Introduction to Deep Reinforcement Learning

What it is about

You learn the theory and fundamentals of Deep Reinforcement Learning (DRL) as well as relevant deep-learning and reinforcement-learning concepts. By the end you will be able to model problems as Markov Decision Processes, understand and apply DRL algorithms, and assess their advantages and disadvantages.

What you will be able to do

• Foundations of search algorithms (graph and tree search)
• Model problems as Markov Decision Process (MDP)
• Foundations of Reinforcement Learning (e.g., Q‑Learning, TD‑Learning)
• Foundations of Deep Learning (e.g., SGD, logistic regression, neural networks)
• Understand Deep Reinforcement Learning (e.g., DQN, PPO)
• Apply DRL frameworks to practical problems
• Evaluate advantages and disadvantages of DRL methods
• Recognize and handle common pitfalls in practical applications

What the module consists of

• VorlesungVermittlung der theoretischen Grundlagen von Deep Reinforcement Learning
• ÜbungenAnwendung und Vertiefung des Vorlesungsinhalts
• Coding LabsPraktische Umsetzung und Implementierung von DRL‑Methoden

Teaching method

• VorlesungErklärung der theoretischen Konzepte
• ÜbungenPraktische Vertiefung und methodische Anwendung
• Coding LabsProgrammierung und Anwendung von DRL auf praxisnahe Probleme