DRL4SnakeGame and DQN-SnakeAI
These two tools are competitors, as both implement a Deep Q-Network (DQN) based deep reinforcement learning agent to play the Snake game.
About DRL4SnakeGame
ZYunfeii/DRL4SnakeGame
Using deep reinforcement learning to play Snake game(贪吃蛇).
Implements PPO (Proximal Policy Optimization) for discrete action spaces with a custom PyTorch neural network architecture, achieving convergence in approximately 30 minutes of training. The project includes a pygame-based Snake environment simulator, reward visualization via matplotlib/seaborn, and modular separation between the RL agent, network architecture, and game environment for easy extension or adaptation to other discrete control problems.
About DQN-SnakeAI
GoldenApplePie404/DQN-SnakeAI
基于DQN打造的贪吃蛇游戏智能体
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