Taylor Expansion Policy Optimization

• URL: http://arxiv.org/abs/2003.06259v1
• Date: Fri, 13 Mar 2020 13:14:19 GMT
• Title: Taylor Expansion Policy Optimization
• Authors: Yunhao Tang, Michal Valko, R\'emi Munos
• Abstract summary: We show that Taylor expansions intimately relate to off-policy evaluation. This new formulation entails modifications which improve the performance of several state-of-the-art distributed algorithms.
• Score: 55.204663986972875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we investigate the application of Taylor expansions in reinforcement learning. In particular, we propose Taylor expansion policy optimization, a policy optimization formalism that generalizes prior work (e.g., TRPO) as a first-order special case. We also show that Taylor expansions intimately relate to off-policy evaluation. Finally, we show that this new formulation entails modifications which improve the performance of several state-of-the-art distributed algorithms.

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