4.7 out of 5
4.7
116 reviews on Udemy

Artificial Intelligence for Simple Games

Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games!
Instructor:
Jan Warchocki
1,434 students enrolled
English [Auto]
SOLVE THE TRAVELLING SALESMAN PROBLEM
Understand and implement Genetic Algorithms
Get the general AI framework
Understand how to use this tool to your own projects
SOLVE A COMPLEX MAZE
Understand and implement Q-Learning
Get the right Q-Learning intuition
Understand how to use this tool to your own projects
SOLVE MOUNTAIN CAR FROM OPENAI GYM
Understand and implement Deep Q-Learning
Build Artificial Neural Networks with Keras
Use the environments provided in OpenAI Gym
Understand how to use this tool to your own projects
SOLVE SNAKE
Understand and implement Deep Convolutional Q-Learning
Build Convolutional Neural Networks with Keras
Understand how to use this tool to your own projects

Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming?

If you’re looking for a creative way to dive into Artificial Intelligence, then ‘Artificial Intelligence for Simple Games’ is your key to building lasting knowledge.

Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more.

1. Whether you’re an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.

2. Key algorithms and concepts covered in this course include: Genetic Algorithms, Q-Learning, Deep Q-Learning with both Artificial Neural Networks and Convolutional Neural Networks.

3. Dive into SuperDataScience’s much-loved, interactive learning environment designed to build knowledge and intuition gradually with practical, yet challenging case studies.

4. Code flexibility means that students will be able to experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry.

‘AI for Simple Games’ Curriculum

Section #1 — Dive into Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge will be to build a spaceship that travels across all planets in the shortest time possible!

Section #2 — Learn the foundations of the model-free reinforcement learning algorithm, Q-Learning. Develop intuition and visualization skills, and try your hand at building a custom maze and design an AI able to find its way out.

Section #3 — Go deep with Deep Q-Learning. Explore the fantastic world of Neural Networks using the OpenAI Gym development environment and learn how to build AIs for many other simple games!

Section #4 — Finish off the course by building your very own version of the classic game, Snake! Here you’ll utilize Convolutional Neural Networks by building an AI that mimics the same behavior we see when playing Snake.

Installation

1
Installing Anaconda

Get the materials

1
Get the materials
2
BONUS: Learning Path

Genetic Algorithms Intuition

1
Plan of Attack
2
The DNA
3
The Fitness Function
4
The Population
5
The Selection
6
The Crossover
7
The Mutation

Genetic Algorithms Practical

1
Step 1 - The Introduction
2
Step 2 - Importing the libraries
3
Step 3 - Creating the bots
4
Step 4 - Initializing the random DNA
5
Step 5 - Building the Crossover method
6
Step 6 - Random Partial Mutations 1
7
Step 7 - Random Partial Mutations 2
8
Step 8 - Initializing the main code
9
Step 9 - Creating the first population
10
Step 10 - Starting the main loop
11
Step 11 - Evaluating the population
12
Step 12 - Sorting the population
13
Step 13 - Adding best previous bots to the population
14
Step 14 - Filling in the rest of the population
15
Step 15 - Displaying the results
16
Step 16 - Running the code

Q-Learning

1
Q-Learning Intuition: Plan of Attack
2
Q-Learning Intuition: What is Reinforcement Learning?
3
Q-Learning Intuition: The Bellman Equation
4
Q-Learning Intuition: The Plan
5
Q-Learning Intuition: Markov Decision Process
6
Q-Learning Intuition: Policy vs Plan
7
Q-Learning Intuition: Living Penalty
8
Q-Learning Intuition: Q-Learning Intuition
9
Q-Learning Intuition: Temporal Difference
10
Q-Learning Intuition: Q-Learning Visualization

Q-Learning Practical

1
Step 1 - Introduction
2
Step 2 - Importing the libraries
3
Step 3 - Defining the parameters
4
Step 4 - Environment and Q-Table initialization
5
Step 5 - Preparing the Q-Learning process 1
6
Step 6 - Preparing the Q-Learning process 2
7
Step 7 - Starting the Q-Learning process
8
Step 8 - Getting all playable actions
9
Step 9 - Playing a random action
10
Step 10 - Updating the Q-Value
11
Step 11 - Displaying the results
12
Step 12 - Running the code

Deep Q-Learning with ANNs

1
Deep Q-Learning Intuition: Plan of Attack
2
Deep Q-Learning Intuition: Step 1
3
Deep Q-Learning Intuition: Step 2
4
Deep Q-Learning Intuition: Experience Replay
5
Deep Q-Learning Intuition: Action Selection Policies

Deep Q-Learning Practical

1
Step 1 - Introduction
2
Step 2 - Brain - Importing the libraries
3
Step 3 - Brain - Building the Brain class
4
Step 4 - Brain - Creating the Neural Network
5
Step 5 - DQN Memory - Initializing the Experience Replay Memory
6
Step 6 - DQN Memory - Remembering new experience
7
Step 7 - DQN Memory - Getting the batches of inputs and targets
8
Step 8 - DQN Memory - Initializing the inputs and the targets
9
Step 9 - DQN Memory - Extracting transitions from random experiences
10
Step 10 - DQN Memory - Updating the inputs and the targets
11
Step 11 - Training - Importing the libraries
12
Step 12 - Training - Setting the parameters
13
Step 13 - Training - Initializing the environment, the brain and dqn
14
Step 14 - Training - Starting the main loop
15
Step 15 - Training - Starting to play the game
16
Step 16 - Training - Taking an action
17
Step 17 - Training - Updating the Environment
18
Step 18 - Training - Adding new experience, training the AI, updating cur. state
19
Step 19 - Training - Lowering epsilon and displaying the results
20
Step 20 - Running the code

Deep Convolutional Q-Learning

1
Deep Convolutional Q-Learning Intuition: Plan of Attack
2
Deep Convolutional Q-Learning Intuition: Deep Convolutional Q-Learning Intuition
3
Deep Convolutional Q-Learning Intuition: Eligibility Trace

Deep Convolutional Q-Learning Practical

1
Step 1 - Introduction
2
Step 2 - Brain - Importing the libraries
3
Step 3 - Brain - Starting building the Brain class
4
Step 4 - Brain - Creating the neural network
5
Step 5 - Brain - Building a method that will load a model
6
Step 6 - DQN - Building the Experience Replay Memory
7
Step 7 - Training - Importing the libraries
8
Step 8 - Training - Defining the parameters
9
Step 9 - Training - Initializing the Environment the Brain and the DQN
10
Step 10 - Training - Building a function to reset the current state
11
Step 11 - Training - Starting the main loop
12
Step 12 - Training - Resetting the Environment and starting to play the game
13
Step 13 - Training - Selecting an action to play
14
Step 14 - Training - Updating the environment
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.7
4.7 out of 5
116 Ratings

Detailed Rating

Stars 5
55
Stars 4
41
Stars 3
12
Stars 2
3
Stars 1
5
30-Day Money-Back Guarantee

Includes

12 hours on-demand video
2 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion

Community

For Professionals

For Businesses

We support Sales, Marketing, Account Management and CX professionals. Learn new skills. Share your expertise. Connect with experts. Get inspired.

Community

Partnership Opportunities

Layer 1
samcx.com
Logo
Register New Account
Compare items
  • Total (0)
Compare
0