TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python

TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python
TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python by Kaushik Balakrishnan
English | 2019 | ISBN: 1789533583 | 184 Pages | EPUB | 11 MB

Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks
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.
What you will learn

  • Understand the theory and concepts behind modern Reinforcement Learning algorithms
  • Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions
  • Develop Reinforcement Learning algorithms and apply them to training agents to play computer games
  • Explore DQN, DDQN, and Dueling architectures to play Atari’s Breakout using TensorFlow
  • Use A3C to play CartPole and LunarLander
  • Train an agent to drive a car autonomously in a simulator