Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities
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 …
of natural evolution, have received widespread acclaim for their exceptional performance in …
Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems
With the development of Artificial Intelligence, Internet of Things and Big Data, intelligent
manufacturing has become a new and popular trend in manufacturing industries …
manufacturing has become a new and popular trend in manufacturing industries …
[PDF][PDF] Structure in reinforcement learning: A survey and open problems
Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …
Networks (DNNs) for function approximation, has demonstrated considerable success in …
Graph-enabled reinforcement learning for time series forecasting with adaptive intelligence
Reinforcement learning (RL) is renowned for its proficiency in modeling sequential tasks
and adaptively learning latent data patterns. Deep learning models have been extensively …
and adaptively learning latent data patterns. Deep learning models have been extensively …
Understanding world models through multi-step pruning policy via reinforcement learning
In model-based reinforcement learning, the conventional approach to addressing world
model bias is to use gradient optimization methods. However, using a singular policy from …
model bias is to use gradient optimization methods. However, using a singular policy from …
Energy-Efficient Satellite Range Scheduling Using A Reinforcement Learning-based Memetic Algorithm
The rapid expansion of the satellite industry has presented numerous opportunities across
various sectors and significantly transformed people's daily lives. However, the high energy …
various sectors and significantly transformed people's daily lives. However, the high energy …
Routes to optimum conditions of plant based microbial fuel cells by reinforcement learning
NA Tapan, ME Günay, T Gürbüz - International Journal of Hydrogen Energy, 2024 - Elsevier
Plant-based microbial fuel cells (PMFC) are fascinating technologies that have the potential
to combine plants and bacteria to produce electricity from different solid and aqueous media …
to combine plants and bacteria to produce electricity from different solid and aqueous media …
Structure in Deep Reinforcement Learning: A Survey and Open Problems
Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …
Networks (DNNs) for function approximation, has demonstrated considerable success in …
Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model
J Liu, R Long, H Chen, M Wu, W Ma, Q Li - Journal of Environmental …, 2024 - Elsevier
Environmental complaints serve as a crucial means for citizens to participate in
environmental regulation, providing precise insights for real-time identification of pollution …
environmental regulation, providing precise insights for real-time identification of pollution …
A knowledge-driven memetic algorithm for the energy-efficient distributed homogeneous flow shop scheduling problem
The reduction of carbon emissions in the manufacturing industry holds significant
importance in achieving the national” double carbon” target. Ensuring energy efficiency is a …
importance in achieving the national” double carbon” target. Ensuring energy efficiency is a …