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Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement
Ke Sun1, Yingnan Zhao2, Enze Shi1
1University of Alberta, Canada.
Advances in Neural Information Processing Systems
|July 9, 2026
Summary
Distributional reinforcement learning (RL) offers superior performance by using an uncertainty-aware entropy regularization. This method captures return distribution details, enhancing policy optimization beyond classical RL methods.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Reinforcement Learning
Background:
- Distributional reinforcement learning (RL) demonstrates strong empirical performance, prompting theoretical investigation into its advantages over classical RL.
- Classical RL typically focuses on the expected return, potentially missing crucial information within the return distribution.
Purpose of the Study:
- To theoretically explain the empirical success of distributional RL.
- To identify the core mechanism behind distributional RL's superiority.
- To introduce a novel perspective on exploration in RL.
Main Methods:
- Decomposition of the categorical distributional loss in distributional RL.
- Derivation and analysis of a novel distribution-matching entropy regularization.
- Empirical validation through extensive experiments comparing distributional RL with classical RL.
Main Results:
- The superiority of distributional RL is attributed to a derived distribution-matching entropy regularization.
- This novel regularization captures additional knowledge from the return distribution, augmenting the reward signal.
- The derived regularization implicitly aligns policies with environmental uncertainty, unlike explicit exploration in MaxEnt RL.
Conclusions:
- Distributional RL's benefits stem from an implicit, uncertainty-aware entropy regularization.
- This approach enhances policy optimization by leveraging the full return distribution.
- The study provides a new framework for understanding exploration in RL through distributional learning.
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