Publication Details

SELECT * FROM publications WHERE Record_Number=11316
Reference TypeConference Proceedings
Author(s)Tosatto, S.; Carvalho, J.; Abdulsamad, H.; Peters, J.
Year2020
TitleA Nonparametric Off-Policy Policy Gradient
Journal/Conference/Book TitleProceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)
Keywordsnonparametric, policy gradient, off policy, reinforcement learning
AbstractReinforcement 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 price of 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.
Link to PDFhttps://www.ias.informatik.tu-darmstadt.de/uploads/Team/SamueleTosatto/tosatto2020.pdf

  

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