{"id":16649003,"url":"https://github.com/jalajthanaki/q_learning_for_simple_atari_game","last_synced_at":"2025-05-12T13:07:13.665Z","repository":{"id":81621610,"uuid":"122447568","full_name":"jalajthanaki/Q_learning_for_simple_atari_game","owner":"jalajthanaki","description":"This repository contains the code which can help us to understand how q-learning algorithm can be applied to build simple video game bot.","archived":false,"fork":false,"pushed_at":"2018-02-23T09:53:24.000Z","size":6,"stargazers_count":3,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-31T22:21:52.680Z","etag":null,"topics":["atari","qlearning-algorithm","reinforcement-learning-algorithms"],"latest_commit_sha":null,"homepage":null,"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/jalajthanaki.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":"2018-02-22T07:55:59.000Z","updated_at":"2019-08-21T06:30:52.000Z","dependencies_parsed_at":"2023-03-13T20:08:02.060Z","dependency_job_id":null,"html_url":"https://github.com/jalajthanaki/Q_learning_for_simple_atari_game","commit_stats":{"total_commits":5,"total_committers":1,"mean_commits":5.0,"dds":0.0,"last_synced_commit":"7676b579264b9ea015d4ca86a17df1daa1966e7d"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jalajthanaki%2FQ_learning_for_simple_atari_game","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jalajthanaki%2FQ_learning_for_simple_atari_game/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jalajthanaki%2FQ_learning_for_simple_atari_game/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jalajthanaki%2FQ_learning_for_simple_atari_game/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jalajthanaki","download_url":"https://codeload.github.com/jalajthanaki/Q_learning_for_simple_atari_game/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253745152,"owners_count":21957317,"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":["atari","qlearning-algorithm","reinforcement-learning-algorithms"],"created_at":"2024-10-12T09:06:18.675Z","updated_at":"2025-05-12T13:07:13.628Z","avatar_url":"https://github.com/jalajthanaki.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Q learning for simple atari game\n\nThis is a simple example of a type of [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning)\ncalled [Q learning](https://en.wikipedia.org/wiki/Q-learning). \n\n## Overview\n\nWe are building simple game and using Q-learning algorithm we built the bot which can able to will this simple game\n\n\t● Rules: The agent (yellow box) has to reach one of the goals to end the game \n\t         (green or red cell).\n\t\n\t● Rewards: Each step gives a negative reward of -0.04. \n\t           The red cell gives a negative reward of -1. \n\t           The green one gives a positive reward of +1.\n\t           \n\t● States: Each cell is a state the agent can be.\n\t● Actions: There are only 4 actions. Up, Down, Right, Left.\n\n## Dependencies\n\n- Python 2.7\n- tkinter\n\n## Installation\n- To install tkinter You need to execute this command: ```$ sudo apt-get install python-tk```\n\n## Usage\n\nRun `python Learner.py` in terminal to see the the bot in action. It'll find the optimal strategy pretty fast (like in 15 seconds)\n\n## Credits\n\nThe credits for this code go to [PhillipeMorere](https://github.com/PhilippeMorere) and [joongwha](https://github.com/joongwha). I've merely created a wrapper to get people started.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjalajthanaki%2Fq_learning_for_simple_atari_game","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjalajthanaki%2Fq_learning_for_simple_atari_game","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjalajthanaki%2Fq_learning_for_simple_atari_game/lists"}