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Deep Reinforcement Learning Hands-On

You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788834247

Length 546 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

View More author details

Table of Contents (21) Chapters

Preface

1. What is Reinforcement Learning? FREE CHAPTER

2. OpenAI Gym

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. DQN Extensions

8. Stocks Trading Using RL

9. Policy Gradients – An Alternative

10. The Actor-Critic Method

11. Asynchronous Advantage Actor-Critic

12. Chatbots Training with RL

13. Web Navigation

14. Continuous Action Space

15. Trust Regions – TRPO, PPO, and ACKTR

16. Black-Box Optimization in RL

17. Beyond Model-Free – Imagination

18. AlphaGo Zero

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Index

Cross-entropy on FrozenLake

The next environment we'll try to solve using the cross-entropy method is FrozenLake. Its world is from the so-called "grid world" category, when your agent lives in a grid of size 4 × 4 and can move in four directions: up, down, left, and right. The agent always starts at a top-left position, and its goal is to reach the bottom-right cell of the grid. There are holes in the fixed cells of the grid and if you get into those holes, the episode ends and your reward is zero. If the agent reaches the destination cell, then it obtains the reward 1.0 and the episode ends.

To make life more complicated, the world is slippery (it's a frozen lake after all), so the agent's actions do not always turn out as expected: there is a 33% chance that it will slip to the right or to the left. You want the agent to move left, for example, but there is a 33% probability that it will indeed move left, a 33% chance that it will end up in the cell...

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Authors (1)

Maxim Lapan

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

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