… of Reinforcement Learning (RL) techniques blends with one or more techniques from Evolutionary
Computation (… as the evolutionary computation versus reinforcement learning (ECRL) …

… Evolutionary computation (EC), which maintains a population of learning agents, has …
In contrast, evolutionary computation (EC) methods have successfully addressed these …

… Deep reinforcement learning algorithms have been successfully applied to a range of … In
this paper, we introduce Collaborative Evolutionary Reinforcement Learning (CERL), a scalable …

… Reinforcement learning (RL) algorithms have been successfully applied in a number of …
This partnership with deep learning, often referred to as Deep Reinforcement Learning (DRL) …

… evolutionary computation, which improves the on-line performance of evolutionary computation
… actions and using them in evolutionary computation to select policies for evaluation. We …

… reinforcement learning RL … of computational tools is advisable to perform hyperparameter
tuning [2]; within these tools, a good option is to apply techniques of evolutionary computation …

… for reinforcement learning driven by evolutionary computation. … Evolutionary-Driven
Reinforcement Learning (Evo-RL), embeds the reinforcement learning algorithm in an evolutionary …

… observability, evolutionary reinforcement-learning approaches have … of evolutionary
computation to reinforcement learning, … and methods for on-line evolutionary reinforcement …

… The operator adaptation mechanisms frequently embed the idea of reinforcement learning.
… rewards obtained from reinforcement learning. The idea of reinforcement learning can also …

… Evolutionary computation is one of the most promising approaches to tackling reinforcement
learning … , we use a challenging reinforcement learning task called server job scheduling. …