We looked at the DDPG algorithm in the previous chapter. One of the main drawbacks of the DDPG algorithm (as well as the DQN algorithm that we saw earlier) is the use of a replay buffer to obtain independent and identically distributed samples of data for training. Using a replay buffer consumes a lot of memory, which is not desirable for robust RL applications. To overcome this problem, researchers at Google DeepMind came up with an on-policy algorithm called Asynchronous Advantage Actor Critic (A3C). A3C does not use a replay buffer; instead, it uses parallel worker processors, where different instances of the environment are created and the experience samples are collected. Once a finite and fixed number of samples are collected, they are used to compute the policy gradients, which are asynchronously sent to a central processor that updates...
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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