Causal RL

RL: Reinforcement Learning

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Overview

Causal reinforcement learning studies how agents can learn not just which actions correlate with reward, but how actions cause changes in an environment, including when data are confounded or outcomes are delayed. Approaches include structural causal models, causal discovery from demonstrations, deliberate action interventions, counterfactual rollouts, and temporal-logic or automata-based models. These methods are tested in settings such as MuJoCo control, gridworlds, offline goal-reaching, and multi-agent coordination, aiming to improve sample efficiency, transfer, and credit assignment. A central challenge is that observational data alone may not identify action effects, while reliable causal conclusions depend on informative interventions and assumptions about the environment.

Latest papers

CardsList

23 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Causal DAG-Guided Reinforcement Learning for Long-Term Physical Activity Maintenance: Using Prior Data to Inform Future Deployment

    Oct 26, 2026Xueqing Liu, Young Hyun Cho, Bhanu Teja Gullapalli +2Reinforcement LearningLong-Horizon RL

    Harvard University · University of Michigan

  2. Next Best Action: Causal Constrained Offline RL for Business Action Targeting

    Oct 19, 2026Surya Ramachandiran, Yiming Li, Juan Mancilla-Caceres +2Offline RLConstrained RL

    Lyft, Inc., San Francisco, CA, USA

  3. Visual Causal-chain Bookmarking: Retrieval-Weighted Credit Assignment for Reinforcement Learning from Delayed Reward

    Oct 16, 2026Prathap SelvakumarCredit Assignment in RLStep-Level Credit Assignment in RL

    University of Manchester

  4. Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning

    Oct 10, 2026Tingrui Huang, Ai Bo, Junzhe ZhangReinforcement LearningOffline RL

    Eindhoven University of Technology, Eindhoven, The Netherlands · Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA

  5. Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning

    Oct 9, 2026Oriol Corcoll, Qiyao Wei, Ciarán Mark Gilligan-LeeRL for RecommendationStep-Level Credit Assignment in RL

    Spotify · University of Cambridge

  6. From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learning

    Sep 30, 2026Jialu Pi, Yanan Ma, Weijie Chen +8Clinical Outcome PredictionMultimodal Reasoning

    Dept. of Radiology, Mayo Clinic, Phoenix, AZ, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA · Dept. of Radiology, Mayo Clinic, Phoenix, USA +2

  7. Temporal-Causal Inference for Reinforcement Learning via Automata Learning

    Sep 7, 2026Jan Corazza, Daniil Kaminskyi, Simon Lutz +4Reinforcement LearningAutomata Learning

    Research Center Trustworthy Data Science and Security, TU Dortmund University, Dortmund, 44227, Germany. · School for Engineering of Matter, Transport, and Energy at Arizona State University, Tempe, Az 85281, USA.

  8. On the Capability Separation Between World-Model Policy Learning and Imitated World-Action Models

    Aug 23, 2026Yang YuWorld Action ModelsImitation Learning

    Nanjing University

  9. IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning

    Aug 11, 2026Zefeng Liang, Jie Qiao, Ruichu Cai +2Reinforcement LearningRobust RL

    School of Computer Science, Guangdong University of Technology, Guangzhou, China · College of Science, Shantou University, Shantou, China

  10. Learning Implicit Causal World Models from Multi-Agent Demonstrations

    Jul 28, 2026Jasorsi GhoshCausal Representation LearningCausal World Models

    Department of Computer Science Purdue University West Lafayette, Indiana, USA

  11. CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery

    Jul 3, 2026Safia Fatima, Kai Olav Ellefsen, Leon MoonenReinforcement LearningRobot Failure Recovery

    Simula Research Laboratory, Norway · University of Oslo, Norway

  12. An Introduction to Causal Reinforcement Learning

    Jun 23, 2026Elias Bareinboim, Junzhe Zhang, Sanghack LeeReinforcement LearningCausal Inference

    Department of Computer Science Columbia University New York, USA · Graduate School of Data Science Seoul National University Seoul, Republic of Korea

  13. Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds

    May 15, 2026Shaojun Xu, Xiaoling Zhou, Yihan Lin +4Reinforcement LearningCredit Assignment in RL

    Center for Brain-Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, China +1

  14. Delay-Empowered Causal Hierarchical Reinforcement Learning

    May 12, 2026Chenran Zhao, Dianxi Shi, Haotian Wang +4Reinforcement LearningHierarchical RL

    College of Computer Science and Technology, National University of Defense Technology, Changsha, China · Intelligent Game and Decision Lab (IGDL), Beijing, China · School of Artificial Intelligence, Hebei University of Technology, Tianjin, China +1

  15. From Causal Graphs to Markovian States: Multi-Order State Exposure for Deep RL

    May 8, 2026Jiamin Xu, Jacqueline Maasch, Kyra GanMarkov Decision ProcessesCausal Representation Learning

    Cornell Tech, New York, NY

  16. Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark

    May 7, 2026Cristiano da Costa Cunha, Ajmal Mian, Tim French +1RL BenchmarksStructural Causal Models

    Department of Computer Science and Software Engineering, University of Western Australia, 6009 WA Australia

  17. MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning

    May 3, 2026Haohan Yu, Jinmiao Cong, Shengzhi Wang +2Multi-Agent CoordinationIntrinsic Reward Methods

    1Dalian University of Technology · 2Microsoft, Beijing, China

  18. From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

    Apr 10, 2026Chenchen ZhangCredit Assignment in RLRL for Language Models

    Independent Researcher

  19. Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning

    Apr 6, 2026Yiyao Zhang, Diksha Goel, Hussain AhmadAdversarial MARLAutonomous Cyber Defense

    Adelaide University, Australia · CSIRO’s Data61, Australia

  20. Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning

    Mar 18, 2026Jiaxin Liu, Anzhe Cheng, Paul BogdanRL ControlCausal RL

    University of Illinois Urbana-Champaign · University of Southern California

  21. Causally Robust Reward Learning from Reason-Augmented Preference Feedback

    Mar 5, 2026Minjune Hwang, Yigit Korkmaz, Daniel Seita +1Reward ModelingPreference-Based RL

    Thomas Lord Department of Computer Science, University of Southern California

  22. Learning Optimal and Sample-Efficient Decision Policies with Guarantees

    Feb 20, 2026Daqian ShaoReinforcement LearningSample-Efficient RL

    Wolfson College University of Oxford

  23. Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

    Jun 23, 2023Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione +5Reinforcement LearningCausal RL

    Arizona State University, Arizona, USA · Max Planck Institute for Software Systems, Kaiserslautern, Germany · University of Texas at Austin, Texas, USA +3