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Lunar Lander Deep Q-Learning 🚀

Welcome to the Lunar Lander Deep Q-Learning project, where we leverage the power of Deep Q-Learning and the Stable Baselines library to train an agent capable of landing a spacecraft on the moon! 🌕🚀

Overview 📖

In this project, we tackle the challenging Lunar Lander environment using reinforcement learning techniques. Our agent learns to navigate the lunar landscape, adjusting thrusters to achieve a smooth and safe landing. We employ Deep Q-Learning, a powerful algorithm that combines deep neural networks with Q-learning, to enable our agent to make intelligent decisions in a complex and dynamic environment.

Features 🌟

  • Deep Q-Learning: Utilize state-of-the-art reinforcement learning techniques to train the agent.
  • Stable Baselines Library: Leverage the Stable Baselines library, a set of high-quality implementations of reinforcement learning algorithms.
  • Customizable Environment: Easily adapt the project to other environments or tweak hyperparameters to suit your needs.
  • Visualization: Observe the training progress and the agent's performance through visualizations and statistics.

Embark on the lunar exploration journey with our Deep Q-Learning agent! If you have any questions or feedback, don't hesitate to reach out. Happy landing! 🌌👨‍🚀

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