{"id":24322749,"url":"https://github.com/oiricaud/markov-decision-process-ai","last_synced_at":"2026-04-22T19:35:13.856Z","repository":{"id":107223281,"uuid":"89029584","full_name":"oiricaud/Markov-Decision-Process-AI","owner":"oiricaud","description":"Implement decision process for Monte-Carlo, Value Iteration \u0026 Q-Learning","archived":false,"fork":false,"pushed_at":"2020-10-13T01:14:01.000Z","size":164,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-11T02:37:52.272Z","etag":null,"topics":["ai","decision","markov","process"],"latest_commit_sha":null,"homepage":"","language":"Java","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/oiricaud.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":"2017-04-21T22:52:00.000Z","updated_at":"2020-10-13T01:14:04.000Z","dependencies_parsed_at":null,"dependency_job_id":"581b0e8f-8cd2-49af-83c0-92e8db8a9524","html_url":"https://github.com/oiricaud/Markov-Decision-Process-AI","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/oiricaud/Markov-Decision-Process-AI","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oiricaud%2FMarkov-Decision-Process-AI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oiricaud%2FMarkov-Decision-Process-AI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oiricaud%2FMarkov-Decision-Process-AI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oiricaud%2FMarkov-Decision-Process-AI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/oiricaud","download_url":"https://codeload.github.com/oiricaud/Markov-Decision-Process-AI/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/oiricaud%2FMarkov-Decision-Process-AI/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32152601,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-22T17:06:48.269Z","status":"ssl_error","status_checked_at":"2026-04-22T17:06:19.037Z","response_time":58,"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":["ai","decision","markov","process"],"created_at":"2025-01-17T17:58:27.301Z","updated_at":"2026-04-22T19:35:13.838Z","avatar_url":"https://github.com/oiricaud.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Markov-Decision-Process: Artificial Intelligence \n\nObjective\n======\nTo expirement with some of the basic algorithms for solving MDPs on a simple domain.\n\nGroups: You may optionally work in groups of 2 students.\n\nDoomain: The domain is based on a simple MDP originally designed by Rich Sutton at the University of Alberta. The example describes a Markov Decision Porcess that models the life of a student and the decisions one must make to both have a good time and remain in good academic standing.\n\nStates\n======\n    R = Rested \n    T = Tired\n    D = Homework Done\n    U = Homework Undone\n    8p = eight o'clock pm\n\nActions\n======\n    P = Party\n    R = Rest\n    S = Study\nany means any action has the same effect\n\n\n\n*note: not all actions are possible in all states*\n\n\nRed numbers are rewards \n\n\nGreen numbers are transition probabilities (all those not labeled are probability 1.0)\n\n\nThe gray rectangle denotes a terminal state.\n\nSee below for the diagram of the MDP.\n\n![alt tag](Screenshots/Diagram.png \"Diagram\") \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foiricaud%2Fmarkov-decision-process-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Foiricaud%2Fmarkov-decision-process-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foiricaud%2Fmarkov-decision-process-ai/lists"}