DORS Tutorial: Introduction to reinforcement learning

Place: Online via Zoom (link will be sent to the registered participants)

Time: 17/12/2020 13:00-17:00

About the tutorial:

Reinforcement learning provides a mathematical formalism for learning-based control. By utilizing reinforcement learning, we can automatically acquire near-optimal behavioral skills, represented by policies, for optimizing user-specified reward functions. It contrasts with the traditional optimization toolbox by considering a stochastic environment whose dynamics are not know apriori. By interacting with the environment and observing delayed rewards, reinforcement learning agents have shown outstanding results at solving complex sequential decision-making problems such as playing Go and videogames at super-human-level performance, autonomous driving, smart grid optimization, etc. This tutorial will cover the basics of reinforcement learning, including terminology and mathematical formalism, Markov Decision Processes, Q-learning and Deep Q-learning. Given the limited time, it will prioritise "breadthness"  over depth, giving pointers to where to learn about certain aspects in more detail. It will be split in multiple…