Reinforcement Learning and recently Deep Reinforcement Learning are popular methods
for solving sequential decision making problems modeled as Markov Decision Processes …

Abstract The combination of Reinforcement Learning (RL) with deep learning has led to a
series of impressive feats, with many believing (deep) RL provides a path towards generally …

This article characterizes the evolutionary algorithm approach to reinforcement learning in
relation to the more standard, temporal di erence methods. We describe several research …

Reinforcement learning (RL) applications, where an agent can simply learn optimal
behaviors by interacting with the environment, are quickly gaining tremendous success in a …

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 …

In reinforcement learning, accurate estimation of the Q-value is crucial for acquiring an
optimal policy. However, current successful Actor-Critic methods still suffer from …

Abstract Different from classic Supervised Learning, Reinforcement Learning (RL), is
fundamentally interactive: an autonomous agent must learn how to behave in an unknown …

The SWIMMER environment is a standard benchmark in reinforcement learning (RL). In
particular, it is often used in papers comparing or combining RL methods with direct policy …

It is widely agreed that the field of reinforcement learning would benefit from the
establishment of standard benchmark problems and perhaps regular competitive events …

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