title: Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
abstract: Learning from datasets without interaction with environments (Offline Learning) is an essential step to apply Reinforcement Learning (RL) algorithms in real-world scenarios.	However, compared with the single-agent counterpart, offline multi-agent RL introduces more agents with the larger state and action space, which is more challenging but attracts little attention. We demonstrate current offline RL algorithms are ineffective in multi-agent systems due to the accumulated extrapolation error. In this paper, we propose a novel offline RL algorithm, named Implicit Constraint Q-learning (ICQ), which effectively alleviates the extrapolation error by only trusting the state-action pairs given in the dataset for value estimation.  Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint.  Experimental results demonstrate that the extrapolation error is successfully controlled within a reasonable range and insensitive to the number of agents. We further show that ICQ achieves the state-of-the-art performance in the challenging multi-agent offline tasks (StarCraft II). Our code is public online at https://github.com/YiqinYang/ICQ.
authors: Yiqin Yang Xiaoteng Ma Chenghao Li Zewu Zheng Qiyuan Zhang Gao Huang Jun Yang Qianchuan Zhao
hIndex: Qianchuan Zhao 11