Reinforcement Learning

Also known as RL

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144 papers in the last four weeks, up 243% on the four weeks before. 1.4% of all new papers.

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Nonparametric Bayesian Inverse Reinforcement Learning with Data-Parallel Gibbs Sampling
Jul 10, 2026Sai Anirudh Katupilla, Shreeya Dasa LakshminathReinforcement Learning Bayesian Inference
University of Maryland, College Park
PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers
Jul 10, 2026Yujie Pang, Zudong LiReinforcement Learning Contact-Rich Robotic Manipulation
Dept. of Mechanical and Energy Engineering · Southern University of Science and Technology · Shenzhen, China
Vascular Geometry Characterization for AI-Based Endovascular Navigation
Jul 10, 2026Han-Ru Wu, Harry Robertshaw, Lisa Dwyer-Joyce +2Reinforcement Learning Medical Image Analysis
Department of Medical Imaging Science National Taiwan University Hospital Taiwan · Surgical & Interventional Engineering School of Biomedical Engineering & Imaging Sciences Kings College London UK · Chelsea & Westminster Hospital UK
Toward Active Object Detection for UAVs in the Wild: A Large-Scale Dataset, Benchmark and Method
Jul 10, 2026Tianpeng Liu, Xinhua Jiang, Li Liu +4Reinforcement Learning Object Detection
College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
Jul 9, 2026Ali Larian, Qian Lin, Chang Zong Wu +1Reinforcement Learning Human-in-the-Loop AI
Kahlert School of Computing, University of Utah.
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
Physics-Guided Biomechanical Gait Adaptation for Humanoid Locomotion on Extreme Sloped Terrains
Jul 8, 2026Xuanyu Chen, Mohan Liu, Dengchen Mei +6Humanoid Robot Locomotion Reinforcement Learning
Nanyang Technological University · A*STAR
Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
Jul 8, 2026Ezgi KorkmazRL Benchmarks Reinforcement Learning
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
Jul 8, 2026Zhenyu Hou, Yujiang Li, Jie Tang +1Agentic RL Asynchronous RL
Tsinghua University · Work done while ZH and YL interned at Z.AI.
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
Jul 8, 2026Florian Fuchs, Jessy Gosselin-Grant, Boris Skuin +5Automated Software Testing Reinforcement Learning
Electronic Arts (EA)
A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents
Jul 8, 2026Hari PrasadComputational Cognitive Modeling Reinforcement Learning
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Jul 8, 2026Ignacio D. Lopez-Miguel, Ezio Bartocci, Thomas Eiter +1Reinforcement Learning Interpretable ML
TU Wien Vienna, Austria
Mathematical methods of reinforcement learning
Jul 8, 2026Denis Belomestny, Alexander Gasnikov, Egor Gladin +5Markov Decision Processes Reinforcement Learning
Duisburg-Essen University, HSE University · Innopolis University, MIPT, HSE University · HSE University +1
A Gold-Standard Study of What Makes a Lightweight Game-Playing Agent Strong
Jul 7, 2026Nima Kelidari, Mohammadsaeed Haghi, Mahdi SalmaniReinforcement Learning Game-Playing Agents
University of Southern California
Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
Jul 6, 2026Omar Al-Refai, Ibrahim Shahbaz, Adam Ali Husseinat +1Reinforcement Learning Federated RL
iSTAR Lab, Texas A&M University College Station, TX, USA
Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Jul 6, 2026Muhammad Zain Amin, Kibele Sebnem YildirimReinforcement Learning RL for Language Model Reasoning
Department of Software Engineering and IT, ´Ecole de Technologie Sup´erieure, Montreal, Canada · Centre int´egr´e de traumatologie, Hˆopital du Sacr´e-Cœur de Montr´eal, Universit´e de Montr´eal, Montreal, Canada · Desautels Faculty of Management, McGill University, Montreal, Canada.
Diffusion-Guided Uncertainty-Aware Delayed Policy Optimization
Jul 6, 2026Junqi Tu, Zejiao Liu, Fangfei Li +1Reinforcement Learning Robotic RL
The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China · The School of Mathematics, East China University of Science and Technology, Shanghai, China
RL-Ballast: Ship Ballast Water Path Planning and Clog Prediction via Reinforcement Learning
Jul 6, 2026Ming-Kuan Lin, Yi-Chung Lai, Ming-Hsin Chiang +2RL Control Reinforcement Learning
Department of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan · AI Research Center · Shinsoft Corporation, Taiwan +1
RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents
Jul 6, 2026Qiang Liu, Taian Guo, Ruizhi Qiao +1Reinforcement Learning Sparse-Reward RL
Tencent YouTu Lab
Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning
Jul 6, 2026Saurabhsingh Rajput, Tushar SharmaRL for Code Generation Reinforcement Learning
Dalhousie University, Halifax, Canada
LLM-as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL
Jul 5, 2026Yujin Kim, Namgyu Ho, Sangmin Hwang +7RL for Language Models Reinforcement Learning
1KAIST · 2Upstage · University of Toronto +2
Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks
Jul 5, 2026Yi Lan, Ye YuanGraph Neural Networks Reinforcement Learning
College of Computer and Information Science, Southwest University, Tiansheng Road, Chongqing, 400715, China
CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning
Jul 4, 2026Jiayi Guan, Tianle Zhang, Li Shen +8Reinforcement Learning Constrained RL
Tongji University, Shanghai, China · JD Explore Academy, Beijing, China · School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +2
Explainable Reinforcement Learning for Adaptive Traffic Signal Control
Jul 4, 2026Dickens Kwesiga, Nishu Choudhary, Angshuman Guin +1Reinforcement Learning Traffic Signal Control
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry
Jul 4, 2026Weiyang Guo, Zesheng Shi, Longhui Zhang +3Reinforcement Learning Credit Assignment in RL
Harbin Institute of Technology, Shenzhen, China
High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning
Jul 3, 2026Yanzhou Li, Guangli Chen, Xiao-Meng Li +3Multi-Robot Formation Control Multi-Robot Systems
Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, Great Bay University, Dongguan 523000, China · Chair of Applied Statistics, Technische Universität Dresden, Dresden, Germany · School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China +2
Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance
Jul 3, 2026Andrei Starodubov, Yaqub Aris Prabowo, Andreas Hadjipieris +2Reinforcement Learning
Cyprus Marine and Maritime Institute, Larnaca, Cyprus · Technical University of Denmark, Lyngby, Denmark
CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
Jul 3, 2026Safia Fatima, Kai Olav Ellefsen, Leon MoonenReinforcement Learning Robot Failure Recovery
Simula Research Laboratory, Norway · University of Oslo, Norway
Anticipatory Reinforcement Learning for Trajectory Tracking
Jul 3, 2026Georg Schäfer, Jakob Rehrl, Stefan Huber +1RL Control Robot Trajectory Tracking
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Salzburg, Austria · Paris Lodron University of Salzburg, Salzburg, Austria
Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System
Jul 3, 2026Georg Schäfer, Jakob Rehrl, Stefan HuberRL Control Reinforcement Learning
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Salzburg, Austria · Paris Lodron University of Salzburg, Salzburg, Austria

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Nonparametric Bayesian Inverse Reinforcement Learning with Data-Parallel Gibbs Sampling

Sai Anirudh Katupilla, Shreeya Dasa Lakshminath

University of Maryland, College Park

Reinforcement Learning Bayesian Inference

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Inverse Reinforcement Learning recovers reward functions from expert demonstrations, but standard formulations assume that all demonstrations come from a single expert. When demonstrations are pooled from multiple experts with distinct preferences, parametric methods recover an averaged reward that fits no individual expert well. We implement Nonparametric Bayesian Inverse Reinforcement Learning with a Dirichlet Process prior over reward functions, allowing the number of latent reward types to be inferred jointly with the rewards themselves. Inference uses a collapsed Gibbs sampler combining a Chinese Restaurant Process update for cluster assignments with a Metropolis-Hastings update for reward weights, and soft value iteration as the inner planning routine. We evaluate on a 10x10 ObjectWorld grid with two and three ground-truth reward types. The serial sampler recovers K=2 with Adjusted Rand Index of 1.000, substantially outperforming a Maximum Entropy IRL baseline (ARI=0.000). Extension to K=3 shows that the sampler correctly identifies the number of clusters in all runs; assignment ARI of 0.48-0.58 reflects behavioral overlap between expert types that persists across grid instantiations, revealing that reliable K=3 evaluation on ObjectWorld requires controlled object placement rather than random seeding. We further parallelize the sampler across CPU cores using Ray on HPC hardware, achieving a peak speedup of 4.79x at 8 workers, and characterize a throughput-versus-accuracy tradeoff arising from the consensus merge heuristic used during state aggregation. Code and a containerized environment are available at https://github.com/dasashreeya/np_bayes_irl.

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Latest papers 1,138