This project implements an agent for playing the SonicTheHedgehog2 game from a ROM file using the Proximal Policy Optimization (PPO) algorithm from the stablebaselines3 library. The agent is trained to learn the optimal actions to take at each step in the game in order to complete the level and maximize the score.
game
reinforcement-learning
sonic-the-hedgehog
proximal-policy-optimization
rom-files
stable-baselines3
rewards-and-scoring
game-playing-agents
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Updated
Dec 12, 2022 - Jupyter Notebook