B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning
This addresses offline multi-agent RL, a domain-specific problem with incremental improvements over existing methods.
The paper tackles the problem of overestimation in offline multi-agent reinforcement learning by proposing B3C, a method that combines behavior cloning regularization with critic clipping and non-linear value factorization, which outperforms state-of-the-art algorithms on various benchmarks.
Overestimation arising from selecting unseen actions during policy evaluation is a major challenge in offline reinforcement learning (RL). A minimalist approach in the single-agent setting -- adding behavior cloning (BC) regularization to existing online RL algorithms -- has been shown to be effective; however, this approach is understudied in multi-agent settings. In particular, overestimation becomes worse in multi-agent settings due to the presence of multiple actions, resulting in the BC regularization-based approach easily suffering from either over-regularization or critic divergence. To address this, we propose a simple yet effective method, Behavior Cloning regularization with Critic Clipping (B3C), which clips the target critic value in policy evaluation based on the maximum return in the dataset and pushes the limit of the weight on the RL objective over BC regularization, thereby improving performance. Additionally, we leverage existing value factorization techniques, particularly non-linear factorization, which is understudied in offline settings. Integrated with non-linear value factorization, B3C outperforms state-of-the-art algorithms on various offline multi-agent benchmarks.