Deep reinforcement learning (DRL) has experienced tremendous growth in the past few
years. However, training stability of agents continues to be an open research question. Here …

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

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

In this paper, we present XuanCe, a comprehensive and unified deep reinforcement
learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and …

We present DARLEI, a framework that combines evolutionary algorithms with parallelized
reinforcement learning for efficiently training and evolving populations of UNIMAL agents …

Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but
agents often fail to generalize beyond the environment they were trained in. As a result …

Recently, an emerging research direction called Evolutionary Reinforcement Learning
(ERL) has been proposed, which combines evolutionary algorithm with reinforcement …

Unstable training and inefficient exploration remain challenging problems in Reinforcement
Learning (RL), especially when handling continuous and high-dimensional state space. In …

In computer vision and natural language processing, innovations in model architecture that
lead to increases in model capacity have reliably translated into gains in performance. In …

skrl is an open-source modular library for reinforcement learning written in Python and
designed with a focus on readability, simplicity, and transparency of algorithm …