P Li, J Hao, H Tang, X Fu, Y Zhen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
C Dong, D Li - Neural Processing Letters, 2024 - Springer
Abstract Evolutionary Reinforcement Learning (ERL) has garnered widespread attention in recent years due to its inherent robustness and parallelism. However, the integration of …
Y Song, Y Wu, Y Guo, R Yan, PN Suganthan… - Swarm and Evolutionary …, 2024 - Elsevier
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 …
H Bai, R Cheng, Y Jin - Intelligent Computing, 2023 - spj.science.org
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 …
Y Lin, F Lin, G Cai, H Chen, L Zou, P Wu - arXiv preprint arXiv:2402.13296, 2024 - arxiv.org
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 …