agent name may not be necessary)
observation_space = spaces.Dict(
{
agent: spaces.Text(max_length=256, min_length=0, charset=CHAR_SET)
for agent in self.possible_agents
}
)
return observation_space
@functools.lru_cache(maxsize=None)
def action_space(self, agent: AgentID):
"""action_space.
Get the action space from the underlying environment.
Args:
agent (AgentID): agent
Returns:
space
"""
# TODO: finalize action space (this enables agents to send messages to specific other players)
action_space = spaces.Dict(
{
agent: spaces.Text(max_length=256, min_length=0, charset=CHAR_SET)
for agent in self.possible_agents
}
)
return action_space
def render(self):
"""render.
Print the current game state.
"""
if not hasattr(self, "initial_timestep"):
raise UserWarning(
"You must reset the environment using reset() before calling render()."
)
self._env.environment.print()
pass
def observe(self, agent: AgentID) -> ObsType:
"""observe.
Args:
agent (AgentID): agent (e.g., "Player 1")
Returns:
observation
"""
messages = self._env.environment.get_observation(agent) # this will only return the messages this agent can see
if len(messages) > 0:
self.current_turn = messages[-1].turn
else:
self.current_turn = 0
new_messages = [m for m in messages if
m.turn == self.current_turn] # we only send the current timestep messages
# string observation
if self.string_observation == True:
observation = ""
for m in new_messages:
observation += f"{m.agent_name}: {m.content}"
# dict observation
else:
observation = {m.agent_name: m.content for m in new_messages}
self.infos[agent]["obs_dict"] = {m.agent_name: m.content for m in new_messages}
return observation
def close(self):
"""close."""
pass
def _unravel_timestep(self, timestep: chatarena.arena.TimeStep):
# get observation
messages = timestep.observation
if len(messages) > 0:
self.current_turn = messages[-1].turn
else:
self.current_turn = 0
new_messages = [m for m in messages if
m.turn == self.current_turn] # we only send the current timestep messages
# string observation
if self.string_observation == True:
observation = ""
for m in new_messages:
observation += f"{m.agent_name}: {m.content}"
# dict observation
else:
observation = {m.agent_name: m.content for m in new_messages}
# get rewards
rewards = timestep.reward
# get termination
termination = timestep.terminal
# get truncation
truncation = self.current_turn > self.max_turns
# get info
player_idx = self.possible_agents.index(self.agent_selection)
player_obj = self._env.players[player_idx]
info = {"turn": self.current_turn, "global_prompt": player_obj.global_prompt,
"agent_desc": player_obj.role_desc}
return observation, rewards, termination, truncation, info
def reset(
self,
return_info: bool | None = False,
seed: int | None = None,
options: dict | None = None,
):
"""reset.
Args:
seed (Optional[int]): seed
options (Optional[Dict]): options
"""
if seed is not None:
print("WARNING: seeding is not supported for LLMs.")
# reset the chat arena environment
self.initial_timestep = self._env.reset()
self.turn = 0
# get the first player
self.agent_selection = self._env.environment.get_next_player()
observation, reward, termination, truncation, info = self._unravel_timestep(self.initial_timestep)
agent = self.agent_selection
self.rewards = reward
self.terminations[agent] = termination
self.truncations[agent] = truncation
self.infos[agent] = info
# all agents
self.agents = self.possible_agents[:]
# boilerplate stuff
self._cumulative_rewards = {a: 0 for a in self.agents}
self.rewards = self.initial_timestep.reward
self.terminations = {a: False for a in self.agents}
self.truncations = {a: False for a in self.agents}
def step(self, action: str):
"""Steps.
Steps the agent with an action.
Args:
action (str): action
"""
if (
self.terminations[self.agent_selection]
or self.truncations[self.agent_selection]
):
return self._was_dead_step(action)
agent = self.agent_selection
timestep = self._env.environment.step(player_name=agent, action=action)
observation, reward, termination, truncation, info = self._unravel_timestep(timestep)
self.rewards = reward
self.terminations[agent] = termination
self.truncations[agent] = truncation
self.infos[agent] = info
self._cumulative_rewards[agent] = 0
self.agent_selection = self._env.environment.get_next_player()
self._accumulate_rewards()
if self.render_mode == "human":
self.render()
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