Recently, there has been a focus on using transfer learning to reduce the sample complexity
in reinforcement learning. One component that enables transfer is an intertask mapping that …

Studies have shown evolution strategies (ES) to be a promising approach for reinforcement
learning (RL) with deep neural networks. However, the issue of high sample complexity …

Recently, evolutionary reinforcement learning has obtained much attention in various
domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the …

We present a simple, sample-efficient algorithm for introducing large but directed learning
steps in reinforcement learning (RL), through the use of evolutionary operators. The …

In the last years, reinforcement learning received a lot of attention. One method to solve
reinforcement learning tasks is Neuroevolution, where neural networks are optimized by …

Reinforcement learning (RL) has attracted rapidly increasing interest in the machine
learning and artificial intelligence communities in the past decade. With tremendous success …

Reinforcement learning is increasingly finding success across domains where the problem
can be represented as a Markov decision process. Evolutionary computation algorithms …

An evolution strategy (ES) variant based on a simplification of a natural evolution strategy
recently attracted attention because it performs surprisingly well in challenging deep …

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

In this paper we introduce EuSANE, a novel reinforcement learning algorithm based on the
SANE neuro-evolution method. It uses a global genetic search algorithm, the Eugenic …