{"id":19301026,"url":"https://github.com/Hamid-Rezaei/Berkeley-AI-Projects","last_synced_at":"2025-04-22T10:32:28.469Z","repository":{"id":152370492,"uuid":"625872708","full_name":"Hamid-Rezaei/Berkeley-AI-Projects","owner":"Hamid-Rezaei","description":"Fundamental of AI course which focuses on search, multiagents, mdp and reinforcement learning algorithms.","archived":false,"fork":false,"pushed_at":"2023-12-04T15:08:31.000Z","size":2325,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-09T23:16:30.522Z","etag":null,"topics":["adversial-search","mdp","python","reinforcement-learning","searching-algorithms"],"latest_commit_sha":null,"homepage":"https://inst.eecs.berkeley.edu/~cs188/fa23/","language":"Python","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/Hamid-Rezaei.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}},"created_at":"2023-04-10T09:41:46.000Z","updated_at":"2024-10-03T09:45:13.000Z","dependencies_parsed_at":"2023-11-12T20:40:37.102Z","dependency_job_id":null,"html_url":"https://github.com/Hamid-Rezaei/Berkeley-AI-Projects","commit_stats":null,"previous_names":["hamid-rezaei/berkeleyaiprojects"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamid-Rezaei%2FBerkeley-AI-Projects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamid-Rezaei%2FBerkeley-AI-Projects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamid-Rezaei%2FBerkeley-AI-Projects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hamid-Rezaei%2FBerkeley-AI-Projects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hamid-Rezaei","download_url":"https://codeload.github.com/Hamid-Rezaei/Berkeley-AI-Projects/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223892982,"owners_count":17220834,"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":["adversial-search","mdp","python","reinforcement-learning","searching-algorithms"],"created_at":"2024-11-09T23:16:34.632Z","updated_at":"2024-11-09T23:16:37.023Z","avatar_url":"https://github.com/Hamid-Rezaei.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI Project Spring 2023: The Pac-Man Projects\n\n## Overview\nThe [Pac-Man Projects, developed at UC Berkeley](http://ai.berkeley.edu), aims to advance the field of artificial intelligence through the development and evaluation of intelligent agents in the context of the Pacman game. It serves as a playground for exploring different AI algorithms, including search algorithms, adversarial search, reinforcement learning, and probabilistic inference.\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://github.com/Hamid-Rezaei/BerkeleyAIProjects/blob/master/overview.gif\" width=\"540\" /\u003e\n\u003c/p\u003e\n\n## Each Project Aim\n\n- ### 1-Search\n   Implement and evaluate search algorithms to enable Pacman agents to navigate the game maze effectively. This includes depth-first search, breadth-first search, uniform-cost search, and A* search.\n- ### 2-MultiAgent\n   Develop intelligent agents that can compete against ghosts using adversarial search algorithms. Minimax and alpha-beta pruning techniques allow agents to make optimal decisions in a competitive environment.\n- ### 3-ReinforcementLearning\n   Employ Markov decision processes and reinforcement learning techniques, such as Value Iteration, Q-learning, and Approximate Q-learning, to enable Pacman agents to learn and improve their performance over time. Agents can adapt and make optimal decisions by maximizing cumulative rewards.\n- ### 4-GhustBusters\n   Utilize probabilistic models, such as Bayesian networks and Hidden Markov Models, to reason under uncertainty and make informed decisions in complex situations.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FHamid-Rezaei%2FBerkeley-AI-Projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FHamid-Rezaei%2FBerkeley-AI-Projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FHamid-Rezaei%2FBerkeley-AI-Projects/lists"}