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 …

Markov decision processes (MDPs), also called stochastic dynamic programming, were first
studied in the 1960s. MDPs can be used to model and solve dynamic decision-making …

Massive parallel sequencing allows scientists to gather DNA sequences composed of
millions of base pairs that can be combined into large datasets and analyzed to infer …

Branching out from operations research roots of the 1950's, Markov decision processes
(MDPs) have gained recognition in such diverse fields as economics, telecommunication …

A method is presented to solve for the optimum maintenance policy of repairable power
equipment. The approach uses a continuous-time semi-Markov process (SMP) to first find …

Considerable diversity abounds among sponges with respect to reproductive and
developmental biology. Their ancestral sexual mode (gonochorism vs. hermaphroditism) …

We study reinforcement learning in non-episodic factored Markov decision processes
(FMDPs). We propose two near-optimal and oracle-efficient algorithms for FMDPs …

This paper is a survey of recent results on continuous-time Markov decision processes
(MDPs) with unbounded transition rates, and reward rates that may be unbounded from …

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 …

Bacterial bioluminescent organs in fishes have a diverse range of tissues of origin, patterns
of compartmentalization, and associated light‐conducting structures. The morphology of the …