Donkey Kong AI Showdown: Genetic Algorithm vs Neural Evolution vs Proximal Policy Optimization

🔍The Genetic Algorithm uses evolution to optimize solutions by selecting the best traits from the population over generations.

🧠Neural Evolution of Augmented Topologies enhances neural networks by evolving their architectures to improve performance.

🎯Proximal Policy Optimization fine-tunes policies using a gradient ascent to maximize the expected rewards.

🔁The algorithm cycles through multiple generations, testing and mutating the solutions to improve performance.

🕹️The AI algorithms are tested in a Donkey Kong game, where their performance is evaluated based on factors like progression and score.

What is the goal of a Genetic Algorithm?

—The goal of a Genetic Algorithm is to optimize solutions by evolving the traits of the population over generations, selecting the best traits for improvement.

How does Neural Evolution of Augmented Topologies enhance neural networks?

—Neural Evolution of Augmented Topologies evolves the architectures of neural networks to improve their performance, allowing for more complex and effective solutions.

What is the purpose of Proximal Policy Optimization?

—Proximal Policy Optimization fine-tunes policies by using a gradient ascent method to maximize the expected rewards, optimizing the decision-making process in the AI algorithm.

How are the AI algorithms tested in the Donkey Kong game?

—The AI algorithms are tested in the Donkey Kong game by evaluating their performance based on factors like progression through the levels and score achieved.

How are the solutions in each generation of the algorithm improved?

—The solutions in each generation of the algorithm are improved through the process of selection, where the best-performing solutions are chosen, and mutation, where small changes are made to the solutions to introduce variation and potential improvement.

00:00Introduction: Testing three AI algorithms, Genetic Algorithm, Neural Evolution of Augmented Topologies, and Proximal Policy Optimization, in a Donkey Kong game.

02:58Explanation of the Genetic Algorithm and its use of evolution to optimize solutions over generations.

04:49Overview of Neural Evolution of Augmented Topologies and its approach to enhancing neural networks by evolving their architectures.

06:49Introduction to Proximal Policy Optimization and its fine-tuning of policies using gradient ascent.

07:59Description of the cycling process of the algorithms, where multiple generations are tested and mutated to improve performance.

09:59Testing of the AI algorithms in a Donkey Kong game, evaluating their performance based on progression and score.