Value iteration and rolling plans for Markov control processes with unbounded rewards
The purpose is to extend known results for discounted Markov decision processes to the convergence of the value-iteration and the existence of error bounds for rolling horizon procedures to the case of a general state space and unbounded rewards. Now the error bounds are pointwise [w.r.t. the initial states] in contrast to the known uniform bounds. Uniformness is then obtained by using weighted norms. Further, under a strong ergodicity condition the bounds can be improved. The condition assumes a positive measure as lower bound for the distributions of states.
scientific article; zbMATH DE number 399094