Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang


Many recent reinforcement learning (RL) methods learn stochastic policies with entropy regularization for exploration and robustness. However, in continuous action spaces, integrating entropy regularization with expressive policies is challenging and usually requires complex inference procedures. To tackle this problem, we propose a novel regularization method that is compatible with a broad range of expressive policy architectures. An appealing feature is that, the estimation of our regularization terms is simple and efficient even when the policy distributions are unknown. We show that our approach can effectively promote the exploration in continuous action spaces. Based on our regularization, we propose an off-policy actor-critic algorithm. Experiments demonstrate that the proposed algorithm outperforms state-of-the-art regularized RL methods in continuous control tasks.