A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

Age-of-information (AoI) based minimization problems have been widely considered in
Internet-of-Things (IoT) networks with the settings of multi-source single-channel systems …

In this paper, we propose an approximate relative value learning (ARVL) algorithm for non-
parametric MDPs with continuous state space and finite actions and average reward …

In this letter, we establish the convergence of fitted value iteration and fitted Q-value iteration
for continuous-state continuous-action Markov decision problems (MDPs) with state …

Recursive stochastic algorithms have gained significant attention in the recent past due to
data-driven applications. Examples include stochastic gradient descent for solving large …

In many branches of engineering, Banach contraction mapping theorem is employed to
establish the convergence of certain deterministic algorithms. Randomized versions of these …

It has long been a challenging problem to design algorithms for Markov decision processes
(MDPs) with continuous states and actions that are provably approximately optimal and can …

复杂多变的战场环境要求后装保障能够根据战场环境变化, 预见性地做出决策. 为此,
提出基于强化学习的动态调度方法. 为准确描述保障调度问题, 提出支持抢占调度 …

Markov decision processes are widely used for modeling sequential decision-making
problems under uncertainty. We propose an online algorithm for solving a class of average …

This dissertation studies decentralized multi-agent collision avoidance and reinforcement
learning (RL) for Markov Decision Process (MDP) with state-dependent action constraints …