Zero-Shot RL

RL: Reinforcement Learning

Follow this topic Ask about this topic

Latest papers

20 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Decision Transformer with Task-Aware Representations for Offline Meta-Reinforcement Learning
Nov 2, 2026Xinbo Zhao, Yuhang Liu, Xin Zhang +3Meta-Reinforcement Learning Offline RL
State University of New York at Binghamton · San Diego State University · LinkedIn
Before We Adapt: The Zero-Shot Generalization Bottleneck in Continual World Models
Oct 29, 2026Amir Mesbah, Di WuContinual RL World Model Learning
Independent Researcher · Department of Mechanical, Industrial and Aerospace Engineering, Concordia University · Department of Electrical and Computer Engineering, McGill University
PrefO2O: Zero-Shot Preference-Based Reinforcement Learning via Offline-to-Online Latent Preference Transfer
Oct 22, 2026Ruiqi Wang, Dezhong Zhao, Xuesu Xiao +3Preference-Based RL Zero-Shot RL
University of Southern California, Los Angeles, CA, USA. · Beijing University of Chemical Technology, Beijing, China. · George Mason University, Fairfax, VA, USA. +1
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
Task Inference Beyond Least Squares in Behavioral Foundation Models
Oct 4, 2026Kuan-Hsun Tu, Chien-Sheng Chiang, Hsin-Wei Chen +2Reinforcement Learning Multi-Task RL
National Taiwan University
Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation
Sep 30, 2026Morgan Byrd, Jacob Blevins, Maks Sorokin +2Generalization in RL Robot Policy Adaptation
Georgia Institute of Technology, Atlanta, GA, 30308, USA · Georgia Tech Research Institute, Atlanta, GA, 30308, USA
Towards Zero-Shot Task Transfer with Neurosymbolic World Models
Aug 18, 2026Isidoro Tamassia, Lennert De Smet, Giuseppe MarraZero-Shot RL World Model Learning
KU Leuven, Department of Computer Science, Belgium
Exploration and Online Transfer with Behavioral Foundation Models
Jun 29, 2026Louis Bagot, Mathieu Lefort, Laëtitia MatignonTransfer Learning in RL RL Exploration
SyCoSMA · Université Lyon 1, CNRS, INSA Lyon LIRIS - UMR5205, 69622 Villeurbanne, France · LIRIS, SyCoSMA, IRISA, MALT, UR +1
Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning
May 13, 2026Stefan Stojanovic, Alexandre ProutiereRepresentation Learning Reinforcement Learning
KTH, Stockholm, Sweden · KTH, Digital Futures, Stockholm, Sweden
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.
Safety Guarantees in Zero-Shot Reinforcement Learning for Cascade Dynamical Systems
Apr 12, 2026Shima Rabiei, Sandipan Mishra, Santiago PaternainZero-Shot RL Safe RL
The authors are with the Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute. · The author is with the Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute.
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
Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning
Mar 26, 2026Jiajun Hu, Nuria Armengol Urpi, Jin Cheng +1Zero-Shot RL Reinforcement Learning
Department of Mechanical Engineering, EPFL · Department of Computer Science, ETH Zurich · Max Planck Institute for Intelligent Systems
Unified Policy Value Decomposition for Rapid Adaptation
Mar 18, 2026Cristiano Capone, Luca Falorsi, Andrea Ciardiello +1Zero-Shot RL Reinforcement Learning
Computational Neuroscience Unit, Istituto Superiore di Sanit`a, 00161, Rome, Italy · Ospedale Santa Lucia, Rome, Italy · School of Computer Science, University of Sheffield,Sheffield,S10 2TN, United Kingdom
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Mar 16, 2026Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal +3Behavioral Foundation Models Zero-Shot RL
Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (Amii) · The University of Texas at Austin +2
Contextual Intelligence The Next Leap for Reinforcement Learning
Feb 17, 2026André BiedenkappContextual RL Zero-Shot RL
Albert-Ludwigs-Universität Freiburg, Germany
Can We Really Learn One Representation to Optimize All Rewards?
Feb 11, 2026Chongyi Zheng, Royina Karegoudra Jayanth, Benjamin EysenbachZero-Shot RL Representation Learning
Princeton University
A Unified Framework for Zero-Shot Reinforcement Learning
Oct 23, 2025Jacopo Di Ventura, Jan Felix Kleuker, Aske Plaat +1Zero-Shot RL Formal Methods for RL
Leiden 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

Nov 2, 2026J/K move · Enter open · S save

Decision Transformer with Task-Aware Representations for Offline Meta-Reinforcement Learning

Xinbo Zhao, Yuhang Liu, Xin Zhang, Lei Le+2

State University of New York at Binghamton · San Diego State University · LinkedIn

Meta-Reinforcement Learning Offline RL Transformer-Based RL Representation Learning for RL Zero-Shot RL LLM-Guided RL

Open paper AskPDF arXiv

To enable effective generalization in offline meta-RL, it is essential to accurately infer the underlying task through latent task representations, which provide a compact and transferable summary of task-specific characteristics for guiding policy generation. In this paper, we propose Contrastive Task-Aware Decision Transformer (CoTA-DT), a DT-based offline meta-RL framework that learns task representations by jointly integrating four complementary components: (i) contrastive learning from offline trajectories, (ii) alignment with natural-language task descriptions, (iii) one-step supervision via a task-conditioned world model, and (iv) long-term supervision via a task-conditioned Q function. The learned task representations are used as conditioning signals for a DT-based policy, enabling task-aware meta-policy learning. To further support generalization in both zero-shot and few-shot settings, we design a language-grounded, self-adaptive strategy that enables effective and reliable adaptation to unseen tasks. Notably, in CoTA-DT, the pretrained LLM serves as a shared backbone for language encoding, world modeling, Q-function estimation, and policy learning. This unified design facilitates parameter sharing, improving both efficiency and generalization. Extensive experiments across diverse offline meta-RL benchmarks demonstrate that CoTA-DT consistently outperforms prior methods, particularly in challenging zero-shot generalization scenarios.