https://github.com/gautams3/irl_intelligentgrading

Using Inverse Reinforcement Learning for grading of physical (sensorimotor) skills. This framework is a proof-of-concept with a toy problem of navigating in grid-based parking lot
https://github.com/gautams3/irl_intelligentgrading

educational-technology inverse-reinforcement-learning machine-learning

Last synced: 8 months ago
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Using Inverse Reinforcement Learning for grading of physical (sensorimotor) skills. This framework is a proof-of-concept with a toy problem of navigating in grid-based parking lot

• Host: GitHub
• URL: https://github.com/gautams3/irl_intelligentgrading
• Owner: gautams3
• License: apache-2.0
• Created: 2018-04-26T03:04:25.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-04-28T05:33:55.000Z (about 8 years ago)
• Last Synced: 2025-01-08T17:05:27.526Z (over 1 year ago)
• Topics: educational-technology, inverse-reinforcement-learning, machine-learning
• Language: Java
• Homepage: https://www.gautamsalhotra.com/2018/04/inverse-reinforcement-learning-for.html
• Size: 2.08 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
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Intelligent Grading with Inverse Reinforcement Learning 

======

This is a project on using Inverse Reinforcement Learning (IRL) for automated grading of physical (sensorimotor) skills. It also includes a snapshot of the [BURLAP](http://burlap.cs.brown.edu/) codebase, since I had to make a few changes in BURLAP to create my IRL framework.

## How to run

This project is built using Maven; I will outline steps to use it with [IntelliJ](https://www.jetbrains.com/idea/), a free Java IDE.

 1. Clone the project to a local directory (`git clone https://github.com/gautams3/IRL_IntelligentGrading.git` ) 

 3. Import the project as a Maven project in IntelliJ (Import Project -> go to root project folder -> double click pom.xml. You may have to wait a while for the dependencies to download and the indexing)

 4. Open file `src/main/java/Tutorial/IRLParkingLotExample.java`

 5. Run that class (`main()` function. You may have to add a Configuration that runs the IRLParkingLotExample class)

There are 4 modes to run in sequential order, explained thoroughly in the paper. The main() function in IRLParkingLotExample let's you choose these modes, using the `GridWorldRunOptions` enum

1. Explore and record: This lets you navigate the parking lot world, and record episodes

2. Playback: Playback recorded episodes

3. RunIRL: Run the IRL algorithm to learn the reward function based on the given expert demonstrations (transitions are considered deterministic)

4. TestUser: Test the user trials based on the learned reward function from step 3.

In order to help you skip to the mode you wish to run, I have added sample output files for expert demonstrations, user trials, and IRL reward function output.