Autonomous driving is one of the hottest technological revolutions in development as of the time of writing this. It will dramatically alter how humanity looks at transportation in general, and will drastically reduce travel costs as well as increase safety. Several state-of-the-art algorithms are used by the autonomous vehicle development community to this end. These include, but are not limited to, perception, localization, path planning, and control. Perception deals with the identification of the environment around an autonomous vehicle—pedestrians, cars, bicycles, and so on. Localization involves the identification of the exact location—or pose to be more precise—of the vehicle in a precomputed map of the environment. Path planning, as the name implies, is the process of planning the path of the autonomous vehicle...
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Table Of Contents
TensorFlow Reinforcement Learning Quick Start Guide
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TensorFlow Reinforcement Learning Quick Start Guide
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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)
Preface
Free Chapter
Up and Running with Reinforcement Learning
Temporal Difference, SARSA, and Q-Learning
Deep Q-Network
Double DQN, Dueling Architectures, and Rainbow
Deep Deterministic Policy Gradient
Asynchronous Methods - A3C and A2C
Trust Region Policy Optimization and Proximal Policy Optimization
Deep RL Applied to Autonomous Driving
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