On the afternoon of April 15, 2025, the Workshop on Reinforcement Learning and Applications, hosted by the School of Intelligence Science and Technology, Nanjing University, was successfully held in Room 303, Tianshu Building, at Nanjing University’s Suzhou Campus. Centered on the latest advances in reinforcement learning and multi-agent systems and their real-world applications, the event attracted enthusiastic participation from faculty and students, creating a lively and engaging academic atmosphere.
The workshop featured invited talks from several renowned experts in the field, including Associate Professor Dengji Zhao from ShanghaiTech University, Associate Professor Shuai Li from Shanghai Jiao Tong University, Researcher Shuyue Hu from Shanghai Artificial Intelligence Laboratory, Professor Cairong Zhao from Tongji University, and Professor Jianye Hao from Tianjin University.
Prof. Yang Gao, Secretary of the CPC Committee and Executive Dean of the School of Intelligence Science and Technology, also attended the workshop, along with Associate Professor Ying Tai, Associate Professor Qi Fan, Assistant Professor Tianpei Yang, and Assistant Professor Jieqi Shi. The event was co-hosted by Prof. Yang Gao and Dr. Jieqi Shi.
In the opening remarks, Prof. Gao reviewed the development of reinforcement learning and multi-agent systems, emphasizing their pivotal role in the future of artificial intelligence. He also expressed Nanjing University’s strong desire to deepen collaboration and enhance exchanges with peer institutions in these fields.
Following the opening address, invited experts shared their latest research findings across a range of topics, including reinforcement learning theory, multi-agent collaboration, large model integration, visual perception and intelligent learning, and embodied intelligent systems. The talks were followed by in-depth academic discussions and inspiring exchanges of ideas.
This workshop not only fostered greater interaction and collaboration between researchers within and beyond the university, but also injected new momentum into the practical application of reinforcement learning in real-world scenarios.
