This paper extends the core results of discrete time infinite horizon dynamic programming to
the case of state-dependent discounting. We obtain a condition on the discount factor …

This paper addresses a class of discrete-time Markov decision processes in Borel spaces
with a finite number of cost constraints. The constrained control model considers costs of …

Reinforcement Learning (RL) enables an agent to learn control policies for achieving its
long-term goals. One key parameter of RL algorithms is a discount factor that scales down …

This paper studies discrete-time hybrid control problems where the controller's payoff
function follows a risk-sensitive discounted model. The associated discount factor can …

This paper studies constrained Markov decision processes under the total expected
discounted cost optimality criterion, with a state-action dependent discount factor that may …

This paper extends the results provided in Jasso-Fuentes et al.(Appl Math Optim 81 (2): 409–
441, 2020b) and Jasso-Fuentes et al.(Pure Appl Funct Anal 9 (3): 675–704, 2024) regarding …

We study a class of discrete-time stochastic systems composed of a large number of N
interacting objects, which are classified in a finite number of classes. The behavior of the …

We propose a novel framework for analyzing competition for opportunities. We introduce a
new type of games, called “opportunity-hunting games,” in which two players compete to …

Switching control problems for coupled finite Markov chains with a long term average cost
(ergodic) are considered as a continuation of the model in [16]. The vanishing discount …

We propose a novel framework for analyzing competition for opportunities. We introduce a
new type of games, called “opportunity-hunting games,” in which two players compete to …