Many challenging real-world control problems require adaptation and learning in the
presence of uncertainty. Examples of these challenging domains include aircraft adaptive …

Social learning is complex, but people often seem to navigate social environments with
ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that …

Model-free control is an important and promising topic in control fields, which has attracted
extensive attention in the past few years. In this paper, we aim to solve the model-free …

This paper provides a theoretical understanding of deep Q-Network (DQN) with the
$\varepsilon $-greedy exploration in deep reinforcement learning. Despite the tremendous …

Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic
optimal control problems. However, despite the promise exhibited, RL has yet to see marked …

In this paper, a data-driven conflict-aware safe reinforcement learning (CAS-RL) algorithm is
presented for control of autonomous systems. Existing safe RL results with predefined …

This paper derives sample complexity results for using Gaussian Processes (GPs) in both
model-based and model-free reinforcement learning (RL). We show that GPs are KWIK …

An important facet of reinforcement learning (RL) has to do with how the agent goes about
exploring the environment. Traditional exploration strategies typically focus on efficiency and …

Parallel batch processor scheduling with dynamic job arrival is complex and challenging in
semiconductor manufacturing. In order to get its reliable and high-performance schedule in …

The dynamic modeling of the steel rolling heating furnace temperature field (SRHFTF) plays
a very important role in the process control of the metallurgical industry. It can control and …