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You're reading from  TensorFlow Reinforcement Learning Quick Start Guide

Product typeBook
Published inMar 2019
PublisherPackt
ISBN-139781789533583
Edition1st Edition
Tools
TensorFlow
Concepts
Reinforcement Learning
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Author (1)
Kaushik Balakrishnan
Kaushik Balakrishnan
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Kaushik Balakrishnan

Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.
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Questions

  1. Is the DDPG an on-policy or off-policy algorithm?
  2. We used the same neural network architectures for both the actor and the critic. Is this required, or can we choose different neural network architectures for the actor and the critic?
  3. Can we use the DDPG for Atari Breakout?
  4. Why are the biases of the neural networks initialized to small positive values?
  5. This is left as an exercise: Can you modify the code in this chapter to train an agent to learn InvertedDoublePendulum-v2, which is more challenging than the Pendulum-v0 that you saw in this chapter?
  6. Here is another exercise: Vary the neural network architecture and check whether the agent can learn the Pendulum-v0 problem. For instance, keep decreasing the number of neurons in the first hidden layer with the values 400, 100, 25, 10, 5, and 1, and check how the agent performs for the different number of neurons in the...
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TensorFlow Reinforcement Learning Quick Start Guide
Published in: Mar 2019Publisher: PacktISBN-13: 9781789533583
© 2019 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Kaushik Balakrishnan

Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.
Read more about Kaushik Balakrishnan

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