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

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

Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful
behaviors in a task-agnostic environment without the guidance of extrinsic rewards to …

We present a representation-driven framework for reinforcement learning. By representing
policies as estimates of their expected values, we leverage techniques from contextual …

Recently, an emerging research direction called Evolutionary Reinforcement Learning
(ERL) has been proposed, which combines evolutionary algorithm with reinforcement …

This research is focused on addressing the energy-aware distributed heterogeneous
welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the …

Recently, various new attempts are being made to improve the quality of media content
according to the expansion of the media market. Pre-visualization is one of those attempts …

Evolutionary Algorithms (EA) have been successfully used for the optimization of neural
networks for policy search, but they still remain sample inefficient and underperforming in …

Database systems serves as powerful tools for managing big data. Within Database
Management System (DBMS), hundreds of parameters impact the performance of database …

Robotic manipulation tasks often span over long horizons and encapsulate multiple
subtasks with different skills. Learning policies directly from long-horizon demonstrations is …