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q-learning

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This repository explores the application of three reinforcement learning algorithms—Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Proximal Policy Optimization (PPO)—for playing Super Mario Bros using the OpenAI Gym and nes-py emulator. It includes a comparative analysis of these models.

  • Updated Jun 20, 2024
  • Jupyter Notebook

This repository compares two methodologies for music recommendation: Q-learning and Deep Reinforcement Learning (Dueling DQN), applied to a dataset of music tracks with features like genre, artist, and danceability. The goal is to build a system that recommends music based on user preferences.

  • Updated Jun 18, 2024
  • Jupyter Notebook

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