Background and summary of fellowship
Reinforcement Learning (RL) is concerned with learning efficient control policies for systems with unknown dynamics and reward functions. RL plays an increasingly important role in a large spectrum of application domains including online platforms (recommender systems and search engines), robotics, and self-driving vehicles. Over the last decade, RL algorithms, combined with modern function approximators such as deep neural networks, have shown unprecedented performance and have been able to solve very complex sequential decision tasks better than humans. Yet, these algorithms are lacking robustness, and are most often extremely data inefficient.
This research project aims at contributing to the theoretical foundations for the design of data-efficient and robust RL algorithms. To this aim, we develop a fundamental two-step process:
- We characterize information-theoretical limits for the performance of RL algorithms (in terms of sample complexity, i.e., data efficiency)
- We leverage these limits to guide the design of optimal RL algorithms, algorithms approaching the fundamental performance limits
