Goal-Conditioned RL

RL: Reinforcement Learning

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13 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning
Jun 10, 2026Marko Cvjetko, Benedikt Hartl, Michael Levin +2Agentic RL Reinforcement Learning
Inria Centre at the University of Bordeaux, Bordeaux, France · Allen Discovery Center at Tufts University, Medford, MA, USA · Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA +1
Learning Object Manipulation from Scratch via Contrastive Interaction
Jun 10, 2026Tongle Shen, Caleb Chuck, Fan Feng +1Dynamical Systems Reinforcement Learning
1UC San Diego · 2UT Austin
Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling
Jun 8, 2026Carlos Vélez García, Miguel Cazorla, Jorge PomaresOffline RL Goal-Conditioned RL
INESCOP · University of Alicante
Dual Advantage Fields
Jun 2, 2026Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin +5Offline RL Goal-Conditioned RL
1NUST MISIS · 2MSU · 3Computational Imaging Lab +3
Short-Term Synaptic Plasticity Stabilizes Goal-Conditioned Dynamics in a PFC-Inspired Reservoir Model for Multistep Goal-Directed Action Planning
Jun 2, 2026Jin Nakamura, Yuichi KatoriComputational Cognitive Modeling Reservoir Computing
Graduate School of Information Science and Technology, The University of Osaka, 1-5 Yamadaoka, Suita, 565-0871, Osaka, Japan · School of Systems Information Science, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate, 041-8655, Hokkaido, Japan
A Goal-Set Characterization of Task Composition in the Boolean Task Algebra
Jun 2, 2026Eduardo Terrés-Caballero, Herke van HoofReinforcement Learning Goal-Conditioned RL
Informatics Institute, University of Amsterdam · AMLab, University of Amsterdam
Survival Reinforcement Learning: Toward Scalable Self-Supervised RL
May 29, 2026Franki Nguimatsia-Tiofack, Fabian Schramm, Théotime Le Hellard +1Reinforcement Learning Goal-Conditioned RL
Inria and École Normale Supérieure PSL Research University Paris, France
Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
May 28, 2026Vittorio Giammarino, Anastasios Manganaris, Ahmed H. QureshiReinforcement Learning Contact-Rich Robotic Manipulation
Department of Computer Science Purdue University
Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
May 27, 2026Kaiqiang Ke, Shenghong He, Chengdong Xu +3Hierarchical RL Offline RL
1Sun Yat-sen University · 2Pengcheng Laboratory
Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
May 25, 2026Hyungkyu Kang, Byeongchan Kim, Min-hwan OhReinforcement Learning Offline RL
Seoul National University, Seoul, South Korea.
Goal-Conditioned Agents that Learn Everything All at Once
May 22, 2026Michael Matthews, Matthew Jackson, Michael Beukman +5Policy Learning Sample-Efficient RL
University of Oxford · 2McGill University · 3MIT +1
Abstraction for Offline Goal-Conditioned Reinforcement Learning
May 21, 2026Clarisse Wibault, Alexander Goldie, Antonio Villares +2Hierarchical RL State Abstraction
FLAIR, MLRG · University of Oxford · FLAIR +1
Compositional Transduction with Latent Analogies for Offline Goal-Conditioned Reinforcement Learning
May 20, 2026Junseok Kim, Dohyeong Kim, Mineui Hong +1Reinforcement Learning Robotic RL
Department of Electrical and Computer Engineering and ASRI, Seoul National University · Independent researcher · Robotics Institute, Carnegie Mellon University
Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation
May 13, 2026Asim Osman, Sasha Abramowitz, Mark Bergh +13Reinforcement Learning Contrastive RL
InstaDeep, AIMS · InstaDeep, University of Stellenbosch
Driving Intents Amplify Planning-Oriented Reinforcement Learning
May 12, 2026Hengtong Lu, Victor Shea-Jay Huang, Chengmin Yang +4RL for Autonomous Driving Goal-Conditioned RL
Li Auto · Tsinghua University · CUHK MMLab
MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning
May 10, 2026Valliappan Chidambaram Adaikkappan, Sai Rajeswar, Pietro Mazzaglia +1Offline RL Predictive Representation Learning
Mila, McGill University · Qualcomm Research
Functional Graphs for Predicting and Explaining Goal Failure in Sparse Goal-Conditioned RL
May 10, 2026Shalley DashReinforcement Learning Sparse-Reward RL
Associate Adjunct Professor, Business Analytics, Institute of Management Technology Ghaziabad, 201001
Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
May 7, 2026Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan +1Reinforcement Learning Agent Skill Learning
Helmholtz Munich, Germany · Max Planck Institute for Biological Cybernetics, Germany · Princeton University, USA +1
Middle-mile logistics through the lens of goal-conditioned reinforcement learning
May 4, 2026Onno Eberhard, Thibaut Cuvelier, Michal Valko +1Vehicle Routing Goal-Conditioned RL
Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Tübingen · Google Research +1
QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
May 3, 2026Xing Lei, Jincheng Wang, Xuetao Zhang +1Offline RL Sparse-Reward RL
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Computer Science, University College London · School of Engineering, Westlake University
PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations
Apr 30, 2026Yang Zhang, Jiangyuan Zhao, Chenyou Fan +11Reinforcement Learning Long-Horizon Robotic Manipulation
Institute of Artificial Intelligence (TeleAI), China Telecom · Tsinghua University · Shanghai Jiao Tong University +1
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
Apr 29, 2026Mahya Ramezani, Holger VoosReinforcement Learning UAV Navigation
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Apr 28, 2026Nazim Bendib, Nicolas Perrin-Gilbert, Olivier SigaudReinforcement Learning Offline RL
Institut des Systèmes Intelligents et de Robotique, ISIR, Sorbonne Université, Paris, France.
Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Apr 22, 2026Aravind Venugopal, Jiayu Chen, Xudong Wu +3Reward Shaping Temporal Credit Assignment
Carnegie Mellon University · The University of Hong Kong · INFIFORCE Intelligent Technology +1
Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning
Apr 22, 2026Zoya Volovikova, Nikita Sorokin, Dmitriy Lukashevskiy +2Reinforcement Learning LLM-Guided RL
1AXXX · 2MIRAI
SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
Apr 19, 2026Franki Nguimatsia Tiofack, Fabian Schramm, Théotime Le Hellard +1Reinforcement Learning Goal-Conditioned RL
Inria and École Normale Supérieure, PSL Research University, Paris, France.
SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space
Mar 19, 2026Huanrong Liu, Chunlin Tian, Tongyu Jia +8Offline RL Trajectory Prediction
Faculty of Science and Technology, University of Macau, Macau, China · University of Macau Advanced Research Institute in Hengqin, Zhuhai, China · Senior Department of Urology, The Chinese PLA General Hospital, Beijing, China +4
Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation
Feb 9, 2026Marc Toussaint, Cornelius V. Braun, Armand Jordana +5Reinforcement Learning Non-Prehensile Manipulation
Technische Universität Berlin · Robotics Institute Germany · LAAS-CNRS +1
Beyond Success Rates: Trainability and Extractability for Offline GCRL
Feb 5, 2026Jan Malte Töpperwien, Aditya Mohan, Marius LindauerRL Benchmarks Offline RL
Institute of Artificial Intelligence (LUH|AI), Leibniz University Hannover · L3S Research Center
Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL
Feb 3, 2026Jinwoo Choi, Sang-Hyun Lee, Seung-Woo SeoReinforcement Learning Hierarchical RL
Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea · Department of Automotive Engineering, Ajou University, Gyeonggi-do, South Korea

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The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning

Marko Cvjetko, Benedikt Hartl, Michael Levin, Clément Moulin-Frier+1

Inria Centre at the University of Bordeaux, Bordeaux, France · Allen Discovery Center at Tufts University, Medford, MA, USA · Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA+1

Agentic RL Reinforcement Learning Cellular Automata Goal-Conditioned RL Autonomous Experimentation

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Existing methods for exploring cellular automata and other complex systems mostly operate in open loop: they set initial conditions, execute a full simulation, and observe the outcome, without intervening during execution. We introduce a closed-loop framework based on autotelic reinforcement learning, in which an agent autonomously samples diverse goals and learns a goal-conditioned policy to intervene in a complex system through minimal, local perturbations. We instantiate this framework on Lenia, a continuous cellular automaton known for life-like self-organizing patterns, in an agentic system we call CARL, and demonstrate three capabilities. First, CARL discovers stable solitons across a wide range of Lenia update rules at a higher rate than heuristic baselines. Second, it learns to steer the movement direction of existing solitons with few interventions, showing that CARL can control self-organizing patterns, not only create them. Third, humans can use trained agents to guide solitons through maze environments in real time by specifying high-level directional commands that the agent translates into low-level interventions. Trained across diverse goals, update rules, and random initial states, the agents acquire policies that generalize zero-shot to various out-of-distribution conditions. These results suggest a path toward artificial experimentalist agents that, autonomously or with human guidance, discover and control emergent phenomena in complex systems.

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