A.I Learns Snake And Wins - Part 1

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I wrote a Genetic Algorithm to play the game snake.
The problem:

Developing an AI to beat the game Snake is a classic problem in the field of artificial intelligence and has been the subject of many research projects and competitions. There are several approaches you can take to create an AI to beat the game Snake. Here are a few:

Reinforcement Learning: You can train an AI using reinforcement learning to beat the game Snake. In this approach, the AI learns by playing the game repeatedly and adjusting its actions based on the feedback it receives from the game. You can use algorithms like Q-learning or Deep Q-Networks (DQNs) to train the AI.

Genetic Algorithms: You can also use genetic algorithms to create an AI that can beat the game Snake. In this approach, you generate a population of snakes with different behaviors and then evolve them over generations to create a population of snakes that are better at the game.

Rule-based approach: Another approach is to develop a set of rules that the AI follows to play the game. For example, the AI could be programmed to avoid walls and obstacles, to chase after food, and to stay away from its own tail.

Regardless of the approach, creating an AI to beat the game Snake requires a good understanding of the game mechanics and an ability to balance exploration and exploitation during the learning process.

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Machine Learning, Neural Network, Genetic Algorithm, AI, artificial intelligence, Self Play, reinforcement learning, ML