Algorithms for evolutionary computation, which simulate the process of natural selection to
solve optimization problems, are an effective tool for discovering high-performing …

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 …

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …

Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective
at solving reinforcement learning (RL) problems. However, since few rigorous empirical …

Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …

In this work, we propose a novel approach for reinforcement learning driven by evolutionary
computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (Evo …

Temporal difference methods are theoretically grounded and empirically effective methods
for addressing reinforcement learning problems. In most real-world reinforcement learning …

Neuroevolution, ie evolution of artificial neural networks, has recently emerged as a
powerful technique for solving challenging reinforcement learning problems. Compared to …

In reinforcement learning, an agent interacting with its environment strives to learn a policy
that specifies, for each state it may encounter, what action to take. Evolutionary computation …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …