Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving

arXiv:2609.27745 · cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/bmopper/groupoid-rl

Terminology

• Reinforcement Learning — 27× in this paper · explained in 4 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Sample Efficiency — 7× in this paper · explained in 4 episodes
• Abstraction — 5× in this paper · explained in 1 episode
• Q-learning — 5× in this paper · explained in 3 episodes
• Markov Decision Process — 4× in this paper
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Curse of Dimensionality — 2× in this paper · explained in 6 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Markov Decision Process (MDP) — 2× in this paper · explained in 11 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Sample Complexity — 2× in this paper · explained in 15 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Monte Carlo Tree Search — 1× in this paper
• Partially Observable Markov Decision Process — 1× in this paper
• Partially Observable Monte Carlo Planning — 1× in this paper
• Partially observable Markov decision process (POMDP) — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• Seven Sketches in Compositionality: An Invitation to Applied Category Theory
• Towards a Definition of Disentangled Representations
• Efficient Duple Perturbation Robustness in Low-rank MDPs
• Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition
• Constrained Reinforcement Learning for the Dynamic Inventory Routing Problem under Stochastic Supply and Demand

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