This lecture (5 ECTS) continues the lecture reinforcement learning from the previous semester. We fill gaps (such as Lai-Robbins lower bounds for bandits) that were left and discuss further topics (such as MCTS, ODE method for stochastic approximation,...).
Reinforcement learning is a type of machine learning that involves training an agent to make a sequence of decisions in an environment in order to maximize a reward. It is often used to control complex, dynamic systems or to optimize performance. Some applications of reinforcement learning include:
Overall, reinforcement learning offers a way to optimize complex systems by learning how to act in certain situations in order to maximize rewards.
Attention: This text was written by chatGPT, an AI tool based on reinforcement learning (RL) itself (and transformer networks). I do not quite agree with chatGPT, financial markets seem not be very well suited to ML methods. Anyways, as we will cross RL in our future lives in manifold occasions it will be useful to know how RL works.
Students from the study programs Mathematics, WiMa, WiFo, MMDS. We will cover the mathematical background of reinforcement learning, coding (in python) will be part of the exercises.
Prof. Dr. Leif Döring, Prof. Dr. Mathias Staudigl, Prof. Dr. Simon Weißmann
Sutton & Barto: “Reinforcement Learning – an Introduction” is available online. This covers all major ideas but skipps essentially all details. In essence, this lecture course follows the core ideas of Sutton & Barto but tries to include as much of the missing mathematics as possible.