Learn To Create Artificially Intelligent Games Using Python3

Learn To Create Artificially Intelligent Games Using Python3

Learn To Create Artificially Intelligent Games Using Python3

Learn to implement basic to advanced deep learning, artificial intelligence algorithms for real world games!

Language: english

Note: 0/5 (0 notes) 271 students  New course 

Instructor(s): Sachin Kafle

Last update: 2022-05-19

What you’ll learn

  • Learn to implement MinMax algorithm
  • Learn about Q-Learning by implementing games
  • Learn about Artificial Intelligence in games
  • Learn about gym module
  • Implement Deep Q-Learning
  • Implement Deep convolution Q-Learning
  • Learn about Tensorflow and Keras
  • Learn to build complex AI player player
  • Learn about Bellman equation and Dynamic Programming
  • Learn about Monte-Carlo simulation
  • Learn to implement Neural Network from Scratch

 

Requirements

  • High school Mathematics: Basic Probability and Statistics
  • No programming experience required.

 

Description

If you’re interested in learning how to make your own Artificially Intelligent games using Python, then this is the course for you!

This course is full of tutorial videos along with materials which one can run to get familiar with this discipline. You no longer need to read complex research papers and have a solid foundation in mathematics to get going. Just follow this course and materials and you’re on your way.

Let’s take a look at the structure of this course:

We are going to start with a simple game that implements popular board game algorithm: MinMax. In this game we are going to create TicTacToe and write an algorithm that plays against human player and tries to beat human player.

Next we are going to learn about gym module: a popular library which can be used to write and test our AI algorithms.

After that, we are going to learn about Bellman Equation and Dynamic Programming. We are going to learn how to find the optimal value of the states using Bellman equations through model dynamics. We are going to implement maze game to implement Q-learning algorithm.

Then, we are going to learn about Monte-Carlo Simulation. We are going to check how value function can be predicted using Monte Carlo simulation when model dynamics is unknown.

Similarly, we are going to implement following games throughout this course:

1. BlackJack game using Monte-Carlo and Q-Learning

2. Pacman using Deep Convolution Neural Network

3. Make unbeatable AI TicTacToe player using Tensorflow and Keras (Human Vs AI)

4. MinMax algorithm for Board game

General Q/A’s:

When most people hear the term artificial intelligence, the first thing they usually think of is robots. That’s because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include mimicking human cognitive activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception, to the extent that these can be concretely defined. Some believe that innovators may soon be able to develop systems that exceed the capacity of humans to learn or reason out any subject. But others remain skeptical because all cognitive activity is laced with value judgments that are subject to human experience.

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

 

Who this course is for

  • Beginners who want to learn to create Artificially intelligent games
  • Programmers who want to implement AI algorithms
  • Beginners who want to learn complex algorithms in fun way by creating games
  • Anyone who want to learn python, pygame (game development tool) and Artificial Intelligence in general.

 

Course content

  • Introduction
    • Introduction
  • Setup Anaconda and Install Dependencies for Project
    • Install Anaconda
    • Create Virtual Environment
    • Install Dependencies/Libraries for the Course
    • Download Visual Studio Code
  • Python Essentials
    • What is Python?
    • Introduction to the data types
    • Basic Arithmetic in Python
    • Operations on Numbers
    • Introduction to Strings in Python
    • Access elements of String
    • Formatting strings
    • Introduction to the variables
    • Create Variables in Python
    • Introduction to Booleans in Python
    • Learn to create conditions
    • “is” operator in Python
    • Logical statements
    • Introduction to conditional statements
    • if else statements
    • Introduction to Data Structures
    • Checking type of Data Structures
    • How to access the items from the list?
    • Introduction to the loops in Python
    • Infinite while loop (Game Loop)
    • Finite Game Loop
    • For loop
    • Important: List Comprehension for Game Development
    • What is Function and Why we need it?
    • Learn to create Functions?
    • Learn about return statements
    • Introduction to the section
    • What is Object Oriented Programming?
    • Class and Objects
    • Class and Objects Continued
    • Constructor in Python
    • What is Inheritance?
    • Multiple Inheritance
  • Pygame Refresher
    • Introduction to the pygame
    • Pygame coordinate System
    • Introduction to Pygame shape
    • Draw shapes using Pygame
    • Color Picker
    • Fundamentals of Pygame — skeleton code
    • Render a rectangle in the Screen
    • Movement of the shapes
    • Smoothen the movement using FPS
    • Make movement within Boundary
  • Introduction to MinMax Algorithm
    • Introduction to Board Games
    • Tree representation of Game
    • Lookahead Problem
    • Solution of Lookahead problem
    • Heuristic Evaluation of Board
    • Example of Heuristic
    • Introduction to MinMax algorithm
    • Example of MinMax
    • MinMax Example for TicTacToe
    • MinMax Algorithm
  • Creating TicTacToe using MinMax algorithm
    • introduction to Game
    • Introduction to Project Files
    • Creating Indecisive Player (Random)
    • Implementing MinMax
    • Calculating Value/Heuristic for Min Max player
    • Implementing MinMax algorithm
    • Setting up Autoplayer (Artificial Intelligent Player)
    • Playing against AI player and Tuning algorithm
  • Introduction to Artificial Intelligence
    • Motivation for Artificial Intelligence
    • Reinforcement Learning
    • Environment
    • Rewards
    • Path
    • Typical RL scenario
    • Policy
    • Rewards
    • Value of the State
    • Model
  • Key Terms of Artificial Intelligence (Important)
    • Markov Property and Markov Chain
    • Markov Reward Process
    • Markov Decision Process
  • Bellman Equation and Dynamic Programming
    • Introduction
    • Tribute to Bellman
    • Value Function
    • Bellman Equation
    • Example
    • Plan
    • Non Deterministic Environment
    • Markov Decision Process + Bellman
    • Introduction to Q-Learning
    • Equation of Q-Learning
    • Q value for Non-Deterministic Environment
    • Temporal Difference
  • Implementation of Q-Learning to Find Optimal Path
    • Introduction to Project
    • Introduction to Project Files
    • Creating Environment
    • Briefing about Q-Table
    • Example of Q-Table
    • Q-Agent
    • Possible Actions
    • Iterations
    • Action Selection Policy (Returning max Q value)
    • Implementing Temporal Difference
    • Executing Game/q-Learning Algorithm
  • Introduction to “gym” module
    • The “gym” module
    • Example of Gym Environment
    • Creating Gym Environment
    • Getting started with Gym
    • State space and Action space
    • Transitional Probability
    • CartPole Example
    • Tennis Game with Random Policy
    • CartPole with Random Policy
  • Monte Carlo Simulation
    • Why Monte Carlo Simulation is important?
    • Monte Carlo Simulation
    • Monte Carlo Method (MC – method)
    • First Visit vs Every Visit MC
    • BlackJack Example
  • Implementing Monte Carlo Predictions
    • BlackJack Game and Rules of the Game
    • Creating BlackJack Environment
    • Defining Policy
    • Generating Episodes
    • Implementing MC simulation
    • Calculate Value of State using MC simulation
  • Creating BlackJack Game
    • Action Selection Policy (Epsilon-Greedy)
    • Introduction to Project Files
    • Q-Table
    • Implementing Epsilon Greedy Policy
    • (State, Action, Reward) of Episodes
    • Introduction to Discount Parameter
    • Implementing Temporal Difference (update Q-values)
    • AI Player steps
    • Making AI to play game
    • Training the Q-Learning model and Running Game
  • Neural Network Refresher
    • Introduction to Artificial Intelligence
    • Introduction to Neural Networks
    • Inspiration and representation for Neural Network
    • History and Application of Neural Network
    • Example of neural network
    • Updating the weights [partial differentiation]
    • Introduction to partial differentiation
    • Introduction to the Activation Function
    • Why do we need bias in the program
    • Why we use regularization in the Neural Network
    • Introduction to the gradient descent [review]
    • Introduction to Stochastic Gradient Descent and Adam Optimizer
    • Introduction to mini-batch SGD
  • Scratch Implementation of Neural Network
    • Setting up environment and coding single neuron
    • Coding neuron layer
    • Using dot product to code neuron layer
    • Coding dense layer [must know Object Oriented Programming]
    • Introduction to Activation Function
    • Implementation of activation function [step and sigmoid]
    • Implementation of activation function [tanh and ReLu]
  • Tensorflow and Keras
    • What is Tensorflow
    • Rank of Tensors
    • Program Elements of Tensorflow
    • Examples
    • Introduction to Keras
    • Keras models (Important)
    • Implementing Neural Network using Keras
  • TicTacToe Tensorflow
    • Introduction to Project Files
    • Creating model for the Game
    • Preprocess the state
    • Define Independent (input) and Dependent (output) Variable
    • Training the model
    • Predict from the model
    • TicTacToe Model
    • TicTacToe Neural Network
    • Creating Neural Network Player
  • Introduction to Deep Q-Learning and Deep Convolution Q-Learning
    • Introduction to Deep Q-Learning
    • Action Selection Policy
    • Exploration vs Exploitation
    • Deep Convolution Q-Learning
  • Convolution Neural Network
    • Introduction to convolution neural network
    • How ConvNet works?
    • Convolution Layer
    • RELU Layer
    • Pooling Layer
    • BackPropagation
  • Deep Convolution Q-Learning Practical: Pacman game
    • Introduction to Replay Buffer
    • Mean Squared Error
    • Main Network and Target Network
    • Creating Environment
    • Solving ROM error
    • Build Convolution Neural Network
    • Store Transition in Replay buffer
    • Build Main Network and Target Network
    • Epsilon Greedy (Action-Selection Policy)
    • Training the neural network
    • Fit the model
    • Preprocess the state
    • Training model for multiple iterations
    • Simulating the game and storing transitions
    • Testing the game
  • Any games you want to suggest?
    • Farewell

 

Learn To Create Artificially Intelligent Games Using Python3Learn To Create Artificially Intelligent Games Using Python3

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