{"id":19531913,"url":"https://github.com/gautams3/irl_intelligentgrading","last_synced_at":"2025-10-28T17:41:41.983Z","repository":{"id":72816377,"uuid":"131092657","full_name":"gautams3/IRL_IntelligentGrading","owner":"gautams3","description":"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","archived":false,"fork":false,"pushed_at":"2018-04-28T05:33:55.000Z","size":2179,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-08T17:05:27.526Z","etag":null,"topics":["educational-technology","inverse-reinforcement-learning","machine-learning"],"latest_commit_sha":null,"homepage":"https://www.gautamsalhotra.com/2018/04/inverse-reinforcement-learning-for.html","language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gautams3.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-04-26T03:04:25.000Z","updated_at":"2018-04-28T05:37:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"155bcdcc-76c0-4046-a032-59a33e48be52","html_url":"https://github.com/gautams3/IRL_IntelligentGrading","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gautams3%2FIRL_IntelligentGrading","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gautams3%2FIRL_IntelligentGrading/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gautams3%2FIRL_IntelligentGrading/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gautams3%2FIRL_IntelligentGrading/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gautams3","download_url":"https://codeload.github.com/gautams3/IRL_IntelligentGrading/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240784057,"owners_count":19856941,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["educational-technology","inverse-reinforcement-learning","machine-learning"],"created_at":"2024-11-11T01:45:02.130Z","updated_at":"2025-10-28T17:41:36.926Z","avatar_url":"https://github.com/gautams3.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Maven Central](https://maven-badges.herokuapp.com/maven-central/edu.brown.cs.burlap/burlap/badge.svg)](https://maven-badges.herokuapp.com/maven-central/edu.brown.cs.burlap/burlap) [![Hex.pm](https://img.shields.io/hexpm/l/plug.svg?maxAge=2592000)]() ![java6](https://img.shields.io/badge/java-6-blue.svg) ![java7](https://img.shields.io/badge/java-7-blue.svg) ![java8](https://img.shields.io/badge/java-8-blue.svg)\n\nIntelligent Grading with Inverse Reinforcement Learning \n======\n\nThis 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.\n\n## How to run\nThis project is built using Maven; I will outline steps to use it with [IntelliJ](https://www.jetbrains.com/idea/), a free Java IDE.\n\n 1. Clone the project to a local directory (`git clone https://github.com/gautams3/IRL_IntelligentGrading.git` ) \n 3. Import the project as a Maven project in IntelliJ (Import Project -\u003e go to root project folder -\u003e double click pom.xml. You may have to wait a while for the dependencies to download and the indexing)\n 4. Open file `src/main/java/Tutorial/IRLParkingLotExample.java`\n 5. Run that class (`main()` function. You may have to add a Configuration that runs the IRLParkingLotExample class)\n\nThere 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\n1. Explore and record: This lets you navigate the parking lot world, and record episodes\n2. Playback: Playback recorded episodes\n3. RunIRL: Run the IRL algorithm to learn the reward function based on the given expert demonstrations (transitions are considered deterministic)\n4. TestUser: Test the user trials based on the learned reward function from step 3.\n\nIn 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgautams3%2Firl_intelligentgrading","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgautams3%2Firl_intelligentgrading","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgautams3%2Firl_intelligentgrading/lists"}