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This is my attempt at implementing the paper "Playing Atari with Deep Reinforcement Learning" By Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra and Martin Riedmiller. This is my first attempt at both reading and implementing a research paper.

Jupyter Notebook 53.05% Python 46.90% Shell 0.05%
deep-q-network deep-reinforcement-learning q-learning reinforcement-learning

deep-q-learning's Introduction

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  • 🔭 I’m currently working on HiPPO-Jax

  • 🌱 My research and interests are centered around:

    • Attention-free sequence modeling methods
    • Enhancing and further addressing long-context dependency in language and video processing.
    • Optimizing these models utilizing Jax/Flax and CUDA frameworks.

  • 👯 I’m looking to collaborate on research related to topics mentioned above

  • 🤝 I’m looking for help with HiPPO-Jax

  • 📝 I regularly write articles on my website

  • 💬 Ask me about ramen noodles, chalk, cafe racers, my current investment strategy, and soccer

  • 📫 How to reach me [email protected]

  • 📄 Know about my experiences from my resume

Connect with me:

beegassy 16429441 https://www.instagram.com/beegassy/ bryank123 beegassy

Languages and Tools:

python c cplusplus bash linux docker git

Machine Learning Tools:

pytorch jax flax tensorflow scikit_learn pandas numpy seaborn

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