Video
Description
Deep Q-Networks refer to the method proposed by Deepmind in 2014 to learn to play ATARI2600 games from the raw pixel observations. This hugely influential method kick-started the resurgence in interest in Deep Reinforcement Learning, however it’s core contributions deal simply with the stabilization of the NQL algorithm. In these session these key innovations (Experience Replay, Target Networks, and Huber Loss) are stepped though, taking the participants from the relatively unstable NQL algorithm to a fully-implemented DQN.
Lecture Slides
Exercise
Follow the link below to access the exercises for lecture 6:
lecture 6 Exercise: DQN Homework Exercise
Exercise Solutions
Follow the link below to access the exercise solutions for lecture 6:
lecture 6 Exercise: DQN Homework Exercise Solutions
Additional Learning Material