In this paper, we survey the use of additional biologically inspired mechanisms, principles,
and concepts in the area of evolutionary reinforcement learning (ERL). While recent years …

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

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

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

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

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 …

Reinforcement Learning (RL) has achieved impressive performance in many complex
environments due to the integration with Deep Neural Networks (DNNs). At the same time …

Witnessing recent achievement in machine learning especially reinforcement learning (RL),
my overarching research ambition revolves around crafting reinforcement learning agents …

In addition to their undisputed success in solving classical optimization problems,
neuroevolutionary and population-based algorithms have become an alternative to standard …

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 …