Distributional RL

RL: Reinforcement Learning

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Variance-Averse $n$ -Step Offline Reinforcement Learning for Sparse Long-Horizon Environments
Oct 6, 2026Guhyeon Kang, Minhae KwonDistributional RL Risk-Sensitive RL
Department of Electrical and Computer Engineering Sungkyunkwan University, Republic of Korea
Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective
Sep 21, 2026Larry Preuett, Qiuyi Zhang, Muhammad Aurangzeb AhmadDistributional RL Markov Decision Processes
University of Washington, Tacoma, USA · Google DeepMind, California, USA · University of Washington, Bothell, USA
A Finite-Sample Analysis of Quantile Temporal-Difference Learning
Aug 27, 2026Zijie Cheng, Xiang Li, Yang Peng +1Distributional RL Stochastic Approximation
School of Mathematical Sciences, Peking University · Perelman School of Medicine, University of Pennsylvania · Yau Mathematical Sciences Center, Tsinghua University
Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
Aug 13, 2026Zijie Cheng, Yang Peng, Zhihua ZhangDistributional RL Reinforcement Learning
School of Mathematical Sciences, Peking University · Yau Mathematical Sciences Center, Tsinghua University
Distributional Soft Bellman Operator under the Cramér Geometry
Jul 20, 2026Keru Wang, Yixin Deng, Yao Lyu +2Distributional RL Maximum Entropy RL
School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland · School of Vehicle and Mobility, Tsinghua University, Beijing, China · College of Artificial Intelligence, Tsinghua University, Beijing, China
Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approach
Jul 14, 2026Tianyu Pang, Hongyu LiDistributional RL Reconfigurable Intelligent Surfaces
Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Auditing the Risk Claims of Distributional Reinforcement Learning
Jul 13, 2026Hari PrasadDistributional RL Algorithmic Auditing
Statistical Efficiency and Inference of Quantile Distributional Reinforcement Learning
Jul 9, 2026Zijie Cheng, Yang Peng, Zhihua ZhangDistributional RL Reinforcement Learning
School of Mathematical Sciences, Peking University · Yau Mathematical Sciences Center, Tsinghua University
Learning the Supports for Categorical Critic in Reinforcement Learning
Jul 2, 2026Jen-Yen Chang, Takayuki Osa, Tatsuya HaradaDistributional RL
Graduate School of Information Science and Technology, The University of Tokyo · RIKEN AIP
FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion
Jun 30, 2026Guanchen Lu, Yajuan Dun, Yi Zhou +4Distributional RL Reinforcement Learning
Chongqing University, Chongqing 400044, China · Ministry of Industry and Information Technology, Beijing 100804, China · Tsinghua University, Beijing 100084, China +1
Dual-Flow Reinforcement Learning with State-Aware Exploration
Jun 29, 2026Qijun Li, Zheng Fu, Qi Song +4Distributional RL Reinforcement Learning
School of Vehicle and Mobility and the State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
RS-Diffuser: Risk-Sensitive Diffusion Planning with Distributional Value Guidance
Jun 26, 2026Shiqiang GongDistributional RL Diffusion-Based Planning
Northwestern Polytechnical University
DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation
Jun 12, 2026Miduo Cui, Haochen Wang, Shangqin Mao +6Distributional RL Automated Bidding
Shandong University, Jinan, China · Meituan, Beijing, China
QnRL: Quantum-Native Reinforcement Learning
Jun 6, 2026Alexander DeRieux, Walid SaadDistributional RL Quantum Generative Modeling
Bradley Department of Electrical and Computer Engineering, Virginia Tech Institute for Advanced Computing, Alexandria, VA 22305 USA
Reinforcement Learning from Rich Feedback with Distributional DAgger
Jun 3, 2026Rishabh Agrawal, Jacob Fein-Ashley, Paria RashidinejadDistributional RL RL for Language Model Reasoning
University of Southern California
Multivariate Distributional Reinforcement Learning Using Sliced Divergences
May 29, 2026Baptiste Debes, Tinne TuytelaarsDistributional RL
Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium.
On Distributional Reinforcement Learning in Chaotic Dynamical Systems
May 28, 2026James Rudd-Jones, Mirco Musolesi, María Pérez-OrtizDistributional RL Reinforcement Learning
Centre for Artificial Intelligence Department of Computer Science University College London London, UK · Department of Computer Science and Engineering, University of Bologna Bologna, Italy
Reinforcement Learning over Predictive Distributions for LLM Regression
May 20, 2026Jungsoo Park, Hyungjoo Chae, Ethan Mendes +4Distributional RL RL for Language Models
Georgia Institute of Technology · Allen Institute for AI
Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
May 11, 2026Ege C. Kaya, Aliasghar Pourghani, Vijay Gupta +1Distributional RL Reinforcement Learning
Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47906
Quantile-Coupled Flow Matching for Distributional Reinforcement Learning
May 8, 2026Michael Groom, Victor-Alexandru Darvariu, Lars Kunze +2Distributional RL Conditional Flow Matching
Oxford Robotics Institute, University of Oxford, Oxford, United Kingdom · Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom · Dyson Institute of Engineering & Technology, Malmesbury, United Kingdom
A Sharp Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
May 7, 2026Ege C. Kaya, Abolfazl HashemiDistributional RL Reinforcement Learning
Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47906
Path-Coupled Bellman Flows for Distributional Reinforcement Learning
May 7, 2026Boyang Xu, Qing Zou, Siqin Yang +1Distributional RL Flow Matching
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
Revisiting Adam for Streaming Reinforcement Learning
May 7, 2026Florin Gogianu, Adrian Catalin Lutu, Razvan PascanuDistributional RL Reinforcement Learning
Bitdefender, Bucharest, Romania · Mila, Montreal, Canada
Quantile Geometry Regularization for Distributional Reinforcement Learning
May 5, 2026Zhaofan Zhang, Minghao Yang, Rufeng Chen +2Distributional RL Quantile Regression
Information Hub, AI Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China
Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
May 3, 2026Sungyoung Lee, Dohyeong Kim, Eshan Balachandar +2Distributional RL Q-Learning
The University of Texas at Austin, Austin, TX, USA · Independent Researcher, Seoul, South Korea.
Distributional Reinforcement Learning via the Cramér Distance
Apr 26, 2026Vanya Aziz, Ivo Nowak, E. M. T HendrixDistributional RL Robotic RL
HAW Hamburg · de Málaga
Distributional Value Estimation Without Target Networks for Robust Quality-Diversity
Apr 22, 2026Behrad Koohy, Jamie BayneDistributional RL Reinforcement Learning
Luffy.AI Culham, Oxfordshire, United Kingdom
Distributional Off-Policy Evaluation with Deep Quantile Process Regression
Apr 20, 2026Qi Kuang, Chao Wang, Yuling Jiao +1Distributional RL Quantile Regression
School of Statistics and Data Science, and Philosophy and Social Sciences Laboratory of Data Science in Finance and Economics at the Ministry of Education,2026 Jiangxi University of Finance and Economics · School of Statistics and Data Science, Shanghai University of Finance and EconomicsApr · School of Artificial Intelligence, and Hubei Key Laboratory of Computational Science, 24 Wuhan University
On the Approximation and Convergence of Distributional Policy Gradient Algorithms for Risk-Sensitive Reinforcement Learning
May 23, 2024Xian Yu, Minheng Xiao, Lei YingStochastic Optimization Distributional RL
Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA · Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

Oct 6, 2026, cs.LGJ/K move · Enter open · S save

Variance-Averse $n$ -Step Offline Reinforcement Learning for Sparse Long-Horizon Environments

Guhyeon Kang, Minhae Kwon

Department of Electrical and Computer Engineering Sungkyunkwan University, Republic of Korea

Distributional RL Risk-Sensitive RL Offline RL RL for Generative Models

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Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions. However, this expressiveness also exposes a key challenge in heterogeneous datasets: generative policies can reproduce unreliable action modes whose return distributions exhibit high variance, occasionally yielding high returns by chance but lacking consistency. Consequently, maximizing the expected

Q

-value alone is insufficient for identifying reliable actions. We propose VAN-Flow (Variance-Averse

n

-step Flow), a framework that promotes reliable actions in generative offline RL. VAN-Flow combines (i) a categorical distributional critic, (ii) a variance-averse expectation operator that smoothly reweights atom probabilities to favor actions with both high returns and low dispersion, and (iii) a flow-matching generative actor guided via rejection sampling. Unlike CVaR or mean-variance objectives, the operator redistributes probability mass over the categorical return distribution without hard truncation or auxiliary penalty terms. Across more than 40 tasks from D4RL and OGBench, VAN-Flow consistently outperforms strong baselines, with the largest gains in long-horizon and high-variance regimes where reliable action selection becomes critical.

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