arXiv AI

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.

arXiv Machine Learning

Sep 14

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.

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

agents reinforcement-learning

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

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 AI

Aug 5

Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning

arXiv:2608. 02993v1 Announce Type: new Abstract: (Flat) Reinforcement Learning (RL) agents face significant challenges in environments with sparse rewards that require long-horizon reasoning.

By Subrat Prasad Panda, Blaise Genest, Arvind Easwaran

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

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

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 Machine Learning

Aug 31

Aligning Agentic World Models via Knowledgeable Experience Learning

arXiv:2601.13247v2 Announce Type: replace-cross Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural ground...

By Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, Huajun Chen

llms agents fine-tuning safety

arXiv AI

Aug 25

Reward-Free Continual Adaptation for Resilient Space Robots

The paper presents a reward‑free continual learning framework for space robots that uses latent‑state world models to adapt to severe hardware degradation. By pre‑training a model‑based agent in diverse simulations, the world model learns to predict reward structure in latent space. During deployment, the observation encoder and reward predictor are frozen while only the transition dynamics are updated via unsupervised rollouts, allowing the policy to adapt using imagined trajectories without new rewards.

By Andrej Orsula, Miguel Olivares-Mendez, Carol Martinez

agents diffusion reinforcement-learning robotics

arXiv AI

Aug 10

Flowing Through States: Neural ODE Regularization for Reinforcement Learning

arXiv:2608. 06595v1 Announce Type: cross Abstract: Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states.

By Mohamed Ghanem, Bernd Finkbeiner

rag agents reinforcement-learning benchmarks

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