Deep Reinforcement Learning for Multi-Agent Systems
A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 424
Special Issue Editor
Interests: probabilistic verification; self-adaptive software systems; event-streaming software systems; autonomous agents; reinforcement learning; data mining
Special Issue Information
Dear Colleagues,
Deep reinforcement learning (DRL) have attracted numerous real-world applications in various domains. While the majority of DRL research is now focused on single agents, there is a fast-growing trend to extend DRL for problems in multiagent systems. Several foundational approaches to multiagent DRL (MADRL) have emerged, including counterfactual multi-agent (COMA), value-decomposition networks (VDNs) and QMIX, but there are still many challenges in this area, such as large-scale agent teams, heterogeneity and robustness.
This Special Issue aims to collect most up-to-date research in MADRL, ranging from theoretical problems to practical application, and to simulation platforms and applications in this area. Specific topics include (but are not limited to): scalability, partial observability, non-stationarity, credit assignment, heterogeneity, curriculum learning, mechanism design, dynamic programming, planning, communication, adversarial attachment, security, empirical study, simulation, and reflection on current trends.
Dr. Guoxin Su
Guest Editor
Manuscript Submission Information
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Keywords
- deep reinforcement learning
- multi-agent system
