Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms

Key Features

• Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm
• Learn how to implement algorithms with code by following examples with line-by-line explanations
• Explore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrations

Book Description

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.

In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples.

The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI's baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.

By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.

What you will learn

• Understand core RL concepts including the methodologies, math, and code
• Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym
• Train an agent to play Ms Pac-Man using a Deep Q Network
• Learn policy-based, value-based, and actor-critic methods
• Master the math behind DDPG, TD3, TRPO, PPO, and many others
• Explore new avenues such as the distributional RL, meta RL, and inverse RL
• Use Stable Baselines to train an agent to walk and play Atari games

Who this book is for

If you're a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you.

Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.

Table of Contents

1. Fundamentals of Reinforcement Learning
2. A Guide to the Gym Toolkit
3. The Bellman Equation and Dynamic Programming
4. Monte Carlo Methods
5. Understanding Temporal Difference Learning
6. Case Study - The MAB Problem
7. Deep Learning Foundations
8. A Primer on TensorFlow
9. Deep Q Network and Its Variants
10. Policy Gradient Method
11. Actor-Critic Methods - A2C and A3C
12. Learning DDPG, TD3, and SAC
13. TRPO, PPO, and ACKTR Methods
14. Distributional Reinforcement Learning
15. Imitation Learning and Inverse RL
16. Deep Reinforcement Learning with Stable Baselines
17. Reinforcement Learning Frontiers
18. Appendix 1 - Reinforcement Learning Algorithms
19. Appendix 2 - Assessments

Sudharsan Ravichandiran is a data scientist, researcher, best selling author, and YouTuber (search for "Sudharsan reinforcement learning"). He completed his Bachelor's in Information Technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, including Natural Language Processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow. He also authored a best-seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.