Keras Reinforcement Learning Projects
更新时间：2021-08-13 15:26:40
最新章节：Leave a review - let other readers know what you think封面
Title Page
Copyright and Credits
Keras Reinforcement Learning Projects
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Preface
Who this book is for
What this book covers
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Conventions used
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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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更新时间：2021-08-13 15:26:40