Implicit Policy for Reinforcement Learning

Yunhao Tang; Shipra Agrawal

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arXiv · 1806.06798

Implicit Policy for Reinforcement Learning

Yunhao Tang·Shipra Agrawal

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Abstract

We introduce Implicit Policy, a general class of expressive policies that can flexibly represent complex action distributions in reinforcement learning, with efficient algorithms to compute entropy regularized policy gradients. We empirically show that, despite its simplicity in implementation, entropy regularization combined with a rich policy class can attain desirable properties displayed under maximum entropy reinforcement learning framework, such as robustness and multi-modality.

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Yunhao Tang, Shipra Agrawal. 2018-06-10. Implicit Policy for Reinforcement Learning. https://arxiv.org/abs/1806.06798

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