An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning

• URL: http://arxiv.org/abs/2106.00707v1
• Date: Tue, 1 Jun 2021 18:04:19 GMT
• Title: An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning
• Authors: Changnan Xiao, Haosen Shi, Jiajun Fan, Shihong Deng
• Abstract summary: Policy-based reinforcement learning methods suffer from the policy collapse problem. We propose an entropy regularization free mechanism that is designed for policy-based methods.
• Score: 1.4566990078034239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Policy-based reinforcement learning methods suffer from the policy collapse problem. We find valued-based reinforcement learning methods with {\epsilon}-greedy mechanism are capable of enjoying three characteristics, Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off, which help value-based methods avoid the policy collapse problem. However, there does not exist a parallel mechanism for policy-based methods that achieves all three characteristics. In this paper, we propose an entropy regularization free mechanism that is designed for policy-based methods, which achieves Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off. Our experiments show that our mechanism is super sample-efficient for policy-based methods and boosts a policy-based baseline to a new State-Of-The-Art on Arcade Learning Environment.

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