Artificial Intelligence: Reinforcement Learning in Python 

Introduction

Where to get the Code

Strategy for Passing the Course

Course Outline

Problem Setup and The Explore-Exploit Dilemma

Applications of the Explore-Exploit Dilemma

Epsilon-Greedy

Updating a Sample Mean

Designing Your Bandit Program

Comparing Different Epsilons

Optimistic Initial Values

UCB1

Bayesian / Thompson Sampling

Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1

Nonstationary Bandits

Bandit Summary, Real Data, and Online Learning

(Optional) Alternative Bandit Designs

What is Reinforcement Learning?

On Unusual or Unexpected Strategies of RL

Defining Some Terms

Naive Solution to Tic-Tac-Toe

Components of a Reinforcement Learning System

Notes on Assigning Rewards

The Value Function and Your First Reinforcement Learning Algorithm

Tic Tac Toe Code: Outline

Tic Tac Toe Code: Representing States

Tic Tac Toe Code: Enumerating States Recursively

Tic Tac Toe Code: The Environment

Tic Tac Toe Code: The Agent

Tic Tac Toe Code: Main Loop and Demo

Tic Tac Toe Summary

Tic Tac Toe: Exercise

Gridworld

The Markov Property

Defining and Formalizing the MDP

Future Rewards

Value Function Introduction

Value Functions

Bellman Examples

Optimal Policy and Optimal Value Function

MDP Summary

Intro to Dynamic Programming and Iterative Policy Evaluation

Gridworld in Code

Designing Your RL Program

Iterative Policy Evaluation in Code

Policy Improvement

Policy Iteration

Policy Iteration in Code

Policy Iteration in Windy Gridworld

Value Iteration

Value Iteration in Code

Dynamic Programming Summary

Monte Carlo Intro

Monte Carlo Policy Evaluation

Monte Carlo Policy Evaluation in Code

Policy Evaluation in Windy Gridworld

Monte Carlo Control

Monte Carlo Control in Code

Monte Carlo Control without Exploring Starts

Monte Carlo Control without Exploring Starts in Code

Monte Carlo Summary

Temporal Difference Intro

TD(0) Prediction

TD(0) Prediction in Code

SARSA

SARSA in Code

Q Learning

Q Learning in Code

TD Summary

Approximation Intro

Linear Models for Reinforcement Learning

Features

Monte Carlo Prediction with Approximation

Monte Carlo Prediction with Approximation in Code

TD(0) Semi-Gradient Prediction

Semi-Gradient SARSA

Semi-Gradient SARSA in Code

Course Summary and Next Steps

Stock Trading Project Section Introduction

Data and Environment

How to Model Q for Q-Learning

Design of the Program

Code pt 1

Code pt 2

Code pt 3

Code pt 4

Stock Trading Project Discussion

What is the Appendix?

Windows-Focused Environment Setup 2018

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

How to Succeed in this Course (Long Version)

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Proof that using Jupyter Notebook is the same as not using it

Python 2 vs Python 3

What order should I take your courses in? (part 1)

What order should I take your courses in? (part 2)

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