[ 993MLPEDRLV20 ] VL Deep Reinforcement Learning

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Computer Science Sepp Hochreiter 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Artificial Intelligence 2021W
Objectives Reinforcement Learning (RL) is getting relevant in the field of Machine Learning, playing a fundamental role in a wide range of areas such as autonomous driving, robotics or health-care. Classical RL techniques combined with Deep Learning allow complex systems to perform highly sophisticated tasks, unthinkable only one decade ago.

This course will provide a broad view of the most important state-of-the-art methods and the core challenges that RL is facing nowadays. The goal of this course is to assimilate the key ideas and understand upcoming contributions to the field. The lectures are distributed in three main blocks: introduction, basic methods and advanced methods. Additionally, emphasis is put on the sensitive subject of evaluating of RL methods. In the final lecture, one of the latest major keystones, AlphaStar, is analyzed.

Subject Introduction

  • Introduction to Reinforcement Learning (RL)
  • Deep Learning for RL

Basic methods

  • Deep Q-Network based methods
  • Policy Gradient based methods
  • RL and Control as Probabilistic Inference
  • Planning with Monte Carlo Tree Search

Advanced methods

  • Exploration in RL
  • Imitation and off-policy learning
  • Hierarchical RL
  • Return decomposition for delayed rewards
Criteria for evaluation Exam
Language English
Changing subject? No
Corresponding lecture in collaboration with 993MLPEDRLU20: UE Deep Reinforcement Learning (1.5 ECTS) equivalent to
993MLPEDRLK19: KV Deep Reinforcement Learning (4.5 ECTS)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment