Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Filippos Christianos, Lukas Schäfer, Stefano Albrecht
Abstract
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents. Our proposed algorithm, called shared Experience Actor-Critic(SEAC), applies experience sharing in an actor-critic framework by combining the gradients of different agents. We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms several baselines and state-of-the-art algorithms by learning in fewer steps and converging to higher returns. In some harder environments, experience sharing makes the difference between learning to solve the task and not learning at all.
BibTeX
@inproceedings{NEURIPS2020_7967cc8e,
author = {Christianos, Filippos and Sch\"{a}fer, Lukas and Albrecht, Stefano},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {10707--10717},
publisher = {Curran Associates, Inc.},
title = {Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/7967cc8e3ab559e68cc944c44b1cf3e8-Paper.pdf},
volume = {33},
year = {2020}
}