You're reading from Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781839210686

Length 760 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

Preface

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit FREE CHAPTER

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. Other Books You May Enjoy

19. Index

Appendix 1 – Reinforcement Learning Algorithms

1. Appendix 2 – Assessments

Chapter 1 – Fundamentals of Reinforcement Learning

In supervised and unsupervised learning, the model (agent) learns based on the given training dataset, whereas, in reinforcement learning (RL), the agent learns by directly interacting with the environment. Thus RL is essentially an interaction between the agent and its environment.
The environment is the world of the agent. The agent stays within the environment. For instance, in the chess game, the chessboard is the environment since the chess player (agent) learns to play chess within the chessboard (environment). Similarly, in the Super Mario Bros game, the world of Mario is called the environment.
The deterministic policy maps the state to one particular action, whereas the stochastic policy maps the state to the probability distribution over an action space.
The agent interacts with the environment by performing actions, starting from the initial state until they reach the final state...