Reinforcement Learning Symposium

Reinforcement Learning (RL), a field in AI inspired by learning mechanisms in biological systems, has emerged as a powerful generalized paradigm for a diverse set of applications, particularly those requiring adaptive reasoning, such as large language model training (e.g., chatGPT), education and rehabilitation technologies, transportation and energy-grid optimization, robotics, and more. However, its impact has thus far been limited due to optimization, implementation, efficiency, and safety challenges. Through invited talks, panels, and discussions, this symposium will uncover fundamental challenges in reinforcement learning frameworks and directions toward addressing them, particularly toward closing the current gap between theory, AI model training, and real-world applications and users.