Abstract: The exploration–exploitation dilemma lies at the heart of reinforcement learning (RL): should an agent exploit known strategies to maximize rewards, or explore new possibilities that may yield greater long-term gains? Striking the right balance is critical for building intelligent systems that can adapt to uncertain and dynamic environments.
In this talk, Dr. Debabrota Basu from Inria will examine how optimism can be calibrated to address uncertainty in RL. The session will highlight theoretical insights and algorithmic approaches that guide agents in making principled decisions under incomplete information, ensuring both efficiency and robustness.
Key themes will include:
Foundations of the exploration–exploitation dilemma in RL.
The role of optimism in guiding exploration strategies.
Techniques for calibrating optimism to balance risk and reward.
Applications in robotics, autonomous systems, and decision-making under uncertainty.
Open challenges in scaling exploration–exploitation strategies to real-world problems.
The presentation will emphasize both conceptual frameworks and practical case studies, showing how calibrated optimism can lead to more reliable and adaptive reinforcement learning agents. By tackling this fundamental dilemma, the talk will shed light on pathways toward building AI systems that learn effectively in the face of uncertainty.