Coordination Failure in Cooperative Offline MARL

TL;DR

This paper investigates coordination failure in offline multi-agent reinforcement learning, revealing a failure mode in joint action policies and proposing a sample prioritization method to improve coordination, supported by theoretical analysis and experiments.

Contribution

It identifies a previously overlooked failure mode in offline MARL and introduces a sample prioritization approach to mitigate coordination failure, grounded in game-theoretic analysis.

Findings

Identified a catastrophic coordination failure mode in BRUD-based algorithms.

Proposed a sample prioritization method based on joint-action similarity.

Demonstrated the effectiveness of the approach through detailed experiments.

Abstract

Offline multi-agent reinforcement learning (MARL) leverages static datasets of experience to learn optimal multi-agent control. However, learning from static data presents several unique challenges to overcome. In this paper, we focus on coordination failure and investigate the role of joint actions in multi-agent policy gradients with offline data, focusing on a common setting we refer to as the 'Best Response Under Data' (BRUD) approach. By using two-player polynomial games as an analytical tool, we demonstrate a simple yet overlooked failure mode of BRUD-based algorithms, which can lead to catastrophic coordination failure in the offline setting. Building on these insights, we propose an approach to mitigate such failure, by prioritising samples from the dataset based on joint-action similarity during policy learning and demonstrate its effectiveness in detailed experiments. More generally, however, we argue that prioritised dataset sampling is a promising area for innovation in offline MARL that can be combined with other effective approaches such as critic and policy regularisation. Importantly, our work shows how insights drawn from simplified, tractable games can lead to useful, theoretically grounded insights that transfer to more complex contexts. A core dimension of offering is an interactive notebook, from which almost all of our results can be reproduced, in a browser.