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

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

Coalbed methane (CBM) is a clean energy source. The prediction of CBM production is a
critical step during CBM exploitation and utilization, especially for geological well selection …

Deep neuroevolution and deep Reinforcement Learning have received a lot of attention
over the past few years. Some works have compared them, highlighting their pros and cons …

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

A fascinating aspect of nature lies in its ability to produce a large and diverse collection of
organisms that are all high-performing in their niche. By contrast, most AI algorithms focus …

In deep reinforcement learning, off-policy data help reduce on-policy interaction with the
environment, and the trust region policy optimization (TRPO) method is efficient to stabilize …

Methane (CH 4) represents a significant greenhouse gas, and the control of its emissions is
crucial in impacting global climate change. Accurate forecasting of CH 4 emissions is …

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for training neural
policies to solve complex control tasks. However, these policies tend to be overfit to the …

Purpose Chronic renal failure (CRF) is a disease with a high morbidity rate that can develop
into uraemia, resulting in a series of complications, such as dyspnoea, mental disorders …