Deep Reinforcement Learning

This course will cover the breadth of modern model-free RL methods, discuss their limitations and introduce a variety of current research topics. In particular, we expect to cover the following:

• Deep learning methodology and architectures

• Stabilization of approximated value estimation

• Modern actor-critic methods

• Planning as inference

• Exploration with deep networks

• Offline reinforcement learning

• Deep multi-agent reinforcement learning

• Multi-task and sim2real learning

The final grade of the course consists of the following components:

• Written Exam (weighting 100%)

• Individual Assignments: Students hand in homework bi-weekly and must get 75% of the total points to be eligible to the exam (pass/fail)

Final grade calculation = 1 * Written Exam + Pass/Fail * Individual Assignments

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Written Exam: Resit opportunity

• Individual Assignments: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).