The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

We study policy gradient (PG) for reinforcement learning in continuous time and space
under the regularized exploratory formulation developed by Wang et al.(2020). We …

We approach the continuous‐time mean–variance portfolio selection with reinforcement
learning (RL). The problem is to achieve the best trade‐off between exploration and …

We propose a unified framework to study policy evaluation (PE) and the associated temporal
difference (TD) methods for reinforcement learning in continuous time and space. We show …

Entropy regularization has been extensively adopted to improve the efficiency, the stability,
and the convergence of algorithms in reinforcement learning. This paper analyzes both …

Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …

Model-free control approaches require advanced exploration-exploitation policies to
achieve practical tasks such as learning to bipedal robot walk in unstructured environments …

Reinforcement learning algorithms typically consider discrete-time dynamics, even though
the underlying systems are often continuous in time. In this paper, we introduce a model …

We study reinforcement learning (RL) in the setting of continuous time and space, for an
infinite horizon with a discounted objective and the underlying dynamics driven by a …