This is the official code of AvalonBench and the Avalon agent Strategist. The corresponding papers are AvalonBench: Evaluating LLMs Playing the Game of Avalon and Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search.
Based on AgentBench, we support Multi-Agent play of The Resistance: Avalon, a popular board game that requires the ability of deductive reasoning, coordinate and collaborate, and skill of deception.
Read the instructions below for how to run AvalonBench!
In this work, we propose Strategist, which utilizes LLMs to acquire new skills for playing multi-agent games through a self-improvement process. Our method gathers quality feedback through self-play simulations with Monte Carlo tree search and LLM-based reflection, which can then be used to learn high-level strategic skills such as how to evaluate states that guide the low-level execution.
You can learn how to play with Strategist on AvalonBench at here, and the code/usage for bi-level tree search of Strategist can be found at the strategist folder.
- [2024/08] 🔥Try out our new agent, Strategist, by using the
avalon-dev-single-discussconfig, and find more details at Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search! - [2024/07] Our new agent
SearchlightLLMAgentWithDiscussionis available atsrc/server/tasks/avalon/agents/search_agent.py. The academic paper will be coming soon. - [2023/11] 🎶Multi-LLM setting with AgentBench v0.2 is ready to roll! Details of the multi-agent submodule can be found here
- [2023/11]
♠️ We've added a new game called GOPS (Game of Pure Strategy [Wiki]). For more details of the code, please refer to here. - [2023/10] 🤖We've updated our code based on AgentBench v0.2. For the older version, please visit here.
GPT-3.5-turbo🤖 playing against rule-based bots in AvalonBench
single_llm_gpt3.5.mp4
GPT-4-turbo🤖 playing against rule-based bots in AvalonBench
single_llm_gpt_4.mp4
GPT-3.5-turbos🤖 playing against each other
multiplayer.mp4
Here are the results of LLMs playing against baseline bots.
We also let LLMs playing against each other. Evil has an 8:2 advantage over Good, which is similar to the stats of rookie human players! Here are also some examples of discussion under this setting.
Install the dependencies.
conda create -n avalonbench python=3.9
conda activate avalonbench
pip install -r requirements.txtYou need to fill your OPENAI API KEY in configs/agents/openai-chat first. Please replace <OPENAI_API_KEY> in Bearer <OPENAI_API_KEY> with your key.
Start the game (3 is the number of workers)
python -m src.start_task -a --start avalon-dev-single 3Open a new terminal and start the assigner
python -m src.assigner --config ./configs/assignments/test_avalon.yaml- You can modify the file
configs/tasks/avalon.yamlto configure the agent list. A config file looks like this:
default:
module: "src.server.tasks.avalon.AvalonBench"
parameters:
num_players: 5
discussion: False
avalon-dev-naive:
parameters:
name: "AvalonBench-dev-naive"
data_file: "data/avalon/dev.json"
agent_list: ["naive", "naive", "naive", "naive", "naive"]
avalon-dev-single:
parameters:
name: "AvalonBench-dev-single"
data_file: "data/avalon/dev.json"
agent_list: ["llm", "naive", "naive", "naive", "naive"]where naive stands for the naive bots. Agents will play the roles with the same index in the data file (see following).
Note: There should only be one "llm" in the `agent_list`
- You can also add data in
data/avalon/dev.json(Note: Currently we only support the 5-player game setting, which includes 1 Merlin, 2 Servants, 1 Minion and 1 Assassin). A data item looks like this:
{
"num_players": 5,
"quest_leader": 0,
"role_names": ["Assassin", "Servant", "Servant", "Merlin", "Minion"]
}where quest_leader is the id of the initial quest leader in this game. You can change the game setup by altering quest_leader with number from 0 to 4, and by permuting role_names.
You can also start a naive experiment using:
python -m src.start_task -a --start avalon-dev-naive 3where all the agents are naive bots. For details of the naive strategies, please refer to the paper.
You can also start a Multi-LLM experiment using:
python -m src.start_task -a --start avalon-dev-multi 3where all the agents will be Large Language Models.
Our agent, Strategist, is also available in this repo. You can start the experiment using:
# Strategist playing against naive baselines
python -m src.start_task -a --start avalon-dev-single-search 1All the prompts are maintained in src/server/tasks/avalon/prompt.py. You can find the respective prompts used in src/server/tasks/avalon/agents/llm_with_discussion.py and src/server/tasks/avalon/wrapper.py.
We also provide our engines along with examples of usage for developers in avalonbench_dev.
You can import and use the game engine by running
from engine import AvalonGameEnvironment, AvalonConfigFirst input your game configurations into AvalonBasicConfig, then create an AvalonGameEnvironment based on that.
For an example of how to use the game engine, see avalonbench_dev/avalon/test_engine.py
@inproceedings{
light2023from,
title={AvalonBench: Evaluating {LLM}s Playing the Game of Avalon},
author={Jonathan Light and Min Cai and Sheng Shen and Ziniu Hu},
booktitle={NeurIPS 2023 Foundation Models for Decision Making Workshop},
year={2023},
url={https://openreview.net/forum?id=ltUrSryS0K}
}
