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 …

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

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

We present RLLTE: a long-term evolution, extremely modular, and open-source framework
for reinforcement learning (RL) research and application. Beyond delivering top-notch …

Reinforcement learning (RL) has attracted rapidly increasing interest in the machine
learning and artificial intelligence communities in the past decade. With tremendous success …

Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

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 …