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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

GA on CartPole

The source code is in Chapter16/03_cartpole_ga.py and it has lots in common with our ES example. The difference is in the lack of the gradient ascent code, which was replaced by the network mutation function as follows:

def mutate_parent(net):
    new_net = copy.deepcopy(net)
    for p in new_net.parameters():
        noise_t = torch.from_numpy(np.random.normal(size=p.data.size()).astype(np.float32))
        p.data += NOISE_STD * noise_t
    return new_net

The goal of the function is to create a mutated copy of the given policy by adding a random noise to all weights. The parent's weights are kept untouched, as a random selection of the parent is performed with replacement, so this network could be used again later.

NOISE_STD = 0.01
POPULATION_SIZE = 50
PARENTS_COUNT = 10

The count of hyperparameters is even smaller than with ES and includes the standard deviation of the noise added-on mutation, the population size, and the number of top performers used to...

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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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