Special Issue on Evolutionary Reinforcement Learning
Guest Editors
- GIUSEPPE PAOLO, HUAWEI, FRANCE, giuseppe.g.paolo@gmail.com
- ALEXANDRE CONINX, SORBONNE UNIVERSITY, FRANCE, alexandre.coninx@sorbonne-universite.fr
- ANTOINE CULLY, IMPERIAL COLLEGE, UK, a.cully@imperial.ac.uk
- ADAM GAIER, AUTODESK RESEARCH, GERMANY, adam.gaier@autodesk.com
This Special Issue aims to highlight the growing field of Evolutionary Reinforcement Learning while proposing an outlet for the two communities, reinforcement learning (RL) and evolutionary algorithms (EA) to present new applications and ideas and discuss past and new challenges. We are particularly interested in papers at the intersection of optimization and reinforcement learning, such as the use of evolutionary optimization for data collection or tuning of reinforcement learning algorithms, reinforcement learning to configure and improve performance of evolutionary optimization, and any hybrids of evolutionary algorithms with other reinforcement learning techniques.
For the full Call for Papers and submission instructions, go to:
Important Dates
Open for Submissions: June 15th, 2022
Submissions deadline: August 27th, 2022
First-round review decisions: October 30th,2022
Deadline for revision submissions: December 30th, 2022
Notification of final decisions: February 28th, 2023
Tentative publication: March 2023
For question and further information, please contact one of the guest editors.
Sign up for TELO TOC alerts at https://dl.acm.org/action/doUpdateAlertSettings?action=addJournal&journalCode=telo.