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Advanced RL algorithms for two simplified versions of chess. Shortest Path finds the minimal moves between two cells based on piece capabilities. Capture Pieces trains against random opponents aiming for maximal captures in set moves. Features Deep Q-Learning, Policy Iteration, TD and more.

License: MIT License

Python 30.68% Jupyter Notebook 68.72% Shell 0.59%
deep-q-learning expected-sarsa q-learning reinforcement-learning sarsa sarsa-lambda

reinforcemate's Introduction

Hi there ๐Ÿ‘‹

I'm Valeria

Newly graduate student of master degree course "Data Science and Artificial Intelligence" @ DSSC - Units

  • ๐ŸŽ“ BSc in Physics @ University of Milan
  • ๐Ÿ“š MSc in Data Science and Scientific Computing - AI
  • ๐Ÿ“ˆ Interested in LLMs and MARL models.
  • ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป Passion for programming
  • โšก Fun fact: I am a former amateur theater actress

๐Ÿ”ฅ Most proud project:

  • GovTaxOptimal_RL - thesis project RL framework designed to simulate and optimize government taxation policies. Focused on balancing economic equality and consumption, it employs state-of-the-art RL algorithms, PPO and SAC, to navigate wealth taxation strategies across various economic scenarios, aiming to achieve optimal wealth distribution and economic efficiency
  • GPTGram - Telegram chatbot API using decoder-only transformer LLM
  • AutoMLP-GA - Automation of topology-optimization for Multi-Layer Perceptrons using Genetic Algorithms for classification tasks

๐Ÿ” Projects apart from pinned:

๐Ÿ“ซ Contact: [email protected]

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