The 1st International Workshop on Advanced Deep Reinforcement Learning for Energy Systems (ADELE) welcomes submissions from academic and industry focusing on Deep Reinforcement Learning and its applications to the energy domain. The workshop is geared towards presenting, exploring, and discussing newest approaches from the DRL domain and inspiring quick transition towards application and further research by scientists in the energy domain. ADELE recognizes that the premier targets of DRL research is not the energy domain and, therefore, strives to foster close cooperation between DRL and energy scientists.
Topics of interest include, but are not limited to:

• Approaches to convering large state/action spaces, including handling underlying physical properties and controller conflicts
• State space representations
• Approachs to eXplainable Deep Reinforcement Learning suitable to large and complex (e.g., mixed discrete-continuous) state/action spaces
• Safe and Risk-averse DRL
• Offline learning from domain user knowledge without explicit trajectory encoding
• Physics-informed or model-based DRL
• Modifications of existing model-free algorithms wrt. the energy domain
• Neuroevolutionary DRL
• Hybrid agents (e.g., DRL-based extensions of known controllers)
• Agent verification

Fomat

ADELE invites submissions in the following format:

• Full workshop papers, up to 10 pages, following the ACM template according to the main conference’s guidelines (https://energy.acm.org/conferences/eenergy/2025/cfp.php).
• Tutorials, with an accompaning paper of up to 4 pages, following the same templates as a full workshop paper

Site

The submission site of ADELE is located at: https://adele25.hotcrp.com/

Submissions will be peer-reviewed by at least 3 reviewers. Presentations will honour the workshop format by providing 20 minutes with the actual slide deck and additional 10 minutes for discussion

Important Dates

• Omid Ardakanian, University of Alberta, Canada

• Dan Wang, The Hong Kong Polytechnic University, Hong Kong, China

• Florence Carton, Total Energies, France

• Tianyu Zhang, Autodesk Research, Canada

• Eric MSP Veith, Carl von Ossietzky University Oldenburg

• Arlena Wellßow, Carl von Ossietzky University Oldenburg

• Torben Logemann, Carl von Ossietzky University Oldenburg