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"""Simple example of running the Umshini deception environment locally using LangChain agents. This can be used to test agents before participating in a tournament."""
from langchain.schema import SystemMessage
from chatarena.environments.umshini.agents import SimpleDeceptionDefender, SimpleDeceptionAttacker, \
PresidentDeceptionAttacker, DeceptionMultiRoleAgent
from chatarena.environments.umshini import PettingZooCompatibilityV0
env = PettingZooCompatibilityV0(env_name="deception", restricted_action="open the door", render_mode="human")
env.reset()
# Note: these agents are only intended to be used as starting examples, and tend to suffer from hallucination if the game continues for many rounds
# However, unlike the ChatArena agents, they can correct handle swapping of roles deterministically using string parsing
langchain_agents = {env.possible_agents[0]: DeceptionMultiRoleAgent(PresidentDeceptionAttacker,
SimpleDeceptionDefender),
env.possible_agents[1]: DeceptionMultiRoleAgent(SimpleDeceptionAttacker, SimpleDeceptionDefender)}
for agent in env.agent_iter():
observation, reward, termination, truncation, info = env.last()
if termination or truncation:
break
# Optional: Use extra information encoded in info dict
messages = info.get("new_messages")
player_name = info.get("player_name")
restricted_action = info.get("restricted_action")
try:
response = langchain_agents[agent].get_response([SystemMessage(content=observation)], restricted_action, player_name)
except Exception as e:
response = str(e).removeprefix("Could not parse LLM output: `").removesuffix("`")
env.step(response)
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