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Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

About the book

Book Cover

Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms.

The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.

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Get the Chinese Version (中文版)

The book is also translated into chinese and you can get it from here (这本书也被翻译成中文,你可以从这里得到它):https://item.jd.com/12506442.html

Table of Contents

  • 1.1. What is Reinforcement Learning?
  • 1.2. Reinforcement Learning Cycle
  • 1.3. How RL differs from other ML Paradigms?
  • 1.4. Elements of Reinforcement Learning
  • 1.5. Agent Environment Interface
  • 1.6. Types of RL Environments
  • 1.7. Reinforcement Learning Platforms
  • 1.8. Applications of Reinforcement Learning
  • 8.1. What is Deep Q network
  • 8.2. Architecture of DQN
  • 8.3. Convolutional Network
  • 8.4. Experience Replay
  • 8.5. Target Network
  • 8.6. Clipping Rewards
  • 8.7. DQN Algorithm
  • 8.8. Building an Agent to Play Atari Games
  • 8.9. Double DQN
  • 8.10. Dueling Architecture
  • 10.1. Asynchronous Actor Critic Algorithm
  • 10.2. The three A's
  • 10.3. Architecture of A3C
  • 10.4. Working of A3C
  • 10.5. Drive up the Mountain with A3C
  • 10.6. Visualization in Tensorboard

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