arXiv Machine Learning

Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries

The paper introduces a reinforcement learning framework that uses groupoids to model local, state-dependent symmetries, allowing agents to discover equivalence structures during interaction. By maintaining orbit representatives and transporters that map raw states to canonical forms, learning and decision-making occur in a symmetry-reduced space while preserving local distinctions. Experiments show that this approach improves sample efficiency and convergence in dense, large-scale environments with partial symmetries, outperforming standard Q‑learning.

arXiv AI

Sep 24

Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving

The paper proposes using category theory to structure reinforcement learning in high‑dimensional, partially observable environments. By partitioning the state space into equivalence classes (symmetry orbits) and treating each class as a groupoid with a canonical representative, the agent can share learning across similar states, reducing redundancy and improving sample efficiency. Experiments on partially observable benchmarks show that this orbit‑based partitioning consistently enhances performance in environments with latent symmetry.

By Ben Opperman, Eduardo Alonso, Esther Mondrag\'on

agents reinforcement-learning benchmarks

arXiv Machine Learning

4d ago

Sample Complexity of Equivariant Reinforcement Learning

arXiv:2609.36421v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a powerful framework for robotic control, yet its practical application is often hindered by high sample complexity. T...

By Rayan Mazouz, Haibo Zhao, Chris Hillar, Christian Shewmake

reinforcement-learning robotics

arXiv Machine Learning

Jun 5

Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control

arXiv:2605. 23415v2 Announce Type: replace Abstract: Reinforcement learning has long struggled with poor sample efficiency.

By Shuai Zhen, Yifan Zhang, Yuling Wang, Yanhua Yu

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 2

Task-Induced Representational Invariances Depend on Learning Objective in Deep RL

arXiv:2606. 01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience.

By Manu Srinath Halvagal, Sebastian Lee, SueYeon Chung

llms reinforcement-learning fine-tuning

Hugging Face Trending Papers

Jul 13

SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics priors in the learning process.

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jun 17

Performance-Driven Environment Abstraction with Multi-Timescale Learning

arXiv:2606. 17377v1 Announce Type: new Abstract: We study performance-driven environment abstraction for decision-making in large Markov decision processes.

By Yue Guan, Dipankar Maity, Panagiotis Tsiotras

reinforcement-learning

arXiv Machine Learning

Jul 28

Sparse Gaussian-Mixture-Model Q-Functions via Hadamard Overparametrization for Online Reinforcement Learning

arXiv:2607. 23474v1 Announce Type: new Abstract: This paper develops an online, off-policy policy-iteration framework for reinforcement learning (RL), based on sparse Gaussian-mixture-model Q-functions (S-GMM-QFs).

By Minh Vu, Konstantinos Slavakis

reinforcement-learning safety

Hugging Face Trending Papers

Jul 26

Sparse Gaussian-Mixture-Model Q-Functions via Hadamard Overparametrization for Online Reinforcement Learning

This paper develops an online, off-policy policy-iteration framework for reinforcement learning (RL), based on sparse Gaussian-mixture-model Q-functions (S-GMM-QFs). The framework reconciles streaming, non-stationary data with the Riemannian structure of the parameter space while handling distributional mismatch through experience replay.

reinforcement-learning safety

arXiv Machine Learning

Jul 14

SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

arXiv:2607. 11624v1 Announce Type: cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency.

By Evelyn D'Elia, Weishu Zhan, Giulio Turrisi, Giulio Romualdi, Giuseppe L'Erario, Raffaello Camoriano, Wei Pan, Daniele Pucci

agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 8

Learning The Minimum Action Distance

arXiv:2506. 09276v4 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent.

By Lorenzo Steccanella, Joshua B. Evans, \"Ozg\"ur \c{S}im\c{s}ek, Anders Jonsson

rag agents reinforcement-learning

arXiv Machine Learning

Jun 9

Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions

arXiv:2601. 22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical.

By Lingkai Kong, Anagha Satish, Hezi Jiang, Akseli Kangaslahti, Andrew Ma, Wenbo Chen, Mingxiao Song, Lily Xu, Milind Tambe

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jul 30

SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception

arXiv:2607. 26985v1 Announce Type: cross Abstract: Deep reinforcement policy learning directly in physical robots (on-robot learning) remains bottlenecked by slow wall-clock training times.

By Gabe Everett, Brice Gunter, Ryan Vander Stelt, Cleiver Ruiz-Martinez, Blake Hull, Juan Rojas

reinforcement-learning robotics benchmarks

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