H. Chang

Transformation of Adaptive Multistage Sampling for Solving Finite-Horizon Markov Decision Processes with Unknown Model

It is shown that AMR is asymptotically optimal such that the sequence of the expected absolute errors approaches zero and its convergence rate depends on the number of visits to each reachable state at each stage from the initial state, essentially transforming the result of AMS into the RL setting.