Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
arXiv:2606.10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints. These features make it difficult to use standard deep reinforcement learning (DRL) algorithms, whose action interfaces typically assume either a fixed finite action catalog or a simple Euclidean space. Motivated by a Taylor expansion of the optimal action-value function, we propose Bellman--Taylor score decoding, a framework that moves policy learning to a Euclidean score space whil
This research addresses a fundamental limitation in applying deep reinforcement learning to real-world operational problems where action sets are complex and state-dependent, a prevalent condition in many current AI applications.
The proposed Bellman-Taylor score decoding framework could significantly expand the applicability of DRL to more nuanced and constrained environments, enhancing AI's ability to automate complex decision-making in operations.
This framework offers a new method for policy learning in reinforcement learning, shifting from direct action space to a Euclidean score space, which allows DRL to handle state-dependent constraints more effectively.
- · Operations research practitioners
- · Logistics and supply chain management
- · AI platform developers
- · Manufacturing and industrial automation
- · Companies relying on simpler, less adaptable DRL algorithms
Improved efficiency and autonomy in complex operational systems through advanced DRL applications.
Increased adoption of AI in industries previously limited by the rigidity of standard DRL action interfaces.
Potential for new AI-driven business models that leverage highly adaptive decision-making in dynamically constrained environments.
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Read at arXiv cs.AI