Poster

Taylor Expansion Policy Optimization

Yunhao Tang ⋅ Michal Valko ⋅ Remi Munos

Keywords: Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning - Deep RL

2020 Poster

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 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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