Successor Representations

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

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Robust Successor Features
Sep 25, 2026Erik Nikulski, Yamen Habib, Vicenç Gomez +3Reinforcement Learning Generalization in RL
Department of Engineering Universitat Pompeu Fabra
Constrained Reinforcement Learning Using Successor Representations
Jul 27, 2026Michael Girstl, Alexander Mattick, Christopher MutschlerConstrained RL Safe RL
Technical University of Darmstadt (TU Darmstadt) · Hessian Center for Artificial Intelligence (hessian.AI) · Fraunhofer Institute for Integrated Circuits IIS, Fraunhofer IIS +1
From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
May 31, 2026Jun-Jie Yang, Chia-Heng Hsu, Kui-Yuan Chen +1Pairwise Preference Learning Representation Learning
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
The Terminal Representation in Reinforcement Learning
May 29, 2026Amir Esterhuysen, Anders JonssonRepresentation Learning for RL Trajectory Representation Learning
Dept. Information and Communication Technologies Universitat Pompeu Fabra Barcelona, Spain
Word-Class and Construction-Like Structure Emerges in Neural Successor Representations Trained on Natural Language
May 23, 2026Mathis Immertreu, Achim Schilling, Thomas Kinfe +1Predictive Representation Learning Representation Geometry in Language Models
Cognitive Computational Neuroscience Group, Friedrich-Alexander-Universität Erlangen–Nürnberg (FAU), Germany. · Mannheim Center for Neuromodulation and Neuroprosthetics (MCNN), University Hospital Mannheim, University Heidelberg, Germany.
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
Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies
May 12, 2026Ethan Parks, Dalal AlharthiMulti-Agent Systems Multi-Agent Reasoning
University of Arizona
Hierarchical Active Inference using Successor Representations
Apr 17, 2026Prashant Rangarajan, Rajesh P. N. RaoActive Inference Hierarchical RL
Paul G. Allen Center for Computer Science and Engineering, University of Washington, Seattle, USA
Saliency-Guided Representation with Consistency Policy Learning for Visual Unsupervised Reinforcement Learning
Apr 7, 2026Jingbo Sun, Qichao Zhang, Songjun Tu +5Reinforcement Learning Visual RL
SKL-MAIS, Institute of Automation, Chinese Academy of Sciences · Pengcheng Laboratory · School of Artificial Intelligence, University of Chinese A
Full-Gradient Successor Feature Representations
Apr 1, 2026Ritish Shrirao, Aditya Priyadarshi, Raghuram Bharadwaj DiddigiReinforcement Learning Successor Representations
Dept. of CSE, IIIT Bangalore · Dept. of DSAI, IIIT Bangalore
Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
Mar 20, 2026Seyed Mahdi B. Azad, Jasper Hoffmann, Iman Nematollahi +3Reinforcement Learning Representation Learning for RL
Department of Computer Science, University of Freiburg, Germany
Can We Really Learn One Representation to Optimize All Rewards?
Feb 11, 2026Chongyi Zheng, Royina Karegoudra Jayanth, Benjamin EysenbachUnsupervised Learning Representation Learning
Princeton University

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Robust Successor Features

Erik Nikulski, Yamen Habib, Vicenç Gomez, Anders Jonsson+2

Department of Engineering Universitat Pompeu Fabra

Reinforcement Learning Generalization in RL Successor Representations Robust RL Sequential Decision Making

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Generalization in Reinforcement Learning (RL) refers to the ability to execute close-to-optimal policies in unseen tasks after the agent has been trained on a different set of tasks. Building on the seminal work of the successor representation and further adaptations with function approximation, Transfer in RL has traditionally focused on generalizing to tasks that only differ in the reward function. A decade after the introduction of the successor representation, Robust RL emerged simultaneously from several articles in the field of operations research. In Robust RL, the transition kernel is unknown, and the goal is to maximize the expected reward under this uncertainty. Our work unifies these two paradigms through robust successor features, which generalize across both the reward function and the transition kernel, under the assumption that tasks are linear Markov Decision Processes. We derive a bound on Generalized Policy Improvement (GPI) that explicitly quantifies how performance degrades with the mismatch between transition kernels, recovering existing successor-feature guarantees when dynamics are shared. Finally, the generalization capabilities of robust successor features are validated on several grid-based benchmarks and compared to previous alternatives that focus solely on either the reward or the transition kernel.