Python Reinforcement Learning Projects by Saito, Sean -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Python Reinforcement Learning Projects

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Contributors

About the authors About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Conventions used

Get in touch

Reviews

Up and Running with Reinforcement Learning

Introduction to this book

Expectations Hardware and software requirements Installing packages

What is reinforcement learning?

The agent Policy Value function Model Markov decision process (MDP)

Deep learning

Neural networks Backpropagation Convolutional neural networks

Advantages of neural networks

Implementing a convolutional neural network in TensorFlow

TensorFlow The Fashion-MNIST dataset Building the network Methods for building the network

build method fit method

Summary References

Balancing CartPole

OpenAI Gym

Gym Installation Running an environment Atari Algorithmic tasks MuJoCo Robotics

Markov models

CartPole

Summary

Playing Atari Games

Introduction to Atari games Building an Atari emulator

Getting started Implementation of the Atari emulator

Atari simulator using gym

Data preparation Deep Q-learning

Basic elements of reinforcement learning Demonstrating basic Q-learning algorithm

Implementation of DQN Experiments Summary

Simulating Control Tasks

Introduction to control tasks

Getting started The classic control tasks

Deterministic policy gradient

The theory behind policy gradient DPG algorithm Implementation of DDPG Experiments

Trust region policy optimization

Theory behind TRPO TRPO algorithm Experiments on MuJoCo tasks

Summary

Building Virtual Worlds in Minecraft

Introduction to the Minecraft environment Data preparation Asynchronous advantage actor-critic algorithm Implementation of A3C Experiments Summary

Learning to Play Go

A brief introduction to Go

Go and other board games Go and AI research

Monte Carlo tree search

Selection Expansion Simulation Update

AlphaGo

Supervised learning policy networks Reinforcement learning policy networks Value network Combining neural networks and MCTS

AlphaGo Zero

Training AlphaGo Zero Comparison with AlphaGo

Implementing AlphaGo Zero

Policy and value networks

preprocessing.py features.py network.py

Monte Carlo tree search

mcts.py

Combining PolicyValueNetwork and MCTS

alphagozero_agent.py

Putting everything together

controller.py train.py

Summary References

Creating a Chatbot

The background problem

Dataset

Step-by-step guide

Data parser Data reader Helper methods Chatbot model Training the data Testing and results

Summary

Generating a Deep Learning Image Classifier

Neural Architecture Search

Generating and training child networks Training the Controller Training algorithm

Implementing NAS

child_network.py cifar10_processor.py controller.py Method for generating the Controller

Generating a child network using the Controller train_controller method Testing ChildCNN

config.py train.py Additional exercises

Advantages of NAS Summary

Predicting Future Stock Prices

Background problem Data used Step-by-step guide

Actor script Critic script Agent script Helper script Training the data Final result

Summary

Looking Ahead

The shortcomings of reinforcement learning

Resource efficiency Reproducibility Explainability/accountability Susceptibility to attacks

Upcoming developments in reinforcement learning

Addressing the limitations Transfer learning Multi-agent reinforcement learning

Summary References

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