The SmartPlay repository is a benchmark and methodology for evaluating the abilities of large language models (LLMs) as agents. It consists of six different games, including Rock-Paper-Scissors, Tower of Hanoi, and Minecraft, each featuring a unique setting that provides up to 20 evaluation settings and infinite environment variations. The games in SmartPlay challenge a subset of nine important capabilities of an intelligent LLM agent, including reasoning with object dependencies, planning ahead, spatial reasoning, learning from history, and understanding randomness. The distinction between the set of capabilities each game tests allows for the analysis of each capability separately. SmartPlay serves as a rigorous testing ground for evaluating the overall performance of LLM agents and as a roadmap for identifying gaps in current methodologies.
Currently included games are:
For more information, please refer to the paper.
First consider setting up a conda environment by running
conda env create --name SmartPlay --file environment.yml
SmartPlay requires MineDojo, please follow the official documentation to install MineDojo first before proceeding.
Then run
pip install -e .
For completeness, we also provide conda environment scripts and requirements.txt in the root directory.
SmartPlay is designed to be used with OpenAI Gym:
import gym
import smartplay
env = gym.make("smartplay:{}-v0".format(env_name))
_, info = env.reset()
while True:
action = info['action_space'].sample()
_, reward, done, info = env.step(action)
manual, obs, history, score = info['manual'], info['obs'], info['history'], info['score']
if not done:
completion=0
else:
completion=info['completed']
Full example to use the benchmark are provided in:
examples/experiments.py
To see all environments available in the SmartPlay benchmark, run the following code:
import smartplay
print(smartplay.env_list)
See MineDojo Documentation for a description of the MineDojo Creative tasks.
@inproceedings{wu2024smartplay,
title={SmartPlay: A Benchmark for LLMs as Intelligent Agents},
author={Wu, Yue and Tang, Xuan and Mitchell, Tom and Li, Yuanzhi},
booktitle={ICLR},
year={2024}
}
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