This book presents the main results of the research I conducted as a Ph. D. student at The
University of Texas at Austin, primarily between 2004 and 2007. The primary contributions …

In deep reinforcement learning (RL) research, there has been a concerted effort to design
more efficient and productive exploration methods while solving sparse-reward problems …

Reinforcement learning often needs to deal with the exponential growth of states and
actions when exploring optimal control in high-dimensional spaces (often known as the …

Despite tremendous progress, machine learning and deep learning still suffer from
incomprehensible predictions. Incomprehensibility, however, is not an option for the use of …

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as
it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic …

The goal of reinforcement learning is to find a policy that maximizes the expected reward
accumulated by an agent over time based on its interactions with the environment; to this …

Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by
temporally abstracting a policy into multiple levels. Promising results in sparse reward …

Historically, artificial intelligence has drawn much inspiration from neuroscience to fuel
advances in the field. However, current progress in reinforcement learning is largely focused …

Improving sample efficiency is central to Reinforcement Learning (RL), especially in
environments where the rewards are sparse. Some recent approaches have proposed to …

Combining Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for policy
search has been proven to improve RL performance. However, previous works largely …