A Nonparametric Off-Policy Policy Gradient

Samuele Tosatto, Joao Carvalho, Hany Abdulsamad, Jan Peters
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:167-177, 2020.

Abstract

Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular policy gradient algorithms that perform updates using on-policy samples. The priceof such inefficiency becomes evident in real world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited. We address this issue by building on the general sample efficiency of off-policy algorithms. With nonparametric regression and density estimation methods we construct a nonparametric Bellman equation in a principled manner, which allows us to obtain closed-form estimates of the value function, and to analytically express the full policy gradient. We provide a theoretical analysis of our estimate to show that it is consistent under mild smoothness assumptions and empirically show that our approach has better sample efficiency than state-of-the-art policy gradient methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-tosatto20a, title = {A Nonparametric Off-Policy Policy Gradient}, author = {Tosatto, Samuele and Carvalho, Joao and Abdulsamad, Hany and Peters, Jan}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {167--177}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/tosatto20a/tosatto20a.pdf}, url = {https://proceedings.mlr.press/v108/tosatto20a.html}, abstract = {Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular policy gradient algorithms that perform updates using on-policy samples. The priceof such inefficiency becomes evident in real world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited. We address this issue by building on the general sample efficiency of off-policy algorithms. With nonparametric regression and density estimation methods we construct a nonparametric Bellman equation in a principled manner, which allows us to obtain closed-form estimates of the value function, and to analytically express the full policy gradient. We provide a theoretical analysis of our estimate to show that it is consistent under mild smoothness assumptions and empirically show that our approach has better sample efficiency than state-of-the-art policy gradient methods.} }
Endnote
%0 Conference Paper %T A Nonparametric Off-Policy Policy Gradient %A Samuele Tosatto %A Joao Carvalho %A Hany Abdulsamad %A Jan Peters %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-tosatto20a %I PMLR %P 167--177 %U https://proceedings.mlr.press/v108/tosatto20a.html %V 108 %X Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular policy gradient algorithms that perform updates using on-policy samples. The priceof such inefficiency becomes evident in real world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited. We address this issue by building on the general sample efficiency of off-policy algorithms. With nonparametric regression and density estimation methods we construct a nonparametric Bellman equation in a principled manner, which allows us to obtain closed-form estimates of the value function, and to analytically express the full policy gradient. We provide a theoretical analysis of our estimate to show that it is consistent under mild smoothness assumptions and empirically show that our approach has better sample efficiency than state-of-the-art policy gradient methods.
APA
Tosatto, S., Carvalho, J., Abdulsamad, H. & Peters, J.. (2020). A Nonparametric Off-Policy Policy Gradient. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:167-177 Available from https://proceedings.mlr.press/v108/tosatto20a.html.

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