snake and snake-game
These are competitors: both are standalone AI implementations for Snake that use different reinforcement learning approaches (A uses unspecified AI methods while B specifically implements Q-Learning, DQN, and SARSA), and users would select one based on algorithm preference and code quality rather than using them together.
About snake
chynl/snake
Artificial intelligence for the Snake game.
This project develops and evaluates different AI strategies for the classic Snake game. It takes in game parameters and an AI algorithm, then outputs metrics like the snake's average length and number of steps taken. This is for game developers, AI researchers, or enthusiasts interested in comparing and understanding game-playing AI.
About snake-game
cfoh/snake-game
Playing snake game using machine learning (Q-Learning, DQN, SARSA)
This project helps undergraduate students and others new to AI understand reinforcement learning by training an AI to play the classic Snake game. It takes in game state information and outputs an AI agent capable of playing the game, demonstrating various learning algorithms like Q-learning and Deep Q-Networks. The primary user is anyone looking to learn or teach the fundamentals of AI and reinforcement learning through a practical example.
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