Goal-Conditioned RL

RL: Reinforcement Learning

Goal-conditioned reinforcement learning studies how an agent can use a desired state or outcome to choose actions, so one learned policy can handle many tasks. Common approaches include hindsight experience replay, contrastive and self-supervised learning, and offline learning from recorded trajectories; hierarchical methods add intermediate goals for long-horizon tasks. Researchers evaluate these methods on maze navigation, locomotion, and manipulation benchmarks such as OGBench and D4RL, with some work extending to physical robots. A central challenge is making goals and value estimates generalize to difficult or unseen targets without losing reliable progress in long-horizon, sparse-reward, or contact-rich settings.

Latest papers

94 papers22 papers in the last four weeks, up 144% on the four weeks before. 0.1% of all new papers.

Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning
Oct 29, 2026Junhyeon Bae, Jaebak Hwang, Gwanwoo Choi +2Offline RL Goal-Conditioned RL
Graduate School of Artificial Intelligence, UNIST, Ulsan, South Korea
VPFlow: Value-Predictive Planning with Imperfect World Models for Offline Goal Reaching
Oct 19, 2026Yunzhe Hu, Dong XuOffline RL Model-Based Planning
School of Computing and Data Science, The University of Hong Kong
VaDar: Implicit Goal Conditioning via Value Disaggregation
Oct 19, 2026Shashwat Saxena, Mehul Goel, Sreyas Venkataraman +2Long-Horizon RL Reinforcement Learning
Carnegie Mellon University
Reinforcement Learning Controllers for Fusion Scenario Development
Oct 19, 2026Evgeny Adishchev, Maxim Nurgaliev, Dmitry Sorokin +5Feedback Control RL Control
Next Step Fusion s.a.r.l.
BeJEPA: A Goal-Conditioned Humanoid Behavior World Model via Unsupervised RL
Oct 16, 2026Liyun Yan, Haoyang Li, Yang Zhang +11Humanoid Robot Control World Models for Robotics
RL2 Lab, Shanghai Jiao Tong University · Shanghai Innovation Institute · Zhejiang University +3
Outperforming 1000 Euclidean Layers With Hyperbolic Goal-Conditioned RL
Oct 14, 2026Timo Klein, Thomas Lang, Andrii Shkabrii +3Reinforcement Learning Contrastive RL
Faculty of Computer Science, University of Vienna, Vienna, Austria · UniVie Doctoral School Computer Science, University of Vienna, Vienna, Austria · Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany +1
Imagining Goals with Geometric Horizon Models for Policy Generalization
Oct 13, 2026Daniel Lawson, Dhruv Sreenivas, Adriana Hugessen +2Robot Policy Generalization Generalization in RL
Mila · Université de Montréal · Google DeepMind
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning
Oct 10, 2026Tingrui Huang, Ai Bo, Junzhe ZhangReinforcement Learning Offline RL
Eindhoven University of Technology, Eindhoven, The Netherlands · Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA
OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models
Oct 9, 2026Xule Gao, Xi Wang, Zehua Zang +3Vision-Language-Action Models Robotic Manipulation
National Key Laboratory of Space Integrated Information System · Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences +1
Q-Shaped Options for Hierarchical Reinforcement Learning
Oct 8, 2026Clarisse Wibault, Antoine Gorceix, Antonio Léon Villares +5Hierarchical RL Representation Learning for RL
University of Oxford
World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning
Oct 7, 2026Junwei Quan, Evgenii Opryshko, Nicholas Rhinehart +1Goal-Conditioned RL Offline RL
University of Toronto · University of Toronto, Vector Institute
An Informational Curse of Horizon in Goal-Conditioned Policy Learning
Oct 7, 2026John L. Zhou, Yuxuan Dong, Jonathan C. KaoGoal-Conditioned RL Generalization in RL
University of California, Los Angeles
Directed Temporal Representations for Offline Visual Control
Oct 6, 2026Chenyang Yuan, Haoyu Wang, Zhuo Sun +1Visual RL Trajectory Representation Learning
University of Sheffield · Shanghai Jiao Tong University · Shanghai University of Finance and Economics +1
Attacca: Goal-Directed Control under State Continuity for Long-Horizon Embodied Agents
Oct 6, 2026Gyusik Seo, Jaehong YoonLong-Horizon Agent Tasks Goal-Conditioned RL
Nanyang Technological University, Singapore
Hierarchical Time-aware Bootstrapping for Off-Policy Subgoal Value Learning
Oct 4, 2026Bingyun Liu, Yuheng JingHierarchical RL Temporal-Difference Learning
Institute of Automation Chinese Academy of Sciences
Direction-Conditioned Policies for Online Goal-Conditioned Reinforcement Learning
Oct 4, 2026S K Swaminathan, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan +1Reinforcement Learning Contrastive RL
Department of Computer Science and Engineering Indian Institute of Technology Kharagpur, India · Department of Mechanical Engineering Indian Institute of Technology Kharagpur, India
Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning
Oct 2, 2026Juri Pfammatter, Kaixian Qu, Clemens Schwarke +2Curriculum RL RL Exploration
Robotic Systems Lab, ETH Zürich, Zürich, Switzerland · NVIDIA
Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning
Oct 1, 2026Pedro Robles Dutenhefner, Dikshant Shehmar, Wagner Meira +1Reinforcement Learning Offline RL
Universidade Federal de Minas Gerais · University of Alberta · Alberta Machine Intelligence Institute (Amii) +1
Learning Goal-Reaching Quasimetric Geometry From Finite-Time Reachability
Sep 30, 2026Daisuke Yamada, Travis Pence, Vikas SinghMetric Learning Offline RL
Department of Computer Science University of Wisconsin–Madison · Department of Biostatistics and Medical Informatics University of Wisconsin–Madison
Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?
Sep 30, 2026Syed Nazmus Sakib, Abdul Monaf Chowdhury, Nafiul Haque +2Offline RL Representation Learning for RL
Department of Robotics and Mechatronics Engineering, University of Dhaka, Bangladesh · Department of Computer Science, University of Oxford, UK
EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making
Sep 29, 2026Yuhan Guo, Jinming Liu, Liang Xu +8LLM Agent Training Generalization in RL
Shanghai Jiaotong University · Eastern Institute of Technology, Ningbo · Zhongguancun Academy, Beijing, China +1
ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning
Sep 28, 2026Nico Bohlinger, Jan PetersRL for Robotics Representation Learning
Technical University of Darmstadt, Germany · Robotics Institute Germany (RIG); German Research Center for AI (DFKI); hessian.AI
Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
Sep 28, 2026Hengrui Zhang, Yuhu Cheng, C. L. Philip Chen +1Diffusion-Based Planning Hierarchical RL
School of Information and Control Engineering, China University of Mining and Technology · School of Computer Science and Engineering, South China University of Technology
GTRL: Grounding Divide-and-Conquer Value Learning with Temporal Differences
Sep 27, 2026Abdul Monaf Chowdhury, MD Sameer Iqbal Chowdhury, Shifat E Arman +1Reinforcement Learning Temporal-Difference Learning
Department of Robotics and Mechatronics Engineering, University of Dhaka, Bangladesh · Department of Computer Science, Texas State University, USA · Department of Computer Science, University of Oxford, UK
PF-RL: Progress Field Reinforcement Learning via Goal-Conditioned Value Geometry for Vision-Language-Action Models
Sep 26, 2026Yunpeng Qing, Yilun Kong, Sixu Lin +9Reinforcement Learning RL for VLMs
Zhejiang University · Nanyang Technological University · CUHK-Shenzhen +7
Improving Offline Goal-Conditioned Reinforcement Learning via Selective Reward Stimulation
Sep 16, 2026Jing ZhangReward Shaping Offline RL
The Hong Kong University of Science and Technology
A note on goal-based hierarchical RL
Sep 13, 2026Kevin MurphyReinforcement Learning Hierarchical RL
VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching
Sep 13, 2026Prajwal Koirala, Mark CampbellRobot Policy Learning Robotic RL
Sibley School of Mechanical and Aerospace Engineering, Cornell University
Topological Necessities: Mechanism-Invariant Strategic Subgoals for Cross-Embodiment Goal-Conditioned Control
Sep 10, 2026Hao Shi, Xi LiHierarchical RL Cross-Embodiment Robot Learning
Army Engineering University of PLA (Shijiazhuang Campus), Shijiazhuang, China
From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs
Sep 9, 2026Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang +1Reinforcement Learning Hierarchical RL
School of Computer Science, McGill University Mila – Quebec AI Institute · Google DeepMind, CIFAR Fellow

Oct 29, 2026J/K move · Enter open · S save

Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning

Junhyeon Bae, Jaebak Hwang, Gwanwoo Choi, Minung Kim+1

Graduate School of Artificial Intelligence, UNIST, Ulsan, South Korea

Offline RL Goal-Conditioned RL Long-Horizon RL Graph-Based Planning

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Offline goal-conditioned reinforcement learning remains challenging in stochastic long-horizon settings, where compounding value estimation errors hinder reliable goal reaching. While prior methods have sought to address this challenge through various approaches, a key challenge remains path mirage, where agents overcommit to spuriously successful trajectories in offline datasets induced by stochasticity, often leading to failure on difficult tasks. To address this issue, we propose Branch-aware Graph Planning (BGP), which captures stochastic branching structures by identifying high-variability points as graph nodes and designing goal-conditioned edges with segmented learning to avoid unreliable branches and reduce unnecessary stochasticity along paths. As a result, BGP enables reliable goal reaching even in highly stochastic long-horizon environments, and experiments on diverse OGBench tasks show that it substantially outperforms prior state-of-the-art offline goal-conditioned RL methods. Our implementation is available at https://umlic.github.io/papers/BGP/.