{"id":26802165,"url":"https://github.com/mileristovski/ai-reinforcementlearning","last_synced_at":"2026-04-28T08:35:54.186Z","repository":{"id":271468208,"uuid":"869542447","full_name":"Mileristovski/AI-ReinforcementLearning","owner":"Mileristovski","description":"Un projet d'apprentissage par renforcement testant divers algorithmes RL, notamment la Programmation Dynamique, Monte Carlo et l'Apprentissage par Différence Temporelle, sur plusieurs environnements comme Grid World, Monty Hall et Pierre-Papier-Ciseaux. 🚀","archived":false,"fork":false,"pushed_at":"2025-03-14T13:19:17.000Z","size":1450,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-14T14:24:07.841Z","etag":null,"topics":["artifical-intelligense","dynamic-programming","monte-c","reinforcement-learning","rust","temporal-differencing-learning"],"latest_commit_sha":null,"homepage":"","language":"Rust","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Mileristovski.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":"2024-10-08T13:23:51.000Z","updated_at":"2025-03-14T13:23:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"fdcf68b4-c963-46dd-90fe-ae4fc93719d5","html_url":"https://github.com/Mileristovski/AI-ReinforcementLearning","commit_stats":null,"previous_names":["mileristovski/reinforcementlearning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FAI-ReinforcementLearning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FAI-ReinforcementLearning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FAI-ReinforcementLearning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FAI-ReinforcementLearning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mileristovski","download_url":"https://codeload.github.com/Mileristovski/AI-ReinforcementLearning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246243547,"owners_count":20746312,"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":["artifical-intelligense","dynamic-programming","monte-c","reinforcement-learning","rust","temporal-differencing-learning"],"created_at":"2025-03-29T21:16:28.877Z","updated_at":"2026-04-28T08:35:53.623Z","avatar_url":"https://github.com/Mileristovski.png","language":"Rust","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Reinforcement Learning Algorithms\n\n## Aperçu\n\nCe projet explore et teste divers **algorithmes d'Apprentissage par Renforcement (RL)** à travers plusieurs environnements. L'objectif est d'évaluer leurs performances et de comprendre leur comportement dans différents contextes.\n\n## Fonctionnalités\n\n- **Algorithmes RL implémentés** :\n  - Programmation Dynamique\n  - Méthodes de Monte Carlo\n  - Apprentissage par Différence Temporelle\n  - Algorithmes de Planification\n  \n- **Environnements testés** :\n  - Monde en Grille (Grid World)\n  - Monde en Ligne (Line World)\n  - Problème de Monty Hall (3 portes)\n  - Problème de Monty Hall (5 portes)\n  - Pierre-Papier-Ciseaux\n  - Environnement Secret 🚀\n\n## Installation\n\n1. **Cloner le dépôt** :\n   ```bash\n   git clone https://github.com/Mileristovski/reinforcementLearning.git\n   ```\n2. **Se déplacer dans le répertoire du projet** :\n   ```bash\n   cd reinforcementLearning/src/bin/back\n   ```\n3. **Installer les dépendances** :\n   ```bash\n   cargo build --release\n   ```\n\n## Utilisation\n\n1. **Exécuter un algorithme RL sur un environnement donné** :\n   ```bash\n   cargo run --release\n   ```\n2. **Suivre la progression de l'entraînement** via les journaux ou les visualisations.\n\n## Environnements Testés\n- Monde en Grille (Grid World)\n- Monde en Ligne (Line World)\n- Problème de Monty Hall (3 portes)\n- Problème de Monty Hall (5 portes)\n- Pierre-Papier-Ciseaux\n- Environnement Secret\n\n## Algorithmes RL Implémentés\n- Programmation Dynamique\n- Méthodes de Monte Carlo\n- Apprentissage par Différence Temporelle\n- Algorithmes de Planification\n\n## Contribution\n\nLes contributions sont les bienvenues ! N'hésitez pas à forker le dépôt et à soumettre une pull request.\n\n## Licence\n\nCe projet est sous licence MIT. Consultez le fichier [LICENSE](LICENSE) pour plus de détails.\n\n---\n\n🚀 Bon apprentissage par renforcement !\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmileristovski%2Fai-reinforcementlearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmileristovski%2Fai-reinforcementlearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmileristovski%2Fai-reinforcementlearning/lists"}