svpino/cs7641-assignment4
CS7641 - Machine Learning - Assignment 4 - Markov Decision Processes
This project helps machine learning students explore and analyze how different Markov Decision Process (MDP) algorithms learn optimal behavior in grid-world scenarios. Students input grid maps with start points, goals, walls, and hazards, along with custom reward values for these elements. The output includes performance metrics like average reward, number of steps, and time taken, which can be used to generate charts for assignment reports. It's designed for students taking a Machine Learning course, specifically those studying MDPs.
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Use this if you need to run experiments and analyze the performance of Value Iteration, Policy Iteration, and Q-Learning algorithms on grid-based problems for a Machine Learning assignment.
Not ideal if you are looking for a general-purpose reinforcement learning library or a tool for real-world, complex decision-making problems beyond educational grid-world simulations.
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Language
Java
License
MIT
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Last pushed
Sep 22, 2025
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