Reinforcement Learning via Fenchel-Rockafellar Duality

• URL: http://arxiv.org/abs/2001.01866v2
• Date: Thu, 9 Jan 2020 19:08:09 GMT
• Title: Reinforcement Learning via Fenchel-Rockafellar Duality
• Authors: Ofir Nachum, Bo Dai
• Abstract summary: We review basic concepts of convex duality, focusing on the very general and supremely useful Fenchel-Rockafellar duality. We summarize how this duality may be applied to a variety of reinforcement learning settings, including policy evaluation or optimization, online or offline learning, and discounted or undiscounted rewards.
• Score: 97.86417365464068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We review basic concepts of convex duality, focusing on the very general and supremely useful Fenchel-Rockafellar duality. We summarize how this duality may be applied to a variety of reinforcement learning (RL) settings, including policy evaluation or optimization, online or offline learning, and discounted or undiscounted rewards. The derivations yield a number of intriguing results, including the ability to perform policy evaluation and on-policy policy gradient with behavior-agnostic offline data and methods to learn a policy via max-likelihood optimization. Although many of these results have appeared previously in various forms, we provide a unified treatment and perspective on these results, which we hope will enable researchers to better use and apply the tools of convex duality to make further progress in RL.

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