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Index
Title Page Copyright and Credits
Keras Reinforcement Learning Projects
Packt Upsell
Why subscribe? Packt.com
Contributors
About the author 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 Download the color images Conventions used
Get in touch
Reviews
Overview of Keras Reinforcement Learning
Basic concepts of machine learning
Discovering the different types of machine learning
Supervised learning Unsupervised learning Reinforcement learning
Building machine learning models step by step
Getting started with reinforcement learning
Agent-environment interface Markov Decision Process Discounted cumulative reward Exploration versus exploitation
Reinforcement learning algorithms
Dynamic Programming Monte Carlo methods Temporal difference learning
SARSA Q-learning Deep Q-learning
Summary
Simulating Random Walks
Random walks
One-dimensional random walk Simulating 1D random walk
Markov chains
Stochastic process Probability calculation Markov chain definition
Transition matrix Transition diagram
Weather forecasting with Markov chains Generating pseudorandom text with Markov chains
Summary
Optimal Portfolio Selection
Dynamic Programming
Divide and conquer versus Dynamic Programming Memoization Dynamic Programming in reinforcement-learning applications
Optimizing a financial portfolio
Optimization techniques
Solving the knapsack problem using Dynamic Programming
Different approaches to the problem
Brute force Greedy algorithms Dynamic Programming
Summary
Forecasting Stock Market Prices
Monte Carlo methods
Historical background Basic concepts of the Monte Carlo simulation
Monte Carlo applications Numerical integration using the Monte Carlo method
Monte Carlo for prediction and control
Amazon stock price prediction using Python
Exploratory analysis The Geometric Brownian motion model Monte Carlo simulation
Summary
Delivery Vehicle Routing Application
Temporal difference learning
SARSA Q-learning
Basics of graph theory
The adjacency matrix Adjacency lists Graphs as data structures in Python Graphs using the NetworkX package
Finding the shortest path
The Dijkstra algorithm
The Dijkstra algorithm using the NetworkX package The Google Maps algorithm
The Vehicle Routing Problem Summary
Continuous Balancing of a Rotating Mechanical System
Neural network basic concepts
The Keras neural network model
Classifying breast cancer using the neural network Deep reinforcement learning
The Keras–RL package
Continuous control with deep reinforcement learning Summary
Dynamic Modeling of a Segway as an Inverted Pendulum System
How Segways work
System modeling basics
OpenAI Gym
OpenAI Gym methods OpenAI Gym installation
The CartPole system
Q-learning solution Deep Q-learning solution
Summary
Robot Control System Using Deep Reinforcement Learning
Robot control
Robotics overview Robot evolution
First-generation robots Second-generation robots Third-generation robots Fourth-generation robots
Robot autonomy Robot mobility Automatic control
Control architectures
The FrozenLake environment
The Q-learning solution A Deep Q-learning solution
Summary
Handwritten Digit Recognizer
Handwritten digit recognition
Optical Character Recognition Computer vision
Handwritten digit recognition using an autoencoder
Loading data Model architecture
Deep autoencoder Q-learning Summary
Playing the Board Game Go
Game theory
Basic concepts Game types
Cooperative games Symmetrical games Zero-sum games Sequential games
Game theory applications
Prisoner's dilemma Stag hunt Chicken game
The Go game
Basic rules of the game Scoring rules
The AlphaGo project
The AlphaGo algorithm
Monte Carlo Tree Search Convolutional networks
Summary
What's Next?
Reinforcement-learning applications in real life
DeepMind AlphaZero IBM Watson The Unity Machine Learning Agents toolkit FANUC industrial robots Automated trading systems using reinforcement learning
Next steps for reinforcement learning
Inverse reinforcement learning Learning by demonstration Deep Deterministic Policy Gradients Reinforcement learning from human preferences Hindsight Experience Replay
Summary
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