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Building on the receding horizon approach by Hernandez-Lerma andLasserre in solving Markov decision processes (MDPs),this paper first analyzes the performance of the (approximate) receding horizon approach in terms of ...

We present a novel algorithm, called ``Simulated Annealing Multiplicative Weights", for approximately solving large finite-horizon stochastic dynamic programming problems. The algorithm is ``asymptotically efficient" in ...

This paper proposes a simple analytical model called M time-scale MarkovDecision Process (MMDP) for hierarchically structured sequential decision making processes, where decisions in each level in the M-level hierarchy are ...

We consider a receding horizon approach as an approximate solution totwo-person zero-sum Markov games with infinite horizon discounted costand average cost criteria. <p>We first present error bounds from the optimalequilibrium ...

Based on recent results for multi-armed bandit problems, we propose an adaptive sampling algorithm that approximates the optimal value of a finite horizon Markov decision process (MDP) with infinite state space but finite ...

We propose a novel algorithm called Evolutionary Policy Iteration (EPI) for solving infinite horizon discounted reward Markov Decision Process (MDP) problems. EPI inherits the spirit of the well-known PI algorithm but ...