Discount Factor as a Regularizer in Reinforcement Learning

Ron Amit, Ron Meir, Kamil Ciosek
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:269-278, 2020.

Abstract

Specifying a Reinforcement Learning (RL) task involves choosing a suitable planning horizon, which is typically modeled by a discount factor. It is known that applying RL algorithms with a lower discount factor can act as a regularizer, improving performance in the limited data regime. Yet the exact nature of this regularizer has not been investigated. In this work, we fill in this gap. For several Temporal-Difference (TD) learning methods, we show an explicit equivalence between using a reduced discount factor and adding an explicit regularization term to the algorithm’s loss. Motivated by the equivalence, we empirically study this technique compared to standard L2 regularization by extensive experiments in discrete and continuous domains, using tabular and functional representations. Our experiments suggest the regularization effectiveness is strongly related to properties of the available data, such as size, distribution, and mixing rate.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-amit20a, title = {Discount Factor as a Regularizer in Reinforcement Learning}, author = {Amit, Ron and Meir, Ron and Ciosek, Kamil}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {269--278}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/amit20a/amit20a.pdf}, url = { http://proceedings.mlr.press/v119/amit20a.html }, abstract = {Specifying a Reinforcement Learning (RL) task involves choosing a suitable planning horizon, which is typically modeled by a discount factor. It is known that applying RL algorithms with a lower discount factor can act as a regularizer, improving performance in the limited data regime. Yet the exact nature of this regularizer has not been investigated. In this work, we fill in this gap. For several Temporal-Difference (TD) learning methods, we show an explicit equivalence between using a reduced discount factor and adding an explicit regularization term to the algorithm’s loss. Motivated by the equivalence, we empirically study this technique compared to standard L2 regularization by extensive experiments in discrete and continuous domains, using tabular and functional representations. Our experiments suggest the regularization effectiveness is strongly related to properties of the available data, such as size, distribution, and mixing rate.} }
Endnote
%0 Conference Paper %T Discount Factor as a Regularizer in Reinforcement Learning %A Ron Amit %A Ron Meir %A Kamil Ciosek %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-amit20a %I PMLR %P 269--278 %U http://proceedings.mlr.press/v119/amit20a.html %V 119 %X Specifying a Reinforcement Learning (RL) task involves choosing a suitable planning horizon, which is typically modeled by a discount factor. It is known that applying RL algorithms with a lower discount factor can act as a regularizer, improving performance in the limited data regime. Yet the exact nature of this regularizer has not been investigated. In this work, we fill in this gap. For several Temporal-Difference (TD) learning methods, we show an explicit equivalence between using a reduced discount factor and adding an explicit regularization term to the algorithm’s loss. Motivated by the equivalence, we empirically study this technique compared to standard L2 regularization by extensive experiments in discrete and continuous domains, using tabular and functional representations. Our experiments suggest the regularization effectiveness is strongly related to properties of the available data, such as size, distribution, and mixing rate.
APA
Amit, R., Meir, R. & Ciosek, K.. (2020). Discount Factor as a Regularizer in Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:269-278 Available from http://proceedings.mlr.press/v119/amit20a.html .

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