… Since it is already relatively rare for optimal strategies to exist. it is to be expected that for
only very special problems will persistently optimal strategies exist. On the other hand, for …

… estimate ˆJ(Υ) of J(Υ) the error W(Υ)= ˆJ(Υ) − J(Υ) has a symmetric pdf, then the Markov
process {Υk} generated by the Stochastic Comparison algorithm will converge to an ϵ-optimal …

… We exhibit e-optimal Markov strategies which are stationary from some time onward. When
… for the computation of such strategies and give a description of the set of optimal strategies. …

… First, we propose a novel persistent monitoring and data collection problem, with the unique
feature that the precise arrival times of events are unknown a priori, but their statistics are …

This paper studies a zero-sum discrete-time stochastic game model with Borel state and
action spaces. The law of motion of the system in the model is assumed to be nonstationary. …

… It is shown that there is always an ε-optimal policy that allows the decision maker to learn
any identified parameters, but that there are other ε-optimal policies with very different limit …

… action correspondingly to obtain an optimal or ε-optimal strategy. An interesting consequence
of our previous results is as follows: the set of strategies that allow interventions at time t = …

… It is, however, less effective for the specification of persistent tasks such as … ϵ-optimal policies
for discounted rewards and a subclass of LTL formulae, or provide Pareto efficient policies …

This paper presents a continuous time stochastic linear quadratic (LQ) adaptive control algorithm
for completely observed linear stochastic systems with unknown parameters. Based on …

… We also prove the non-stationary optimal policy can be approximated by non-stationary ϵ−optimal
policies. In Section 5, we give an example of a recursive discount rate whenever the …