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Markov Decision Processes and Expectation Maximization Algorithm Project 📚

📖 Table of Contents

  • About the Project
  • Getting Started
  • Usage
  • Contributing
  • References

🧐 About

Markov Decision Processes (MDPs) are mathematical frameworks for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning.

This project discusses:

  • What are MDPs
  • Solving MDPs using Value Iteration algorithm
  • Example of MDP - Recycling Robot
  • Expectation Maximization (EM) algorithm
  • Implementing EM algorithm using reaction networks

🏁 Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes:

Prerequisites

  • Python 3.x
  • Knowledge of Markov Decision Processes and Reinforcement Learning
  • Basic understanding of Expectation Maximization algorithm

🎈 Usage

To use this project, follow the steps below:

  • Run main.py to learn about MDPs and solve using Value Iteration algorithm
  • Check recycling_robot.py for an example MDP - Recycling Robot
  • Refer project report to understand EM algorithm

🤝 Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  • Fork the Project
  • Create your Feature Branch
  • Commit your Changes
  • Push to the Branch
  • Open a Pull Request

📖 References

  • Richard S. Sutton and Andrew G. Barto, Reinforcement Learning:An Introduction.
  • Abhinav, Masters Thesis, Molecular Algorithms and Schemes for their Implementation using DNA.
  • LIHONG LI, A Unifying Framework For Computational Reinforcement Learning Theory.
  • Muppirala Viswa Virinchi, Abhishek Behera, Manoj GopalKrishnan, A reaction network scheme which implements the EM algorithm