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).
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 snake-game
cfoh/snake-game
Playing snake game using machine learning (Q-Learning, DQN, SARSA)
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