Reinforcement Learning for Intelligent Agents
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 11
Editors
Interests: LLM agent; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Interests: decision making
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Reinforcement Learning (RL) has established itself as a foundational paradigm for building intelligent agents that learn optimal decision-making strategies through interaction with complex, uncertain environments. Despite substantial progress, critical challenges persist—sample efficiency, generalization across tasks, safe exploration, multi-agent coordination and reliable real-world deployment remain open problems.
This Special Issue invites original research that advances the theory, algorithms and applications of RL for intelligent agents. Topics of interest include, but are not limited to, model-based and model-free RL, deep and offline RL, multi-agent and hierarchical RL, RL with human feedback, reward shaping and RL applications in robotics, autonomous driving, game AI and beyond.
We welcome both theoretical breakthroughs and empirical studies that demonstrate meaningful progress toward building more capable, robust and trustworthy intelligent agents. This issue aims to foster interdisciplinary dialog and accelerate the translation of RL innovations into real-world intelligent systems. We look forward to your contributions.
Dr. Wanyuan Wang
Dr. Vincent Chau
Prof. Dr. Weiwei Wu
Guest Editors
Manuscript Submission Information
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Keywords
- reinforcement learning
- multi-agent systems
- LLM agent
- symbolic agent
- planning
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