Taylor Expansion Policy Optimization

@InProceedings{pmlr-v119-tang20d,
  title = 	 {Taylor Expansion Policy Optimization},
  author =       {Tang, Yunhao and Valko, Michal and Munos, Remi},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {9397--9406},
  year = 	 {2020},
  editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--18 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v119/tang20d/tang20d.pdf},
  url = 	 {https://proceedings.mlr.press/v119/tang20d.html},
  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.}
}