This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of
affine nonlinear systems. We review four seminal methods that are the centerpieces of the …

This paper presents a novel approach for optimal control of nonlinear stochastic systems
using infinitesimal generator learning within infinite-dimensional reproducing kernel Hilbert …

The concept of digital twins promises to revolutionize engineering by offering new avenues
for optimization, control, and predictive maintenance. We propose a novel framework for …

Control of dynamic systems involving hybrid actions is a challenging task in robotics. To
address this, we present a novel algorithm called Generalized Policy Iteration using Tensor …

Reinforcement Learning (RL) allows learning non-trivial robot control laws purely from data.
However, many successful applications of RL have relied on ad-hoc regularizations, such as …

A default assumption in reinforcement learning (RL) and optimal control is that observations
arrive at discrete time points on a fixed clock cycle. Yet, many applications involve …

Continuous-time reinforcement learning (CT-RL) methods hold great promise in real-world
applications. Adaptive dynamic programming (ADP)-based CT-RL algorithms, especially …

This work presents a new framework for model-based continuous-time reinforcement
learning (CT-RL) control of hypersonic vehicles (HSVs). The predominant classes of CT-RL …

The standard framework in reinforcement learning (RL) dictates that an agent should use
every observation collected from interactions with the environment when updating its value …

This thesis introduces novel methods to expedite the solution of a broad range of
optimization and control problems using machine learning, specifically decision tree …