Weighted Entropy Modification for Soft Actor-Critic

• URL: http://arxiv.org/abs/2011.09083v1
• Date: Wed, 18 Nov 2020 04:36:03 GMT
• Title: Weighted Entropy Modification for Soft Actor-Critic
• Authors: Yizhou Zhao, Song-Chun Zhu
• Abstract summary: We generalize the existing principle of the maximum Shannon entropy in reinforcement learning (RL) to weighted entropy by characterizing the state-action pairs with some qualitative weights. We propose an algorithm motivated for self-balancing exploration with the introduced weight function, which leads to state-of-the-art performance on Mujoco tasks despite its simplicity in implementation.
• Score: 95.37322316673617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We generalize the existing principle of the maximum Shannon entropy in reinforcement learning (RL) to weighted entropy by characterizing the state-action pairs with some qualitative weights, which can be connected with prior knowledge, experience replay, and evolution process of the policy. We propose an algorithm motivated for self-balancing exploration with the introduced weight function, which leads to state-of-the-art performance on Mujoco tasks despite its simplicity in implementation.

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