arXiv Machine Learning By Boyang Xu, Qing Zou, Siqin Yang, Hao Yan

Path-Coupled Bellman Flows for Distributional Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2605. 08253v2 Announce Type: replace Abstract: Distributional reinforcement learning (DRL) models the full return distribution, but existing finite-support or quantile-based methods rely on projections, while recent flow-based approaches can suffer from \emph{boundary mismatch} at the flow source or from \emph{high-variance} bootstrapping when current and successor noises are independent.

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arXiv AI
Jun 2

Value Flows

arXiv:2510. 07650v4 Announce Type: replace-cross Abstract: While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods exploit the return distribution to provide stronger learning signals and to enable applications in exploration and safe RL.

By Perry Dong, Chongyi Zheng, Chelsea Finn, Dorsa Sadigh, Benjamin Eysenbach
arXiv AI
Sep 21

Deep Reinforcement Learning with Buffered Quantile Objectives

The paper introduces Deep-BQRL, a model‑free distributional reinforcement‑learning framework that extends buffered‑quantile learning to neural function approximation. It learns conditional return quantiles from sampled transitions, constructs buffered action scores, and uses ensemble disagreement for exploration, enabling risk‑sensitive decision‑making without explicit return‑law planning. Experiments on asset‑selling and slippery FrozenLake show that Deep‑BQRL achieves smaller mean cumulative point‑quantile policy gaps than PPO and TRPO, while illustrating interpretable risk‑sensitive stopping decisions.

By Mohammad Alipour-vaezi, Sajad Khodadadian