Awesome Open Source
Awesome Open Source

Deep Reinforcement Learning With Python

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

About the book

Book Cover

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

In addition to exploring RL basics and foundational concepts such as the Bellman equation, Markov decision processes, and dynamic programming, this second edition dives deep into the full spectrum of value-based, policy-based, and actor- critic RL methods with detailed math. 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 implement popular RL algorithms effortlessly. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.

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Table of Contents

Download the detailed and complete table of contents from here.

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit

3. Bellman Equation and Dynamic Programming

4. Monte Carlo Methods

  • 4.1. Understanding the Monte Carlo Method
  • 4.2. Prediction and Control Tasks
  • 4.3. Monte Carlo Prediction
  • 4.4. Understanding the BlackJack Game
  • 4.5. Every-visit MC Prediction with Blackjack Game
  • 4.6. First-visit MC Prediction with Blackjack Game
  • 4.7. Incremental Mean Updates
  • 4.8. MC Prediction (Q Function)
  • 4.9. Monte Carlo Control
  • 4.10. On-Policy Monte Carlo Control
  • 4.11. Monte Carlo Exploring Starts
  • 4.12. Monte Carlo with Epsilon-Greedy Policy
  • 4.13. Implementing On-Policy MC Control
  • 4.14. Off-Policy Monte Carlo Control
  • 4.15. Is MC Method Applicable to all Tasks?

5. Understanding Temporal Difference Learning

6. Case Study: The MAB Problem

7. Deep Learning Foundations

  • 7.1. Biological and artifical neurons
  • 7.2. ANN and its layers
  • 7.3. Exploring activation functions
  • 7.4. Forward and backward propgation in ANN
  • 7.5. Building neural network from scratch
  • 7.6. Recurrent neural networks
  • 7.7. LSTM-RNN
  • 7.8. Convolutional neural networks
  • 7.9. Generative adversarial networks

8. Getting to Know TensorFlow

9. Deep Q Network and its Variants

  • 9.1. What is Deep Q Network?
  • 9.2. Understanding DQN
  • 9.3. Playing Atari Games using DQN
  • 9.4. Double DQN
  • 9.5. DQN with Prioritized Experience Replay
  • 9.6. Dueling DQN
  • 9.7. Deep Recurrent Q Network

10. Policy Gradient Method

  • 10.1. Why Policy Based Methods?
  • 10.2. Policy Gradient Intuition
  • 10.3. Understanding the Policy Gradient
  • 10.4. Deriving Policy Gradien
  • 10.5. Variance Reduction Methods
  • 10.6. Policy Gradient with Reward-to-go
  • 10.7. Cart Pole Balancing with Policy Gradient
  • 10.8. Policy Gradient with Baseline

11. Actor Critic Methods - A2C and A3C

  • 11.1. Overview of Actor Critic Method
  • 11.2. Understanding the Actor Critic Method
  • 11.3. Advantage Actor Critic
  • 11.4. Asynchronous Advantage Actor Critic
  • 11.5. Mountain Car Climbing using A3C
  • 11.6. A2C Revisited

12. Learning DDPG, TD3 and SAC

  • 12.1. Deep Deterministic Policy Gradient
  • 12.2. Components of DDPG
  • 12.3. Putting it all together
  • 12.4. Algorithm - DDPG
  • 12.5. Swinging Up the Pendulum using DDPG
  • 12.6. Twin Delayed DDPG
  • 12.7. Components of TD3
  • 12.8. Putting it all together
  • 12.9. Algorithm - TD3
  • 12.10. Soft Actor Critic
  • 12.11. Components of SAC
  • 12.12. Putting it all together
  • 12.13. Algorithm - SAC

13. TRPO, PPO and ACKTR Methods

  • 13.1 Trust Region Policy Optimization
  • 13.2. Math Essentials
  • 13.3. Designing the TRPO Objective Function
  • 13.4. Solving the TRPO Objective Function
  • 13.5. Algorithm - TRPO
  • 13.6. Proximal Policy Optimization
  • 13.7. PPO with Clipped Objective
  • 13.9. Implementing PPO-Clipped Method
  • 13.10. PPO with Penalized Objective
  • 13.11. Actor Critic using Kronecker Factored Trust Region
  • 13.12. Math Essentials
  • 13.13. Kronecker-Factored Approximate Curvature (K-FAC)
  • 13.14. K-FAC in Actor Critic

14. Distributional Reinforcement Learning

  • 14.1. Why Distributional Reinforcement Learning?
  • 14.2. Categorical DQN
  • 14.3. Playing Atari games using Categorical DQN
  • 14.4. Quantile Regression DQN
  • 14.5. Math Essentials
  • 14.6. Understanding QR-DQN
  • 14.7. Distributed Distributional DDPG

15. Imitation Learning and Inverse RL

  • 15.1. Supervised Imitation Learning
  • 15.2. DAgger
  • 15.3. Deep Q learning from Demonstrations
  • 15.4. Inverse Reinforcement Learning
  • 15.5. Maximum Entropy IRL
  • 15.6. Generative Adversarial Imitation Learning

16. Deep Reinforcement Learning with Stable Baselines

17. Reinforcement Learning Frontiers

  • 17.1. Meta Reinforcement Learning
  • 17.2. Model Agnostic Meta Learning
  • 17.3. Understanding MAML
  • 17.4. MAML in the Supervised Learning Setting
  • 17.5. Algorithm - MAML in Supervised Learning
  • 17.6. MAML in the Reinforcement Learning Setting
  • 17.7. Algorithm - MAML in Reinforcement Learning
  • 17.8. Hierarchical Reinforcement Learning
  • 17.9. MAXQ Value Function Decomposition
  • 17.10. Imagination Augmented Agents

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