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arXiv · 2003.06259

Taylor Expansion Policy Optimization

Yunhao Tang·Michal Valko·Rémi Munos
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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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Yunhao Tang, Michal Valko, Rémi Munos. 2020-03-13. Taylor Expansion Policy Optimization. https://arxiv.org/abs/2003.06259

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