Multi-Task Offline Reinforcement Learning

Abstract

This paper examines the generalization capabilities of the Soft Actor-Critic (SAC) algorithm when combined with Behavioral Cloning (BC) in a MiniGrid Four-Room Environment. Reinforcement learning (RL), particularly offline, is important for tasks where interactions with the environments are risky or costly, and this research focuses on multi-task environments where generalizability to new tasks is crucial. Our findings indicate that SAC+BC can achieve generalization performance close to BC. Notably, while BC shows robustness across various dataset characteristics (quality, diversity, size), SAC alone struggles without integrating BC, highlighting the enhancement in generalization brought by this hybrid approach. Furthermore, an increased data size only enhances generalizability when introducing greater diversity. However, these results are constrained by hardware limitations, suggesting that further hyperparameter optimization and using more seeds could validate and possibly enhance our findings, demonstrating that SAC+BC is even more effective
than shown. The implementation details and the source code for this study are available on GitHub at https://github.com/AxelGeist/multi-task-offlinereinforcement-learning.