Believe What You See: Implicit Constraint Approach for Offline
  Multi-Agent Reinforcement Learning
  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.
