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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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The A2C algorithm

The difference between A2C and A3C is that A2C performs synchronous updates. Here, all the workers will wait until they have completed the collection of experiences and computed the gradients. Only after this are the global (or master) network's parameters updated. This is different from A3C, where the update is performed asynchronously, that is, where the worker threads do not wait for the others to finish. A2C is easier to code than A3C, but that is not undertaken here. If you are interested in this, you are encouraged to take the preceding A3C code and convert it to A2C, after which the performance of both algorithms can be compared.

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