Meta-Reinforcement Learning

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 24

All topics

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving
Sep 30, 2026Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang LiuMeta-Reinforcement Learning RL for Autonomous Driving
Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · Daniel Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA 30332, USA
The Free Inference Dimension: Complexity Measure for Zero-Collision Navigation under Hypothesis Mixtures
Sep 15, 2026Luiz Carlos Castro Guedes, Edward Hermann HaeuslerReinforcement Learning Meta-Reinforcement Learning
Departamento de Informática – PUC-Rio, Brazil · Seção de Engenharia de Computação – Instituto Militar de Engenharia, Brazil
Fast-Convergent Meta-RL via Gradient-Clustered BS Sampling for Edge Caching
Sep 14, 2026Farnaz Niknia, Ping WangEdge Computing Meta-Reinforcement Learning
Lassonde School of Engineering at York University, Toronto, ON, M3J 1P3, Canada
Safe Meta-Reinforcement Learning via Information Space Reachability
Sep 14, 2026Zeyang Li, Sunbochen Tang, Navid AzizanReachability Analysis Constrained RL
Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Does Latent Context Help? A Controlled Evaluation of Inverse Reinforcement Learning in Arctic Shipping
Aug 6, 2026Vaishnav Vaidheeswaran, Dilith Jayakody, Biruk Ambaw +3Reinforcement Learning Meta-Reinforcement Learning
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada · Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, NS, Canada
From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning
Jul 21, 2026Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj DiddigiReinforcement Learning Meta-Learning
International Institute of Information Technology Bangalore, India
Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning
Jul 21, 2026Julian G. SoltesNeural Network Initialization Meta-Reinforcement Learning
Regis University, USA
Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
Jul 20, 2026Taewoon Kim, Vincent François-Lavet, Michael CochezReinforcement Learning Partially Observable RL
HumemAI, The Netherlands · Vrije Universiteit Amsterdam, The Netherlands · ELLIS Institute Finland & Abo Akademi University, Finland
Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
Jun 20, 2026Rifny Rachman, Bahrul Ilmi Nasution, Josh Tingey +3Multi-Objective Reinforcement Learning Multi-Objective Evolutionary Optimization
The University of Manchester, United Kingdom · Peak AI, Ltd, United Kingdom
Knowledge Reutilization in Meta-Reinforcement Learning
Jun 16, 2026Yuan Meng, Bo Wang, Juan de los Rios Ruiz +4Cross-Embodiment Robot Learning Meta-Reinforcement Learning
School of Computation, Information and Technology, Technical University of Munich, Munich, Germany · State Key Laboratory for Novel Software Technology, Nanjing University, Suzhou, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations
Jun 16, 2026Alejandro Posadas-Nava, Richard Linares, Minduli WijayatungaSpacecraft Rendezvous and Proximity Operations Control Barrier Functions
Researcher, AeroAstro, MIT, 77 Massachusetts Avenue, Cambridge, MA 02139-4307. · Associate Professor and Rockwell International Career Development Professor, AeroAstro, MIT, 77 Massachusetts Avenue, Cambridge, MA 02139-4307. · Assistant Professor, Department of Aerospace Engineering, University of Illinois, Urbana-Champaign, 104 S Wright St, Urbana, IL 61801.
Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
Jun 7, 2026Lixuan Jin, Bingxuan Lan, Xinyi Bao +9Meta-Reinforcement Learning Aerial Manipulation
National Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Artiﬁcial Intelligence, Nanjing University, Nanjing, China · Faculty of Robot Science and Engineering, Northeastern University, China +1
Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications
May 30, 2026Vignesh Subramanian, Subhajit Roy, Suguman BansalReinforcement Learning Meta-Reinforcement Learning
School of Computer Science, Georgia Institute of Technology, USA · Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, India
Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
May 30, 2026Fuyuan Qian, Menglong Zhang, Song Wang +1Representation Learning Offline RL
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China · AI Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China · Omni-Intelligence, Shenzhen, Guangdong, China +1
Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment
May 26, 2026Gengyue Han, Yiheng FengReinforcement Learning Sim-to-Real Transfer
Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning
May 19, 2026Yuan Zhao, Youwei Pang, Jiaming Zuo +10Image Segmentation Promptable Image Segmentation
1Dalian University of Technology, China · 3Nanyang Technological University, Singapore · 2X3000 Inspection Co., Ltd +3
Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
May 18, 2026Yaorong Huang, Jingtao Luo, Xuechao WangFederated RL Edge Computing
∗The Hong Kong University of Science and Technology (Guangzhou) · †Chengdu Neusoft University
MATE: Solving Contextual Markov Decision Processes with Memory of Accumulated Transition Embeddings
May 17, 2026Himchan Hwang, Hyeokju Jeong, Gene Chung +3Markov Decision Processes Meta-Reinforcement Learning
Seoul National University · Saige · Ulsan National Institute of Science and Technology (UNIST)
RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
May 11, 2026Gaotang Li, Bhavana Dalvi Mishra, Zifeng Wang +9Reinforcement Learning Rubric-Based RL
University of Illinois Urbana-Champaign · Google Cloud AI Research
A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
May 4, 2026Sanjiv R. Das, Harshad Khadilkar, Sukrit Mittal +3Financial Services Meta-Learning
Dept. of Finance, Santa Clara University, Santa Clara, CA, USA · Indian Institute of Technology, Mumbai, India · Franklin Templeton, Mumbai, India +4
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
Mar 3, 2026Mohammadreza Nakheai, Aidan Scannell, Kevin Luck +1Representation Learning Offline RL
Aalto University · Toyota Research Institute · Vrije Universiteit Amsterdam
Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
Jan 26, 2026Shobhita Sundaram, John Quan, Ariel Kwiatkowski +3Self-Play RL RL for Language Model Reasoning
1MIT · 2Meta FAIR · 3New York University
Meta-RL with Bayesian Linear Task Models
Dec 24, 2025Jingyang You, Hanna KurniawatiRepresentation Learning Reinforcement Learning
School of Computing Australian National University
Meta-reinforcement learning with minimum attention
May 22, 2025Shashank Gupta, Pilhwa LeeMeta-Reinforcement Learning Policy Learning
Department of EECS University of Michigan Ann Arbor, MI 48109 · Department of Mathematics Morgan State University Baltimore, MD 21251

Sep 30, 2026, cs.AIJ/K move · Enter open · S save

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu

Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · Daniel Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA 30332, USA

Meta-Reinforcement Learning RL for Autonomous Driving Multi-Agent Reinforcement Learning

Open paper AskPDF arXiv

This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level optimization mechanism. However, existing meta-RL generally focuses on single-agent systems. Extending these frameworks and algorithms to multi-agent systems poses additional challenges, as tasks are characterized by not only the environment but also agents' strategic interactions. To address these challenges, we model multi-agent reinforcement learning (MARL) problems as Markov games (MGs) and develop a meta-MARL framework for rapid interactive policy adaptation across a distribution of MGs. A new concept, called meta-NE, is defined to describe the desired solution concept in a meta-MARL problem. Sufficient conditions for the equivalence between a meta-NE and a stationary point of the gradient-play-based meta-MARL algorithm are established. Our evaluation on autonomous-driving tasks demonstrates that the proposed meta-MARL method achieves faster adaptation than pretrained MARL baselines, validating the effectiveness of our framework.

Momentum

Related topics

Latest papers 24