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 …

Abstract Evolutionary Reinforcement Learning (ERL) has garnered widespread attention in
recent years due to its inherent robustness and parallelism. However, the integration of …

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles
of natural evolution, have received widespread acclaim for their exceptional performance in …

Reinforcement Learning (RL) has proven to be highly effective in various real-world
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Evolution strategy (ES) has been shown great promise in many challenging reinforcement
learning (RL) tasks, rivaling other state-of-the-art deep RL methods. Yet, there are two …

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in
complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a …

Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling
complex reinforcement learning (RL) problems due to high parallelism, while they are prone …

In this work, we propose a novel approach for reinforcement learning driven by evolutionary
computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (Evo …