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Reinforcement Learning Lab

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General Information

  • HISinOne: 11LE13P-7320
  • Kickoff meeting: Monday 20.10.2025, 10:00 a.m. - 11:00 a.m. Nexus Lab
  • Location: Intelligent Machine-Brain Interface Technology (IMBIT), Nexus Lab 
  • Further Informations, exercises and solutions will be posted on ILIAS.
  • Language: English
  • Email:

 

Overview

The Reinforcement Learning (RL) Lab (not to be confused with the Deep Learning Lab!) is a practical course in which students will learn to program their own deep reinforcement learning (DRL) agents using state-of-the-art algorithms such as Deep Q-Learning (Mnih et al., 2013) and Soft Actor-Critic (Haarnoja et al., 2018). Every student will implement these methods using Python and PyTorch, a popular library for coding neural networks, in weekly exercises. Finally, students will propose and solve their own DRL problem in small groups using the methods they have implemented, and present their results.

 

Prerequisites

Students are expected to understand basic concepts of reinforcement learning (i.e., to have attended the corresponding lecture) and to have experience programming in Python.

 

Format

Everything except the final presentation will be online via HISinOne.