{"id":16966366,"url":"https://github.com/masterscrat/getting-started","last_synced_at":"2026-03-04T07:02:52.348Z","repository":{"id":71443879,"uuid":"235321621","full_name":"MasterScrat/getting-started","owner":"MasterScrat","description":"Introductory notebooks for the Flatland environment and challenge","archived":false,"fork":false,"pushed_at":"2020-03-14T04:45:37.000Z","size":1334,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-11T23:37:08.694Z","etag":null,"topics":["competitive-programming","machine-learning","operations-research","reinforcement-learning"],"latest_commit_sha":null,"homepage":"https://www.aicrowd.com/challenges/flatland-challenge","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MasterScrat.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2020-01-21T11:01:14.000Z","updated_at":"2020-10-25T06:54:21.000Z","dependencies_parsed_at":null,"dependency_job_id":"e85c96ed-6df1-425e-a471-d4983bbef823","html_url":"https://github.com/MasterScrat/getting-started","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/MasterScrat/getting-started","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MasterScrat%2Fgetting-started","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MasterScrat%2Fgetting-started/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MasterScrat%2Fgetting-started/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MasterScrat%2Fgetting-started/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MasterScrat","download_url":"https://codeload.github.com/MasterScrat/getting-started/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MasterScrat%2Fgetting-started/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30075425,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-04T05:31:57.858Z","status":"ssl_error","status_checked_at":"2026-03-04T05:31:38.462Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["competitive-programming","machine-learning","operations-research","reinforcement-learning"],"created_at":"2024-10-14T00:05:36.024Z","updated_at":"2026-03-04T07:02:52.330Z","avatar_url":"https://github.com/MasterScrat.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Getting Started with Flatland\n\n\u003e **Flatland** is an environment for developing and comparing multi-agent reinforcement learning algorithms in gridworlds.\n\nThis repository contains notebooks to get you started on the right track with the Flatland environment, in order to take part in the [AIcrowd Flatland Challenge](https://www.aicrowd.com/challenges/flatland-challenge).\n\nIf you want to dive into challenge baselines right away, [check out the various approaches below](#challenge-baselines). \n\nDiscovering Flatland\n---\n\n**Part 1: The Rail Environment**\n\n[![Open In Binder](https://mybinder.org/static/images/badge_logo.svg)](https://mybinder.org/v2/gh/MasterScrat/getting-started/master?filepath=notebook_1.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MasterScrat/getting-started/blob/master/notebook_1.ipynb)\n\n- Create a `RailEnv` environment and render it\n- Check out the default observations\n- \"Train\" a random agent\n\n![notebook1](assets/sparse_env.png)\n\n**Part 2: Observations**\n\n[![Open In Binder](https://mybinder.org/static/images/badge_logo.svg)](https://mybinder.org/v2/gh/MasterScrat/getting-started/master?filepath=notebook_2.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MasterScrat/getting-started/blob/master/notebook_2.ipynb)\n\n- Finding suitable observations\n- Creating your own observations\n- Visualizing observations\n\n![notebook2](assets/movie.gif)\n\n**Part 3: Level Generation**\n\n[![Open In Binder](https://mybinder.org/static/images/badge_logo.svg)](https://mybinder.org/v2/gh/MasterScrat/getting-started/master?filepath=notebook_1.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MasterScrat/getting-started/blob/master/notebook_3.ipynb)\n\n- Creating random rail networks\n- Creating schedules\n- Adjusting size and difficulty\n\n**Part 4: Malfunctions**\n\n[![Open In Binder](https://mybinder.org/static/images/badge_logo.svg)](https://mybinder.org/v2/gh/MasterScrat/getting-started/master?filepath=notebook_1.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MasterScrat/getting-started/blob/master/notebook_4.ipynb)\n\n- Introducing stochastic malfunctions\n- Handling malfunctions\n\n**Part 5: Speed Profiles**\n\n[![Open In Binder](https://mybinder.org/static/images/badge_logo.svg)](https://mybinder.org/v2/gh/MasterScrat/getting-started/master?filepath=notebook_1.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MasterScrat/getting-started/blob/master/notebook_5.ipynb)\n\n- Handling agent speed\n- Handling partial moves\n\n\nChallenge Baselines - Coming soon!\n---\n\nThe Flatland Challenge can be approached in different ways - for example using methods from **operations research**, **reinforcement learning**, or anything else!\n\nThe following notebooks show how to approach the problem using each of these methods.\n\n**Reinforcement Learning: DDQN**\n\n[![Open In Binder](https://mybinder.org/static/images/badge_logo.svg)](https://mybinder.org/v2/gh/MasterScrat/getting-started/master?filepath=notebook_1.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MasterScrat/getting-started/blob/master/notebook_5.ipynb)\n\nSolve simple rail problems using Double DQN.\n\n**Operations Research**\n\n[![Open In Binder](https://mybinder.org/static/images/badge_logo.svg)](https://mybinder.org/v2/gh/MasterScrat/getting-started/master?filepath=notebook_1.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MasterScrat/getting-started/blob/master/notebook_5.ipynb)\n\nSolve simple rail problems using OR methods.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmasterscrat%2Fgetting-started","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmasterscrat%2Fgetting-started","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmasterscrat%2Fgetting-started/lists"}