You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Length 826 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Chatbots

Author (1):

Maxim Lapan

View More author details

Table of Contents (28) Chapters

Preface

1. What Is Reinforcement Learning?

2. OpenAI Gym FREE CHAPTER

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. Higher-Level RL Libraries

8. DQN Extensions

9. Ways to Speed up RL

10. Stocks Trading Using RL

11. Policy Gradients – an Alternative

12. The Actor-Critic Method

13. Asynchronous Advantage Actor-Critic

14. Training Chatbots with RL

15. The TextWorld Environment

16. Web Navigation

17. Continuous Action Space

18. RL in Robotics

19. Trust Regions – PPO, TRPO, ACKTR, and SAC

20. Black-Box Optimization in RL

21. Advanced Exploration

22. Beyond Model-Free – Imagination

23. AlphaGo Zero

24. RL in Discrete Optimization

25. Multi-agent RL

26. Other Books You May Enjoy

27. Index

The Bellman equation of optimality

To explain the Bellman equation, it's better to go a bit abstract. Don't be afraid; I'll provide concrete examples later to support your learning! Let's start with a deterministic case, when all our actions have a 100% guaranteed outcome. Imagine that our agent observes state s₀ and has N available actions. Every action leads to another state, s₁ ... s_N, with a respective reward, r₁ ... r_N. Also, assume that we know the values, V_i, of all states connected to state s₀. What will be the best course of action that the agent can take in such a state?

Figure 5.3: An abstract environment with N states reachable from the initial state

If we choose the concrete action, a_i, and calculate the value given to this action, then the value will be . So, to choose the best possible action, the agent needs to calculate the resulting values for every action and choose the maximum possible outcome. In other words, . If we are using the...