Stochastic first-order methods for average-reward Markov decision processes. (arXiv:2205.05800v1 [cs.LG])

Web: http://arxiv.org/abs/2205.05800

We study the problem of average-reward Markov decision processes (AMDPs) and
develop novel first-order methods with strong theoretical guarantees for both
policy evaluation and optimization. Existing on-policy evaluation methods
suffer from sub-optimal convergence rates as well as failure in handling
insufficiently random policies, e.g., deterministic policies, for lack of
exploration. To remedy these issues, we develop a novel variance-reduced
temporal difference (VRTD) method with linear function approximation for
randomized policies along with optimal convergence guarantees, and an
exploratory variance-reduced temporal …

arxiv decision markov processes stochastic