Abstract: Reinforcement Learning (RL) has become one of the most dynamic areas of artificial intelligence, offering a framework for agents to learn optimal behaviors through interaction with their environment. From robotics and autonomous systems to healthcare and finance, RL has demonstrated its potential to solve complex sequential decision-making problems.
In this talk, Dr. Bapi Chatterjee and Dr. M. Vidyasagar from IIIT Hyderabad will provide a comprehensive overview of reinforcement learning, covering both foundational principles and recent advances. The session will highlight how RL algorithms balance exploration and exploitation, the role of reward structures, and the challenges of scaling RL to real-world applications.
Key themes will include:
Fundamentals of reinforcement learning: agents, environments, states, actions, and rewards.
Classical approaches such as Q-learning and policy gradients.
Advances in deep reinforcement learning and its transformative impact.
Applications of RL across diverse domains, including robotics, healthcare, and resource optimization.
Open challenges such as sample efficiency, safety, and interpretability.
The presentation will serve as both an introduction and a forward-looking perspective, showing how reinforcement learning continues to evolve as a cornerstone of modern AI research. By bridging theory with practice, the speakers will illustrate the promise and limitations of RL in shaping intelligent systems of the future.