The Deep Reinforcement Learning Guide To Connect Four

Master Deep Reinforcement Learning by building an AI agent from scratch to excel at Connect Four.

What you'll learn

Implement Tic-tac-toe and Connect Four in Python from scratch

Understand the foundations of Reinforcement Learning (RL)

Understand the basics of Deep Learning (DL)

Handle the challenges combining RL and Dl into Deep Reinforcement Learning (DRL)

Implement an artifical intelligent (AI) agent that plays Connect Four using DRL

Develop AI agents for simple and complex games

Build and optimize AI models in Python

Understand heuristic approaches in implementing bots for games

Requirements

Basic programming knowledge: Familiarity with Python will help you follow along with the coding examples and exercises. However, we have a quick Python refresher with all the concepts that we will use later.

Basic understanding of machine learning concepts: Some exposure to general machine learning principles is helpful but not required (like linear regression, training models, understanding data, etc).

Familiarity with development tools: Having basic experience using an IDE (e.g., PyCharm) or Jupyter notebooks can streamline the coding process, but we will guide you on how to set these up and how to use them.

For beginners, don’t worry! We’ll cover the essential concepts in reinforcement learning and neural networks, and you’ll be able to follow along even with limited prior experience. No specialized equipment is required beyond a computer capable of running Python which we’ll help you set up.

Description

Are you ready to elevate your AI skills by mastering Deep Reinforcement Learning (DRL) through an exciting project?  Embark on a comprehensive journey into the world of DRL with our meticulously designed course, "The Deep Reinforcement Learning Guide to Connect Four." This course is tailored to guide you from foundational concepts to advanced applications, culminating in the creation of a proficient DRL player for the game of Connect Four.Course Highlights:Foundations of Reinforcement Learning: Begin with an in-depth exploration of tabular reinforcement learning using the classic game of Tic-Tac-Toe. Understand the core principles and methodologies that form the bedrock of Reinforcement Learning (RL).Transition to Complex Environments: Progress to the more intricate game of Connect Four, where you'll learn to implement heuristics to navigate the limitations of tabular methods.Introduction to Neural Networks: Dive into the realm of neural networks, focusing on their role as value approximation functions. You'll gain hands-on experience by constructing a neural network library from scratch using only NumPy, demystifying the mechanics behind these powerful models.Building a DRL Player: In the culminating chapter, integrate all acquired knowledge to develop a Deep Reinforcement Learning player for Connect Four. Despite utilizing a straightforward architecture with dense layers, your DRL agent will exhibit impressive gameplay capabilities.Why Enroll?Comprehensive Curriculum: Our course offers a structured learning path, ensuring a solid grasp of both theoretical concepts and practical implementations.Hands-On Projects: Engage in a project that uses deep reinforce learning and provide tangible outcomes, enhancing your portfolio.Expert Guidance: Benefit from clear, concise explanations and step-by-step instructions, making complex topics accessible.Who Should Enroll?This course is ideal for:Aspiring AI and machine learning enthusiasts seeking to delve into reinforcement learning.Developers aiming to enhance their skill set with advanced DRL techniques.Anyone with a passion for understanding the intricacies of AI through practical applications.Join us in this educational adventure and equip yourself with the skills to design and implement sophisticated DRL agents from the ground up. Enroll now to start your journey in building advanced AI agents!

Overview

Section 1: Introduction to Python and Essential Libraries

Lecture 1 Preview

Lecture 2 Downloading Python

Lecture 3 Python Data Types and Basic Operations

Lecture 4 Loops, Functions, and Objects

Lecture 5 Flow Control and Exceptions

Lecture 6 Numpy Basics

Lecture 7 Useful Libraries

Section 2: Reinforcement Learning and the Game of Tic-Tac-Toe

Lecture 8 Introduction

Lecture 9 The Rules of Tic-Tac-Toe

Lecture 10 Building the Board Object - Part A

Lecture 11 Building the Board Object - Part B

Lecture 12 Creating the First Simple Players and the Game of Tic-Tac-Toe

Lecture 13 Game Tree

Lecture 14 Minimax Algorithm

Lecture 15 Implementing the Minimax Player

Lecture 16 Dynamic Programming (DP)

Lecture 17 Implementing the DP Player

Lecture 18 Monte Carlo (MC) Methods

Lecture 19 Implementing the MC Player

Lecture 20 Temporal Difference (TD) Learning

Lecture 21 Implementing the TD Player - Part A

Lecture 22 Implementing the TD Player - Part B

Lecture 23 Tabular Reinforcement Learning

Lecture 24 Equivalent Board Positions

Lecture 25 Identifying Representative Board Positions

Lecture 26 Exercise 1

Lecture 27 Exercise 1 - Solution

Section 3: Transition to Connect Four

Lecture 28 Introduction

Lecture 29 Rules of Connect Four

Lecture 30 Building the Game and Basic Players

Lecture 31 Scalability Challenge

Lecture 32 Heuristics

Lecture 33 Implementing the Simple Heuristic Player

Lecture 34 N-Lookahead

Lecture 35 Implementing the Lookahead Heuristic Player

Lecture 36 Exercise 2

Lecture 37 Exercise 2 - Solution

Section 4: Neural Networks as Approximation Functions

Lecture 38 Introduction

Lecture 39 Linear Regression

Lecture 40 Layers of a Neural Network

Lecture 41 Matrix Multiplication

Lecture 42 Multiple Layers and Activation Functions

Lecture 43 Backpropagation

Lecture 44 Loading the MNIST Dataset

Lecture 45 MNIST Example Part 1

Lecture 46 MNIST Example Part 2

Lecture 47 Regularization

Lecture 48 Exercise 3

Lecture 49 Create a Simple Framework for Neural Networks Part A

Lecture 50 Create a Simple Framework for Neural Networks Part B

Section 5: Developing a Deep Reinforcement Learning Agent for Connect Four

Lecture 51 Introduction

Lecture 52 Bringing Everything Together

Lecture 53 Challenges in Applying Deep Learning to Reinforcement Learning

Lecture 54 Implementing the Replay Buffer and Equivalent Positions for Connect Four

Lecture 55 Implementing Our Deep Reinforcement Learning Player Part A

Lecture 56 Implementing Our Deep Reinforcement Learning Player Part B

Lecture 57 What Next?

This course is designed for anyone interested in learning how to build AI agents using deep reinforcement learning.,Aspiring AI developers: If you want to learn how to create AI models that can play games, make decisions, and optimize strategies, this course will guide you through step by step.,Software developers and engineers: If you’re looking to expand your skill set into AI, neural networks, and reinforcement learning, this course will help you build a solid foundation.,Data scientists and machine learning enthusiasts: Those with an interest in reinforcement learning and its applications to game-playing AI, such as Tic-Tac-Toe and Connect Four, will find this course practical and engaging.,Students and academics: If you’re studying computer science, machine learning, or AI, this course offers practical examples and hands-on coding experience to reinforce theoretical knowledge.,Beginners to AI and neural networks: Even if you have limited experience with AI or Python programming, this course is designed to ease you into these topics and help you understand how AI agents learn through reinforcement learning.,Whether you are looking to strengthen your knowledge of AI algorithms or explore how deep reinforcement learning can be applied to real-world scenarios, this course provides valuable insights and practical experience.