Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration

• URL: http://arxiv.org/abs/2006.01419v2
• Date: Wed, 9 Jun 2021 03:05:50 GMT
• Title: Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
• Authors: Seungyul Han, Youngchul Sung
• Abstract summary: Exploiting the sample distribution obtainable from the replay buffer, the proposed sample-aware entropy regularization maximizes the entropy of the weighted sum of the policy action distribution and the sample action distribution from the replay buffer for sample-efficient exploration. A practical algorithm named diversity actor-critic (DAC) is developed by applying policy iteration to the objective function with the proposed sample-aware entropy regularization.
• Score: 22.539300644593936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, sample-aware policy entropy regularization is proposed to enhance the conventional policy entropy regularization for better exploration. Exploiting the sample distribution obtainable from the replay buffer, the proposed sample-aware entropy regularization maximizes the entropy of the weighted sum of the policy action distribution and the sample action distribution from the replay buffer for sample-efficient exploration. A practical algorithm named diversity actor-critic (DAC) is developed by applying policy iteration to the objective function with the proposed sample-aware entropy regularization. Numerical results show that DAC significantly outperforms existing recent algorithms for reinforcement learning.

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