Deep Reinforcement Learning 2.0

Deep Reinforcement Learning 2.0

The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG

Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team | 9.5 hours on-demand video

Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).

What you’ll learn

  • Q-Learning
  • Deep Q-Learning
  • Policy Gradient
  • Actor Critic
  • Deep Deterministic Policy Gradient (DDPG)
  • Twin-Delayed DDPG (TD3)
  • The Foundation Techniques of Deep Reinforcement Learning
  • How to implement a state of the art AI model that is over performing the most challenging virtual applications

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