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stable-baselines3

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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.

  • Updated Dec 12, 2022
  • Jupyter Notebook

In this project, I created an agent using the PPO algorithm from stable baselines3 to complete a task in the LunarLander environment. The agent was trained using reinforcement learning techniques to maximize its performance in the task. The resulting model was able to achieve a high level of success in the LunarLander environment.

  • Updated Nov 24, 2022
  • Jupyter Notebook

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