- Foundations of Decision Making (Reward Hypothesis, Markov Property, Markov Reward Process, Value Iteration, Markov Decision Process, Policy Iteration, Bellman Equation.
- Principles of Reinforcement Learning (Generalize Policy Iteration, On & Off-policy learning, Monte-Carlo Approaches, (Multi-step) TD-Learning, Eligibility Traces, Exploration and Exploitation strategies)
- Deep Reinforcement Learning (Introduction to Deep Networks, Stochastic Gradient Descent, Function Approximation, Fitted Q-Iteration, (Double) Deep Q-Learning, Policy-Gradient approaches, open questions)
- Students get a comprehensive understanding of basic decision making theories, assumptions and methods.
- Students understand and can apply advanced policy gradient methods to real world problems.
- Students learn to analyze the challenges in a reinforcement learning application and to identify promising learning approaches.
- The students will also experiment with the basic (deep) reinforcement learning methods and the simulation tools OpenAI Gym in accompanying exercises.
