Computing monotone policies for Markov decision processes: a nearly-isotonic penalty approach

Monotone optimal policies in discounted Markov decision processes with transition probabilities independent of the current state: existence and approximation

A LEARNING ALGORITHM FOR COMMUNICATING MARKOV DECISION PROCESSES WITH UNKNOWN TRANSITION MATRICES by

This study is concerned with finite Markov decision processes (MDPs) whose state are exactly observable but its transition matrix is unknown. We develop a learning algorithm of the reward-penalty type for the communicating case of multi-chain MDPs. An adaptively optimal policy and an asymptotic sequence of adaptive policies with nearly optimal properties are constructed under the average expect...

On the Class of Closed Dynamic Programs

This paper considers a class of general dynamic programs which satisfies the mono tonicity and contraction assumption, and in which the sets of cost functions and policies are closed under the monotone contraction operators. This class of dynamic programs includes, piecewise linear, affine dynamic programs, partially observable Markov decision processes, and many sequential decision processes u...

Utilizing Generalized Learning Automata for Finding Optimal Policies in MMDPs

Multi agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a generalized learning automata based algorithm for finding optimal policies in MMDP is proposed. In the proposed algorithm, MMDP ...

Identification of optimal policies in Markov decision processes

In this note we focus attention on identifying optimal policies and on elimination suboptimal policies minimizing optimality criteria in discrete-time Markov decision processes with finite state space and compact action set. We present unified approach to value iteration algorithms that enables to generate lower and upper bounds on optimal values, as well as on the current policy. Using the mod...