Representation Learning for RL

RL: Reinforcement Learning

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Overview

Representation learning for reinforcement learning studies how agents can turn observations and interaction histories into features that preserve what matters for predicting outcomes and choosing actions. Common approaches include predictive and contrastive latent models, successor and spectral representations, bisimulation metrics, and regularizers that encourage sparse, stable, or disentangled features. These methods support visual control, exploration, offline goal-conditioned navigation, world modeling, and transfer across tasks or environments. A central tension is that reusable features must discard irrelevant variation without losing details needed for control, and better-looking representations do not always improve downstream performance.

Latest papers

CardsList

69 papers18 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

  1. Primal-Dual Representation Learning for Low-Rank Constrained MDPs

    Oct 22, 2026Chenhao Zhou, Chao Zhang, Wensong Bai +2Markov Decision ProcessesConstrained RL

    College of Computer Science and Technology, Zhejiang University

  2. Short-to-Long: Detached Features for Long-Horizon Value Learning

    Oct 21, 2026Tong Che, Boris Ivanovic, Marco PavoneReinforcement LearningLong-Horizon RL

    NVIDIA Research

  3. Reframing Object-Centric Reinforcement Learning: From Absolute to Relative Representations

    Oct 20, 2026Cedric Derstroff, Jannis Blüml, Janosch Moos +4Reinforcement LearningRepresentation Learning for RL

    The Hessian Center for Artificial Intelligence (hessian.AI) · Technische Universität Darmstadt · Centre for Cognitive Science +1

  4. Dynamics-Aligned Representation Learning under Induced Partial Observability

    Oct 18, 2026SM Mazharul Islam, Manfred HuberModel-Based RLRepresentation Learning for RL

    Department of Computer Science and Engineering, University of Texas at Arlington, TX-76019, USA

  5. Reinforcement Learning with Hawkes-Structured Networks for Market Making

    Oct 12, 2026Fernando Acero, Philipp Plank, Yufei ZhangHawkes ProcessesNon-Markovian RL

    Department of Mathematics, Imperial College London, United Kingdom

  6. Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning

    Oct 11, 2026Chang-Hoon Jeong, Kisung Shin, Hanwool Sul +1Meta-Reinforcement LearningOOD Generalization

    Dept. of Computer Science and Engineering, Seoul National University · Interdisciplinary Program in Artificial Intelligence, Seoul National University

  7. Sparse-TD-JEPA: Improving Zero-Shot Reinforcement Learning With Sparse Latent Task Representations

    Oct 9, 2026Duaa Nakshbandi, Zhiyu An, Wan DuZero-Shot RLReinforcement Learning

    University of California, Merced

  8. Disentangling Task-Relevant Latent Dynamics for Reward Modeling

    Oct 9, 2026Dayou Li, Jionghao Wang, Peihao Wang +9Dense Reward RLMultimodal Reward Modeling

    Texas A&M University · University of Texas at Austin · Google DeepMind +1

  9. Q-Shaped Options for Hierarchical Reinforcement Learning

    Oct 8, 2026Clarisse Wibault, Antoine Gorceix, Antonio Léon Villares +5Hierarchical RLRepresentation Learning for RL

    University of Oxford

  10. FOCUS: From Privileged States to RGB-D with Controlled Modality Switching and Representation Alignment

    Oct 8, 2026Filip Grigorov, Kourosh Darvish, Nandita VijaykumarRobotic RLVisual RL

    University of Toronto Canada

  11. Taylor Representations for Model-Free RL in Networked MDPs

    Oct 4, 2026Salah Chikhi, Abdelhaq Chaoui, Asuman Ozdaglar +1Markov Decision ProcessesRepresentation Learning for RL

    Massachusetts Institute of Technology

  12. Mastering Atari 2600 Games with Discovered Options

    Oct 2, 2026Erik M. Lintunen, Marlos C. MachadoRL for Game AIHierarchical RL

    University of Alberta · Alberta Machine Intelligence Institute (Amii) · Canada CIFAR AI Chair

  13. Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings

    Oct 2, 2026Niklas Emonds, Georgia KoppeTransfer Learning in RLReinforcement Learning

    Hector Institute for AI in Psychiatry (HITKIP) & Department of Psychiatry and Psychotherapy, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University · Interdisciplinary Center for Scientific Computing (IWR), Faculty of Mathematics and Computer Science, Heidelberg University · Hertie Institute for AI in Brain Health, University of Tübingen, Germany

  14. eRLT: Efficient VLA Reinforcement Learning via Action-Relevant Token Routing

    Oct 1, 2026Dehao Huang, Jianbang Liu, Jianpan Gao +7Efficient VLA ModelsRobotic Manipulation

    Southern University of Science and Technology, Shenzhen, China. · Beijing Zhongguancun Academy, Beijing, China. · Samsung Robotics eXperience. +2

  15. Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?

    Sep 30, 2026Syed Nazmus Sakib, Abdul Monaf Chowdhury, Nafiul Haque +2Offline RLRepresentation Learning for RL

    Department of Robotics and Mechatronics Engineering, University of Dhaka, Bangladesh · Department of Computer Science, University of Oxford, UK

  16. Reinforcement Learning with Complex (valued) Memories

    Sep 29, 2026Sathya Kamesh Bhethanabhotla, Efstratios Gavves, André BiedenkappPartially Observable RLRecurrent Neural Networks

    University of Freiburg

  17. State Trace Rationale As Auxiliary Task in Reinforcement Learning

    Sep 29, 2026Muhammad U. Nasir, Alex Vogt, Steven D. James +1Reinforcement LearningRepresentation Learning for RL

    University of the Witwatersrand Johannesburg, South Africa · New York University New York, USA

  18. Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

    Sep 28, 2026Antonio Pariente, Ignacio Boero, Nikolai Matni +1Dynamical SystemsWorld Model Learning

    University of Pennsylvania

  19. Offline Policy Evaluation via Mixed Bellman Residuals and Adaptive Critic Representations

    Sep 27, 2026Amitakshar Biswas, Yuhan Li, Ruoqing ZhuOff-Policy EvaluationRepresentation Learning for RL

    Department of Statistics, University of Illinois at Urbana-Champaign

  20. Self-Confirming Superposition Traps in Reinforcement Learning

    Sep 26, 2026Dai Shi, Andi Han, Feng Chen +3Representation Learning for RL

    University of Cambridge · University of Sydney · University of Adelaide +1

  21. Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks

    Sep 18, 2026Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke +2Intelligent Transportation SystemsRL Control

    Dept. of Civil, Environmental and Infrastructure Engineering George Mason University Fairfax, VA, USA · Dept. of Sustainability University of South Dakota Vermillion, SD, USA · School of Information Studies Syracuse University Syracuse, NY, USA +2

  22. MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling

    Sep 11, 2026Tiangang Li, Shi Ying, Xiangbo Tian +2Cross-AttentionCloud Computing

    School of Computer Science, Wuhan University, Wuhan 430072, China · School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

  23. Mind the Phase: Effective Rank and Representation Health in Legged Locomotion

    Sep 7, 2026Felipe Tommaselli, Ricardo V. Godoy, Thiago H. Segreto +2RL for Legged LocomotionRepresentation Learning for RL

    Mobile Robotics Group, University of S˜ao Paulo

  24. Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL

    Aug 31, 2026Michal Korniak, Kamil Dybek, Benjamin Eysenbach +2Reinforcement LearningOffline RL

    ETH Zurich · University of Warsaw · Princeton University +3

  25. Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations

    Aug 26, 2026Aditya Makkar, Benjamin Unger, Jeongyeol Kwon +3Mean-Field MARLReinforcement Learning

    New York University · ETH Zurich · Meta Platforms Inc. +1

  26. FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning

    Aug 17, 2026Tristan Gottwald, Maximilian Schier, Melanie Schaller +1Representation Learning for RLEvent-Based Vision

    Institute of Information Processing, L3S, Leibniz University Hannover, Germany

  27. Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading

    Aug 16, 2026Arishi Orra, Himanshu Choudhary, Manoj ThakurReinforcement LearningRepresentation Learning for RL

    School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi, Mandi, 175005, Himachal Pradesh, India

  28. Temporal Logic Guided Universal Task Representations for Reinforcement Learning

    Aug 16, 2026Hao Zhang, Zhangli Zhou, Zhen KanRepresentation Learning for RLReinforcement Learning

    Department of Automation at the University of Science and Technology of China, Hefei, Anhui, China, 230026 · Anhui Provincial Key Laboratory of Humanoid Robots, Hefei, China, 230000

  29. Removing Temporal Note Redundancy Improves Multimodal Reinforcement Learning for Medicine

    Aug 14, 2026Chenran Weng, Joo Seung Lee, Malini Mahendra +1Reinforcement LearningRepresentation Learning for RL

    University of California, Berkeley Berkeley, CA, USA · University of California, San Francisco San Francisco, CA, USA

  30. Flowing Through States: Neural ODE Regularization for Reinforcement Learning

    Aug 6, 2026Mohamed Ghanem, Bernd FinkbeinerLatent Dynamics ModelingRepresentation Learning for RL

    CISPA Helmholtz Center for Information Security · Technical University of Munich