Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

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 …

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

Deep neuroevolution and deep Reinforcement Learning have received a lot of attention
over the past few years. Some works have compared them, highlighting their pros and cons …

A fascinating aspect of nature lies in its ability to produce a large and diverse collection of
organisms that are all high-performing in their niche. By contrast, most AI algorithms focus …

We propose a novel reinforcement learning algorithm, QD-RL, that incorporates the
strengths of off-policy RL algorithms into Quality Diversity (QD) approaches. Quality-Diversity …

Reward-based optimization algorithms require both exploration, to find rewards, and
exploitation, to maximize performance. The need for efficient exploration is even more …

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 …

Although deep reinforcement learning (DRL) has shown promising results for autonomous
navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior …

Quality-Diversity (QD) is a concept from Neuroevolution with some intriguing applications to
Reinforcement Learning. It facilitates learning a population of agents where each member is …