Skip to Main content Skip to Navigation
Conference papers

Taylor expansion policy optimization

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [3 references]  Display  Hide  Download
Contributor : Michal Valko Connect in order to contact the contributor
Submitted on : Monday, March 16, 2020 - 9:09:45 PM
Last modification on : Friday, December 18, 2020 - 6:46:06 PM
Long-term archiving on: : Wednesday, June 17, 2020 - 3:17:43 PM


Files produced by the author(s)


  • HAL Id : hal-02509561, version 1


Yunhao Tang, Michal Valko, Rémi Munos. Taylor expansion policy optimization. International Conference on Machine Learning, 2020, Vienna, Austria. ⟨hal-02509561⟩



Record views


Files downloads