Hands-On Reinforcement Learning With Python

Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Hands-On Reinforcement Learning With Python
Hands-On Reinforcement Learning With Python

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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Table of Contents

1. Introduction to Reinforcement Learning

  • 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

2. Getting Started with OpenAI and Tensorflow

3. Markov Decision Process and Dynamic Programming

4. Gaming with Monte Carlo Methods

5. Temporal Difference Learning

6. Multi-Armed Bandit Problem

7. Deep Learning Fundamentals

8. Atari Games With Deep Q Network

  • 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

9. Playing Doom With Deep Recurrent Q Network

10. Asynchronous Advantage Actor Critic Network

11. Policy Gradients and Optimization

12. Capstone Project: Car Racing using DQN

13. Recent Advancements and Next Steps