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

Temporal difference and evolutionary methods are two of the most common approaches to
solving reinforcement learning problems. However, there is little consensus on their relative …

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 …

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

Reinforcement Learning (RL) has achieved impressive performance in many complex
environments due to the integration with Deep Neural Networks (DNNs). At the same time …

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 …

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

To excel in challenging tasks, intelligent agents need sophisticated mechanisms for action
selection: they need policies that dictate what action to take in each situation. Reinforcement …

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 …