arXiv:2605.24759v1 Announce Type: new Abstract: Discounted reinforcement learning is usually presented through Bellman equations on closed Markov decision processes. This paper develops a compositional view: a one-step decision process is treated as an open stochastic component, and infinite-horizon policy evaluation is obtained by closing a contractive feedback loop. The resulting semantics assigns typed Bellman transformers to open components, interprets series and parallel wiring as composition and tensoring of transformers, and interprets feedback as an admissible guarded Banach trace real
The continuous evolution of AI research pushes for more robust and compositional theoretical frameworks to handle increasingly complex learning environments.
A more compositional and theoretically sound understanding of reinforcement learning could lead to more efficient, reliable, and scalable AI agents, impacting various industries.
This research provides a new theoretical lens for understanding and developing reinforcement learning algorithms, moving from closed systems to open, compositional ones.
- · AI researchers
- · Developers of reinforcement learning systems
- · Sectors reliant on autonomous AI agents
- · Developers relying solely on ad-hoc RL approaches
Improved theoretical grounding for advanced reinforcement learning systems.
Faster development and deployment of more robust and less error-prone AI agents across various applications.
Enhanced AI capabilities leading to fundamental shifts in automation and decision-making systems, potentially accelerating progress in autonomous AI agents.
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Read at arXiv cs.LG