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Description

Description

This course is all about the application of deep learning and neural networks to reinforcement learning. The combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus—they want to reach a goal. In this course, you’ll work with more complex environments, specifically, those provided by the OpenAI Gym.

  • Access 52 lectures & 5 hours of content 24/7
  • Extend your knowledge of temporal difference learning by looking at the TD Lambda algorithm
  • Explore a special type of neural network called the RBF network
  • Look at the policy gradient method
  • Examine Deep Q-Learning

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