Interpreting Distributional Reinforcement Learning: A Regularization PerspectiveDownload PDF

Published: 01 Feb 2023, Last Modified: 13 Feb 2023Submitted to ICLR 2023Readers: Everyone
Keywords: distributional reinforcement learning, regularization, entropy
TL;DR: We interpret distributional reinforcement learning from the perspectives of regularization.
Abstract: Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the entire distribution of the total return rather than its expected value alone. The theoretical advantages of distributional RL over expectation-based RL remain elusive, despite the remarkable performance of distributional RL. Our work attributes the superiority of distributional RL to its regularization effect stemming from the value distribution information regardless of only its expectation. We decompose the value distribution into its expectation and the remaining distribution part using a variant of the gross error model in robust statistics. Hence, distributional RL has an additional benefit over expectation-based RL thanks to the impact of a \textit{risk-sensitive entropy regularization} within the Neural Fitted Z-Iteration framework. Meanwhile, we investigate the role of the resulting regularization in actor-critic algorithms by bridging the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL. It reveals that distributional RL induces an augmented reward function, which promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments verify the importance of the regularization effect in distributional RL, as well as the mutual impacts of different entropy regularizations. Our study paves the way towards a better understanding of distributional RL, especially when looked at through a regularization lens.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)
Supplementary Material: zip
16 Replies