Zero-Shot Policy Transfer

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14 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Sparse-TD-JEPA: Improving Zero-Shot Reinforcement Learning With Sparse Latent Task Representations
Oct 9, 2026Duaa Nakshbandi, Zhiyu An, Wan DuZero-Shot RL Reinforcement Learning
University of California, Merced
Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation
Sep 30, 2026Morgan Byrd, Jacob Blevins, Maks Sorokin +2Robot Policy Learning Robot Policy Adaptation
Georgia Institute of Technology, Atlanta, GA, 30308, USA · Georgia Tech Research Institute, Atlanta, GA, 30308, USA
Exploration and Online Transfer with Behavioral Foundation Models
Jun 29, 2026Louis Bagot, Mathieu Lefort, Laëtitia MatignonReinforcement Learning RL Exploration
SyCoSMA · Université Lyon 1, CNRS, INSA Lyon LIRIS - UMR5205, 69622 Villeurbanne, France · LIRIS, SyCoSMA, IRISA, MALT, UR +1
Decentralized Autonomous Traffic Management through Corridor Networks
Jun 22, 2026Jasmine Jerry Aloor, Aadarsh Govada, Hamsa BalakrishnanAir Traffic Management Intelligent Transportation Systems
Department of Aeronautics and Astronautics · Massachusetts Institute of Technology · Cambridge, MA, USA +2
Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation
Jun 1, 2026Zhiming Xu, Weitao Zhou, Xianghui Pan +4Robot Policy Adaptation Latent Dynamics Modeling
School of Computer Science and Engineering, Tongji University, Shanghai, China · School of Vehicle and Mobility, Tsinghua University, Beijing, China · Simple AI, Beijing, China +1
Zero-shot adaptation to order book dynamics
May 20, 2026Arip AsadulaevSequential Decision Making Stochastic Optimal Control
Structural Equivalence and Learning Dynamics in Delayed MARL
May 5, 2026Jules Sintes, Ana Bušić, Jiamin ZhuPartially Observable Markov Decision Processes Zero-Shot Policy Transfer
Inria · DIENS, École Normale Supérieure, PSL University · Paris, France +2
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Apr 28, 2026Nazim Bendib, Nicolas Perrin-Gilbert, Olivier SigaudZero-Shot RL Offline RL
Institut des Systèmes Intelligents et de Robotique, ISIR, Sorbonne Université, Paris, France.
ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfer
Apr 9, 2026Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu RanaZero-Shot RL Zero-Shot Policy Transfer
Applied Artificial Intelligence Initiative, Deakin University, Waurn Ponds, Geelong · School of IT, Deakin University, Waurn Ponds, Geelong
MorFiC: Fixing Value Miscalibration for Zero-Shot Quadruped Transfer
Mar 15, 2026Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao +1Cross-Embodiment Robot Learning RL for Legged Locomotion
University of Maryland, College Park, MD, USA · George Mason University, Fairfax, VA, USA
Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
Mar 4, 2026Thomas PravetzZero-Shot Policy Transfer Reinforcement Learning
CourtGuard: A Model-Agnostic Framework for Zero-Shot Policy Adaptation in LLM Safety
Feb 26, 2026Umid Suleymanov, Rufiz Bayramov, Suad Gafarli +4Zero-Shot Policy Transfer Language Model Safety Evaluation
Department of Computer Science, Virginia Tech · School of IT and Engineering, ADA University · School of Law, ADA University
MAGIK: Mapping to Analogous Goals via Imagination-enabled Knowledge Transfer
Jun 2, 2025Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu RanaReinforcement Learning Transfer Learning
Applied Artificial Intelligence Initiative, Deakin University · School of IT, Deakin University
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
Oct 4, 2024Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan +1Reinforcement Learning RL Exploration
Delft University of Technology, Delft, The Netherlands

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

Duaa Nakshbandi, Zhiyu An, Wan Du

University of California, Merced

Zero-Shot RL Reinforcement Learning Zero-Shot Policy Transfer Representation Learning for RL

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Successor-feature-based zero-shot reinforcement learning methods pre-train a task encoder and successor-feature predictor on offline, reward-free data, then recover a policy at test time by solving a closed-form linear regression over a small labeled dataset. We identify an underexplored limitation of this approach: when the task encoder produces dense representations, the inferred reward signal becomes diffuse across the state space, reducing the contrast between goal-relevant states and the background and providing a weaker signal for the policy to follow. We address this limitation by introducing an l1 sparsity penalty that concentrates reward-predictive structure into a smaller effective subspace of the task encoder's latent space. The penalty is applied only during training and requires no architectural change or modification to the closed-form inference procedure. We instantiate this approach as Sparse-TD-JEPA, a single-line of code addition based on Temporal-Difference Joint-Embedding Predictive Architecture (TD-JEPA) that significantly improved its performance in zero-shot reinforcement learning. We evaluate Sparse-TD-JEPA on 65 tasks across the DeepMind Control (DMC) Suite and OGBench, covering locomotion, navigation, trajectory stitching, puzzle solving, and robotic manipulation. Sparse-TD-JEPA improves over TD-JEPA in all 13 domain-level comparisons. On DMC, it increases the average return from 661 +- 6 to 714 +- 3. On OGBench, it increases the average success rate from 38.0 +- 0.8 to 48.4 +- 2.3, with improvements of 13.4-18.0 percentage points on the evaluated antmaze navigation and stitching domains. Overall, Sparse-TD-JEPA achieves the highest mean performance among established zero-shot baselines in nine of the 13 domains. Analysis further shows that sparsity produces a sharper and more task-informative inferred reward landscape.