PC5 Deep Reinforcement Learning
Deep Reinforcement Learning (RL) is at the forefront of Artificial Intelligence research. In this Practical Course, you’ll understand the most recent advances in Deep RL, learn cutting-edge algorithms like Deep Q-Learning (DQN), Deep Deterministic Policy Gradients (DDPG), Actor-Critic, etc., and train your own AI agents in simulated environments.
Please bring your laptops.
You’ll train AI agents to walk, drive, manipulate objects, or perform other complex tasks in simulated environments [1, 2, 3, 4]. This course consists of demonstrations, crystal-clear examples of code implementation, and practical exercises which you will perform on your laptops.
Deep Q-Network & Dueling network architectures for deep reinforcement learning
Introduction to Reinforcement Learning
Spinning Up in Deep RL!
Exercises and solutions for popular Reinforcement Learning algorithms
- basic Python experience: you can write nested for loops, define functions, etc., and read and understand code written by others.
- basic knowledge of machine learning techniques: you have seen a few examples of neural network architecture (e.g., CNN), and can explain what means backpropagation (https://www.youtube.com/results?search_query=backpropagation).
- personal laptop: Linux, Mac and Windows machines are equally fine for the purpose of this course.
Andrew currently works on Deep Reinforcement Learning approaches for robotics at CITEC, Bielefeld University, Germany.
He received his doctoral degree in Cognitive Science at Osnabrück University for his thesis on sensorimotor processing in the human brain and in cognitive architectures.