DRL4SnakeGame and snake-game
These are competitors offering alternative implementations of reinforcement learning approaches to the Snake game, with A focusing on deep reinforcement learning (DRL) while B provides multiple baseline algorithms (Q-Learning, DQN, SARSA) for comparative study.
About DRL4SnakeGame
ZYunfeii/DRL4SnakeGame
Using deep reinforcement learning to play Snake game(贪吃蛇).
This project offers a way to train an AI to play the classic Snake game. It takes the game environment as input and outputs a trained AI agent that can play the game intelligently. This is for anyone interested in observing or demonstrating how artificial intelligence can master simple game mechanics.
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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