Learn To Create Artificially Intelligent Games Using Python3

Learn to create and activate a conda virtual environment in Anaconda to isolate project dependencies. Prevent library version conflicts when multiple AI game projects run on the same machine.

Learn basic arithmetic in Python, covering addition, subtraction, multiplication, division, and modulo, with integers and floats and using type and print to inspect data types.

Learn to access characters in a Python string using positive indexing (left to right starting at zero) and negative indexing (right to left starting at minus one) with practical examples.

Explore Python booleans, storing and accessing true or false values, and applying boolean logic, conditional statements, and logical operators (and, or, not) in games.

Learn how to access items from a Python list using positive and negative indexing, with zero-based starts, and see examples retrieving specific elements.

Learn Python list comprehension to write elegant one-line code, replacing loops with expressions. Explore syntax, loops, and conditionals through examples like adding 100 to list items and selecting even numbers.

Define an employee class, instantiate multiple objects, and use the constructor (__init__) to initialize base salary, overtime, and rate, then use a method to calculate salary.

Demonstrates multiple inheritance in Python by defining a doctor class that inherits from employee, uses super to call the base constructor, and shows method overriding.

Explore the fundamentals of pygame with a reusable skeleton template that covers imports, a main game loop, event handling, screen initialization, and rendering updates.

Move a rectangle in a python-based game by updating x and y in 50-unit steps using keyboard input (a, d, w), handle boundaries, and learn about timing for smoother motion.

Learn to restrict a game's player movement within the window boundary by checking x and y coordinates, accounting for the object's width, and preventing crossing walls.

Demonstrate a three level minimax lookahead using a backtracking bottom up tree, showing max and min players alternating and propagating values to select moves.

Explore a tic tac toe minimax example, with a max player maximizing the score and a human min player minimizing it, using a lookahead depth of two.

Develop a tic tac toe game with an artificially intelligent agent using the Minimax algorithm in Python3, competing against a human player. Install dependencies and run the game from resources.

Learn to set up an autoplayer using a minimax algorithm in a tic tac toe game, configure lookahead, and integrate the minimax player into the game code.

Discover how artificial intelligence creates data through environment interaction, beyond input-output patterns, using deep neural networks and reinforcement learning for robotics, sensor-to-action mappings, and AlphaGo games.

Define a simple policy in reinforcement learning to decide actions in blackjack, using a condition to hit or extend, and follow the policy in the game environment.

Explore how reinforcement learning uses rewards to tell the agent how good a given situation is, with negative rewards signaling bad outcomes and positive rewards guiding decision making.

Explore the Markov reward process, extending the Markov property with a reward function and transition probabilities between states, where states carry a reward that may be zero, positive, or negative.

Explore the q-learning equation in a deterministic environment, where the Q value equals reward plus gamma times the max Q value of the next state, linking value and Q-value.

Train an artificially intelligent player with Q learning in a grid environment. Explore rewards, penalties, including living penalties, and obstacles like walls, bombs, and water, then implement a from-scratch algorithm.

Explore how a 10 by 10 grid defines 100 states, with zero as start and rewards finish line and explosives, while Q tables update state–action values via temporal differential learning.