Reinforcement Learning

This course introduces the theoretical and algorithmic foundations of Reinforcement Learning, the subfield of Machine Learning studying adaptive agents that take actions and interact with an unknown environment. Reinforcement learning is a powerful paradigm for the study of autonomous AI systems, and has been applied to a wide range of tasks, including self-driving cars, game playing, customer management, and healthcare.

Upon completion of the course students will be able to:
- formalize problems in terms of Markov Decision Processes,
- understand basic methods of strategic exploration,
- understand algorithms for direct policy optimization,
- run experiments in simulated environments.
These objectives are measured via a combination of two components: the project report and the oral discussion. The final grade is formed by assessing the project report, and then using the oral discussion for fine tuning.