Sample-based Distributional Policy Gradient

Rahul Singh, Keuntaek Lee, Yongxin Chen
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:676-688, 2022.

Abstract

Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards. Most of the existing literature on DRL focuses on problems with discrete action space and value based methods. In this work, motivated by applications in control engineering and robotics where the action space is continuous, we propose the sample-based distributional policy gradient (SDPG) algorithm. It models the return distribution using samples via a reparameterization technique widely used in generative modeling. We compare SDPG with the state-of-the-art policy gradient method in DRL, distributed distributional deterministic policy gradients (D4PG). We apply SDPG and D4PG to multiple OpenAI Gym environments and observe that our algorithm shows better sample efficiency as well as higher reward for most tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v168-singh22a, title = {Sample-based Distributional Policy Gradient}, author = {Singh, Rahul and Lee, Keuntaek and Chen, Yongxin}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference}, pages = {676--688}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/singh22a/singh22a.pdf}, url = {https://proceedings.mlr.press/v168/singh22a.html}, abstract = {Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards. Most of the existing literature on DRL focuses on problems with discrete action space and value based methods. In this work, motivated by applications in control engineering and robotics where the action space is continuous, we propose the sample-based distributional policy gradient (SDPG) algorithm. It models the return distribution using samples via a reparameterization technique widely used in generative modeling. We compare SDPG with the state-of-the-art policy gradient method in DRL, distributed distributional deterministic policy gradients (D4PG). We apply SDPG and D4PG to multiple OpenAI Gym environments and observe that our algorithm shows better sample efficiency as well as higher reward for most tasks.} }
Endnote
%0 Conference Paper %T Sample-based Distributional Policy Gradient %A Rahul Singh %A Keuntaek Lee %A Yongxin Chen %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-singh22a %I PMLR %P 676--688 %U https://proceedings.mlr.press/v168/singh22a.html %V 168 %X Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards. Most of the existing literature on DRL focuses on problems with discrete action space and value based methods. In this work, motivated by applications in control engineering and robotics where the action space is continuous, we propose the sample-based distributional policy gradient (SDPG) algorithm. It models the return distribution using samples via a reparameterization technique widely used in generative modeling. We compare SDPG with the state-of-the-art policy gradient method in DRL, distributed distributional deterministic policy gradients (D4PG). We apply SDPG and D4PG to multiple OpenAI Gym environments and observe that our algorithm shows better sample efficiency as well as higher reward for most tasks.
APA
Singh, R., Lee, K. & Chen, Y.. (2022). Sample-based Distributional Policy Gradient. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:676-688 Available from https://proceedings.mlr.press/v168/singh22a.html.

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