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TensorFlow Reinforcement Learning Quick Start Guide

TensorFlow Reinforcement Learning Quick Start Guide

By : Balakrishnan
5 (2)
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TensorFlow Reinforcement Learning Quick Start Guide

TensorFlow Reinforcement Learning Quick Start Guide

5 (2)
By: Balakrishnan

Overview of this book

Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.
Table of Contents (11 chapters)
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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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