Categorical Semantics of Compositional Reinforcement Learning
Authors: Georgios Bakirtzis, Michail Savvas, Ufuk Topcu
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Using a categorical point of view, we develop a knowledge representation framework for a compositional theory of RL. Our approach relies on the theoretical study of the category MDP, whose objects are Markov decision processes (MDPs) acting as models of tasks. The categorical semantics models the compositionality of tasks through the application of pushout operations akin to combining puzzle pieces. We further prove that properties of the category MDP unify concepts, such as enforcing safety requirements and exploiting symmetries, generalizing previous abstraction theories for RL. We construct a unifying compositional theory for engineering RL systems that mechanizes functional composition into subprocess behaviors. To achieve this explicit deļ¬nition of compositionality in RL, we give a symbolic and semantic interpretation of compositional phenomena of the problem or task by translating them into categorical properties. |
| Researcher Affiliation | Academia | Georgios Bakirtzis EMAIL LTCI, T el ecom Paris, Institut Polytechnique de Paris Michail Savvas EMAIL The University of Iowa Ufuk Topcu EMAIL The University of Texas at Austin |
| Pseudocode | No | The paper uses mathematical definitions, propositions, theorems, and proofs to describe its theoretical framework, but does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper mentions 'grid world (Leike et al., 2017)' as a conceptual example, but it does not use or provide concrete access information for any publicly available or open dataset used in empirical experiments. |
| Dataset Splits | No | The paper focuses on theoretical development and does not conduct experiments involving datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper presents a theoretical framework and does not report on experimental results that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental implementations that would necessitate detailing specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not present experimental results, therefore no specific experimental setup details like hyperparameter values or training configurations are provided. |