Modern Reinforcement Learning: Deep Q Learning in PyTorch Course
In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. Will read the original papers that introduced the Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms.
You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym’s Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to:
If you do not have prior experience in reinforcement or deep reinforcement learning, that’s no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym.
Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym.
Last updated 5/2020
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