Flappy Bird NEAT AI
Flappy Bird NEAT AI, available at Free, has an average rating of 3.35, with 12 lectures, based on 75 reviews, and has 6184 subscribers.
You will learn about Neat AI Genetic Algorithm This course is ideal for individuals who are Python Developers or Game Developers or Bignner Python Developer or Computer Science Engineer It is particularly useful for Python Developers or Game Developers or Bignner Python Developer or Computer Science Engineer.
Enroll now: Flappy Bird NEAT AI
Summary
Title: Flappy Bird NEAT AI
Price: Free
Average Rating: 3.35
Number of Lectures: 12
Number of Published Lectures: 12
Number of Curriculum Items: 12
Number of Published Curriculum Objects: 12
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Neat AI Genetic Algorithm
Who Should Attend
- Python Developers
- Game Developers
- Bignner Python Developer
- Computer Science Engineer
Target Audiences
- Python Developers
- Game Developers
- Bignner Python Developer
- Computer Science Engineer
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures (“complexifying”).
Traditionally a neural network topology is chosen by a human experimenter, and effective connection weight values are learned through a training procedure. This yields a situation whereby a trial and error process may be necessary in order to determine an appropriate topology. NEAT is an example of a topology and weight evolving artificial neural network (TWEANN) which attempts to simultaneously learn weight values and an appropriate topology for a neural network.
In order to encode the network into a phenotype for the GA, NEAT uses a direct encoding scheme which means every connection and neuron is explicitly represented. This is in contrast to indirect encoding schemes which define rules that allow the network to be constructed without explicitly representing every connection and neuron allowing for more compact representation.
The NEAT approach begins with a perceptron-like feed-forward network of only input neurons and output neurons. As evolution progresses through discrete steps, the complexity of the network’s topology may grow, either by inserting a new neuron into a connection path, or by creating a new connection between (formerly unconnected) neurons.
Competing conventions
The competing conventions problem arises when there is more than one way of representing information in a phenotype. For example, if a genome contains neurons A, B and C and is represented by [A B C], if this genome is crossed with an identical genome (in terms of functionality) but ordered [C B A] crossover will yield children that are missing information ([A B A] or [C B C]), in fact 1/3 of the information has been lost in this example. NEAT solves this problem by tracking the history of genes by the use of a global innovation number which increases as new genes are added. When adding a new gene the global innovation number is incremented and assigned to that gene. Thus the higher the number the more recently the gene was added. For a particular generation if an identical mutation occurs in more than one genome they are both given the same number, beyond that however the mutation number will remain unchanged indefinitely.
These innovation numbers allow NEAT to match up genes which can be crossed with each other
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: NEAT AI
Chapter 2: Genetic Algorithm
Lecture 1: What is GA
Lecture 2: Understanding Neural Networks
Chapter 3: Code Implementation
Lecture 1: Config txt file for neural network
Lecture 2: Requirements
Lecture 3: Imgs file
Lecture 4: Class Bird
Lecture 5: Class Pipe
Lecture 6: Collision
Lecture 7: Class Base
Chapter 4: Output
Lecture 1: Conclusion
Instructors
-
Vijay A
Full stack developer
Rating Distribution
- 1 stars: 9 votes
- 2 stars: 11 votes
- 3 stars: 15 votes
- 4 stars: 12 votes
- 5 stars: 28 votes
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