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

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

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

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

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

• 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