Optimal Policies for Quantum Markov Decision Processes

Optimal Policies for Partially Observable Markov Decision Processes

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...

Discovery of Structured Optimal Policies in Markov Decision Processes

In this chapter we continue work on vfd, the novel method for discovery of relative value functions for Markov Decision Processes that we introduced in Chapter 6. vfd discovers algebraic descriptions of relative value functions using ideas from the Evolutionary Algorithm eld and, in particular, these descriptions include the model parameters of the mdp. We extend that work and demonstrate how a...

Constrained Markov Decision Process and Optimal Policies

In the course lectures, we have discussed a lot regarding unconstrained Markov Decision Process (MDP). The dynamic programming decomposition and optimal policies with MDP are also given. However, in this report we are going to discuss a different MDP model, which is constrained MDP. There are many realistic demand of studying constrained MDP. For instance, in the wireless sensors networks, each...

Accelerated decomposition techniques for large discounted Markov decision processes

Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorith...