Rényi Regularised Reinforcement Learning

Edward James Young, Yashar Ahmadian

Keywords: Reinforcement Learning, Variational Inference, Rényi divergence

TL;DR: We generalise soft-actor critic using Rényi divergences.

Abstract:

Entropy regularisation has proven effective in reinforcement learning (RL) for encouraging exploration. Recent work demonstrating the equivalence between entropy regularised RL and approximate probabilistic inference suggests the potential for improving existing methods by generalising the inference procedure. We develop the Rényi regularised RL framework by using Rényi variational inference to learn a stochastic policy. We present theoretical results for policy evaluation and improvement within this new framework. Additionally, we propose two novel algorithms, $\alpha$-SAC and $\alpha$-SQL, for large-scale RL tasks. We show that these algorithms attain higher returns on games from the Atari suite relative to an entropy-regularised benchmark, SAC-Discrete.

Primary Area: reinforcement learning

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Submission Number: 10959