Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning

• URL: http://arxiv.org/abs/2005.08844v2
• Date: Fri, 5 Jun 2020 17:21:40 GMT
• Title: Entropy-Augmented Entropy-Regularized Reinforcement Learning and a Continuous Path from Policy Gradient to Q-Learning
• Authors: Donghoon Lee
• Abstract summary: entropy augmentation is reformulated and leads to a motivation to introduce an additional entropy term to the objective function. It results in a policy which monotonically improves while interpolating from the current policy to the softmax greedy policy.
• Score: 5.185562073975834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entropy augmented to reward is known to soften the greedy argmax policy to softmax policy. Entropy augmentation is reformulated and leads to a motivation to introduce an additional entropy term to the objective function in the form of KL-divergence to regularize optimization process. It results in a policy which monotonically improves while interpolating from the current policy to the softmax greedy policy. This policy is used to build a continuously parameterized algorithm which optimize policy and Q-function simultaneously and whose extreme limits correspond to policy gradient and Q-learning, respectively. Experiments show that there can be a performance gain using an intermediate algorithm.

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