Now showing items 1-6 of 6
Approximate Receding Horizon Approach for Markov Decision Processes: Average Reward Case
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 ...
An Asymptotically Efficient Algorithm for Finite Horizon Stochastic Dynamic Programming Problems
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 ...
Multi-time Scale Markov Decision Processes
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 ...
Markov Games: Receding Horizon Approach
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 ...
An Adaptive Sampling Algorithm for Solving Markov Decision Processes
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 ...
Evolutionary Policy Iteration for Solving Markov Decision Processes
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 ...