MARKOV DECISION PROCESSES WITH RANDOM HORIZON

Finite-horizon variance penalised Markov decision processes

We consider a finite horizon Markov decision process with only terminal rewards. We describe a finite algorithm for computing a Markov deterministic policy which maximises the variance penalised reward and we outline a vertex elimination algorithm which can reduce the computation involved.

Finite-Horizon Markov Decision Processes with Sequentially-Observed Transitions

Markov Decision Processes (MDPs) have been used to formulate many decision-making problems in science and engineering. The objective is to synthesize the best decision (action selection) policies to maximize expected rewards (or minimize costs) in a given stochastic dynamical environment. In this paper, we extend this model by incorporating additional information that the transitions due to act...

Finite-Horizon Markov Decision Processes with State Constraints

Markov Decision Processes (MDPs) have been used to formulate many decision-making problems in science and engineering. The objective is to synthesize the best decision (action selection) policies to maximize expected rewards (minimize costs) in a given stochastic dynamical environment. In many practical scenarios (multi-agent systems, telecommunication, queuing, etc.), the decision-making probl...

Markov decision processes with time-varying discount factors and random horizon

The complexity of unobservable nite - horizon Markov decision processes

Markov Decision Processes (MDPs) model controlled stochastic systems. Like Markov chains, an MDP consists of states and probabilistic transitions; unlike Markov chains, there is assumed to be an outside controller who chooses an action (with its associated transition matrix) at each step of the process, according to some strategy or policy. In addition, each state and action pair has an associa...