Lecture "Reinforcement Learning"

I serve as a teaching assistant for this lecture from 2020 to 2023 in University of Luebeck, to generate all exercise sheets with theoretical questions and programming tasks, while presenting the solutions and tutoring the students during complete exercise sessions. More information is available at Univerisity of Luebeck.

The lecture covers the following topics:

(i) Foundations of Decision Making (Markov Decision Process, Partial Observability, Value Iteration, Policy Iteration, Bellman Equation, Generalize Policy Iteration)

(ii) Principles of Reinforcement Learning (On & Off-policy learning, Monte-Carlo Approaches, (Multi-step) TD-Learning, Eligibility Traces, Exploration Strategies)

(iii) Deep Reinforcement Learning (Function Approximation, Deadly-Triad Problem, Fitted Q-Iteration, (Double) Deep Q-Learning, Prioritized Experince Replay, Train Atari-Games)

(iv) Policy-Gradient Approaches (REINFORCE, Actor-Critic Algorithms)

Honghu Xue
Honghu Xue
PhD student

My research interests include Dep Reinforcement Learning and Deep Learning.