arXiv Machine Learning
Sep 25

Vector Bellman Theory for Multichain Robust Average-Reward Markov Decision Processes

The paper introduces a vector Bellman theory for multichain robust average‑reward Markov decision processes, addressing the state‑dependent optimal long‑run rewards that arise under uncertainty. It develops a gain‑first, bias‑second optimization principle for finite models with compact, post‑action $(s,a)$‑rectangular ambiguity, yielding a coupled vector gain‑bias system and stationary saddle strategies from all initial states. The authors also characterize solvability conditions, provide certificates for asymptotically affine trajectories of the robust Bellman operator, and design a robust approximately shifted Halpern planning algorithm that converges to the optimal gain vector and produces average‑optimal greedy controllers.

By Yue Wang, George Atia
arXiv Machine Learning
19h ago

Dissimilarity-Based Markov Decision Processes for Stabilizing State Distributions

arXiv:2610.29024v1 Announce Type: new Abstract: Reinforcement learning for stochastic systems typically optimizes expected cumulative rewards, while reliable and repeatable behavior also depends on h...

By Xun Shen, Yuepeng Wang, Daniyal Sajedi-Hosseini, Akifumi Wachi, Ken Kawano, Yoshihiko Fujisawa, Mehrshad Sadria, S\'ebastien Gros, Katsuki Fujisawa