Book Image

Keras Reinforcement Learning Projects

By : Giuseppe Ciaburro
Book Image

Keras Reinforcement Learning Projects

By: Giuseppe Ciaburro

Overview of this book

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Overview of Keras Reinforcement Learning
Overview of Keras Reinforcement Learning
Basic concepts of machine learning
Getting started with reinforcement learning
Reinforcement learning algorithms
Summary
2
Simulating Random Walks
Simulating Random Walks
Random walks
Markov chains
Summary
3
Optimal Portfolio Selection
Optimal Portfolio Selection
Dynamic Programming
Optimizing a financial portfolio
Solving the knapsack problem using Dynamic Programming
Summary
4
Forecasting Stock Market Prices
Forecasting Stock Market Prices
Monte Carlo methods
Amazon stock price prediction using Python
Summary
5
Delivery Vehicle Routing Application
Delivery Vehicle Routing Application
Temporal difference learning
Basics of graph theory
Finding the shortest path
The Vehicle Routing Problem
Summary
6
Continuous Balancing of a Rotating Mechanical System
Continuous Balancing of a Rotating Mechanical System
Neural network basic concepts
Classifying breast cancer using the neural network
Deep reinforcement learning
Continuous control with deep reinforcement learning
Summary
7
Dynamic Modeling of a Segway as an Inverted Pendulum System
Dynamic Modeling of a Segway as an Inverted Pendulum System
How Segways work
OpenAI Gym
The CartPole system
Summary
8
Robot Control System Using Deep Reinforcement Learning
Robot Control System Using Deep Reinforcement Learning
Robot control
The FrozenLake environment
Summary
9
Handwritten Digit Recognizer
Handwritten Digit Recognizer
Handwritten digit recognition
Handwritten digit recognition using an autoencoder
Deep autoencoder Q-learning
Summary
10
Playing the Board Game Go
Playing the Board Game Go
Game theory
The Go game
The AlphaGo project
Summary
11
What's Next?
What's Next?
Reinforcement-learning applications in real life
Next steps for reinforcement learning
Summary
12
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Finding the shortest path

So far, we have only dealt with creating a graph by defining the list of vertices and the connections between them. But, we haven't said anything about the characteristics of these connections. We can state that it is precisely the characteristics of the connections that have made the graphs particularly useful for the representation of a very large number of problems.

Regarding the edge, we have only distinguished between undirected and directed edges. We can add to them another type: weighted edges. Directed or undirected edges can also have a weight or a quantitative value associated with them. This property can be used to define characteristics such as distances between the nodes of a road network, the costs necessary to find a specific resource, the capacity of a line, and the energy required to move between locations along a route. Edges can...

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