In Chap. 2, we formally introduce the concepts associated to a continuous time MDP.
Namely, the basic model of continuous-time MDPs and the concept of a Markov policy are …

For discrete-time nonlinear systems, this research is concerned with optimal control
problems (OCPs) with constrained cost, and a novel value iteration with constrained cost …

In classical Markov decision process (MDP) theory, we search for a policy that, say,
minimizes the expected infinite horizon discounted cost. Expectation is, of course, a risk …

This paper introduces and develops a new approach to the theory of continuous time jump
Markov decision processes (CTJMDP). This approach reduces discounted CTJMDPs to …

Control and optimization of dynamical systems in the presence of stochastic uncertainty is a
mature field with a large range of applications. A comprehensive treatment of such problems …

This paper considers constrained Markov control processes in Borel spaces, with
unbounded costs. The criterion to be minimized is a long-run expected average cost, and …

In this paper, we introduce the maximum casual entropy Inverse Reinforcement Learning
(IRL) problem for discrete-time mean-field games (MFGs) under an infinite-horizon …

This paper studies several average-cost criteria for Markov control processes on Borel
spaces with possibly unbounded costs. Under suitable hypotheses we show (i) the …

This paper is devoted to studying constrained continuous-time Markov decision processes
(MDPs) in the class of randomized policies depending on state histories. The transition rates …

This paper studies a discrete-time total-reward Markov decision process (MDP) with a given
initial state distribution. A (randomized) stationary policy can be split on a given set of states …