Maximum Total Correlation Reinforcement Learning

Bang You, Puze Liu, Huaping Liu, Jan Peters, Oleg Arenz

Keywords: Reinforcement Learning, Total Correlation

TL;DR: Maximizing trajectory total correlation for learning simple and robust RL policies.

Abstract: Simplicity is a powerful inductive bias. In reinforcement learning, regularization is used for simpler policies, data augmentation for simpler representations, and sparse reward functions for simpler objectives, all that, with the underlying motivation to increase generalizability and robustness by focusing on the essentials. Supplementary to these techniques, we investigate how to promote simple behavior throughout the duration of the episode. To that end, we introduce a modification of the reinforcement learning problem, that additionally maximizes the total correlation within the induced trajectories. We propose a practical algorithm that optimizes all models, including policy and state representation, based on a lower bound approximation. In simulated robot locomotion environments, our method naturally generates policies that induce periodic and compressible trajectories, and that exhibit superior robustness to noise and changes in dynamics compared to baseline methods, while also improving performance in the original tasks.

Primary Area: reinforcement learning

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Submission Number: 6703