As we mentioned earlier, we have parallel workers in A3C, and each worker will compute the policy gradients and pass them on to the central (or master) processor. The A3C paper also uses the advantage function to reduce variance in the policy gradients. The loss functions consist of three losses, which are weighted and added; they include the value loss, the policy loss, and an entropy regularization term. The value loss, Lv, is an L2 loss of the state value and the target value, with the latter computed as a discounted sum of the rewards. The policy loss, Lp, is the product of the logarithm of the policy distribution and the advantage function, A. The entropy regularization, Le, is the Shannon entropy, which is computed as the product of the policy distribution and its logarithm, with a minus sign included. The entropy regularization term is like a bonus for...
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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