Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jimmy Ba

Keywords: goal-conditioned reinforcement learning, learning invariants, representation learning, domain generalization, block mdp, deep reinforcement learning

Abstract: Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes after deployment in the real world. These changes are often spurious and unrelated to the underlying problem, such as background shifts for visual input agents. Unfortunately, deep RL policies are usually sensitive to these changes and fail to act robustly against them. This resembles the problem of domain generalization in supervised learning. In this work, we study this problem for goal-conditioned RL agents. We propose a theoretical framework in the Block MDP setting that characterizes the generalizability of goal-conditioned policies to new environments. Under this framework, we develop a practical method PA-SkewFit that enhances domain generalization. The empirical evaluation shows that our goal-conditioned RL agent can perform well in various unseen test environments, improving by 50\% over baselines.

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TL;DR: We propose a new method for learning domain invariant policies for Goal-conditioned RL agents.

Code: https://github.com/YeeCY/PASF

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2110.14248/code)