Speaker
Chen Yan
Description
We explore a general reinforcement learning framework within a Markov decision process (MDP) consisting of a large number $N$ of independent sub-MDPs, linked by global constraints. In the non-learning scenario, when the model meets a specific non-degenerate condition, efficient algorithms (i.e., polynomial in $N$) exist, achieving a performance gap smaller than $\sqrt{N}$ relative to the linear program upper bound. Analyzing the learning scenario in relation to this upper bound forms the central topic of this work.
