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TensorFlow Reinforcement Learning Quick Start Guide

You're reading from  TensorFlow Reinforcement Learning Quick Start Guide

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789533583
Pages 184 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Kaushik Balakrishnan Kaushik Balakrishnan
Profile icon Kaushik Balakrishnan
LinkedIn
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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Table of Contents (11) Chapters

Preface 1. Up and Running with Reinforcement Learning 2. Temporal Difference, SARSA, and Q-Learning 3. Deep Q-Network 4. Double DQN, Dueling Architectures, and Rainbow 5. Deep Deterministic Policy Gradient 6. Asynchronous Methods - A3C and A2C 7. Trust Region Policy Optimization and Proximal Policy Optimization 8. Deep RL Applied to Autonomous Driving 9. Assessment 10. Other Books You May Enjoy

Cliff walking and grid world problems

Let's consider cliff walking and grid world problems. First, we will introduce these problems to you, then we will proceed on to the coding part. For both problems, we consider a rectangular grid with nrows (number of rows) and ncols (number of columns). We start from one cell to the south of the bottom left cell, and the goal is to reach the destination, which is one cell to the south of the bottom right cell.

Note that the start and destination cells are not part of the nrows x ncols grid of cells. For the cliff walking problem, the cells to the south of the bottom row of cells, except for the start and destination cells, form a cliff where, if the agent enters, the episode ends with catastrophic fall into the cliff. Likewise, if the agent tries to leave the left, top, or right boundaries of the grid of cells, it is placed back in the...

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Authors (1)

Profile icon Kaushik Balakrishnan
LinkedIn
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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See other products by Kaushik Balakrishnan

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