Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets 事件

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 simp