{"id":23437264,"url":"https://github.com/pvnieo/beating-atari","last_synced_at":"2026-05-07T08:36:43.048Z","repository":{"id":255012333,"uuid":"165383839","full_name":"pvnieo/beating-atari","owner":"pvnieo","description":"Implementation of RL algorithms to beat Atari 2600 games","archived":false,"fork":false,"pushed_at":"2019-09-08T22:19:11.000Z","size":583,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-15T11:23:20.199Z","etag":null,"topics":["atari","double-dqn","dqn","pytorch","reinforcement-learning","rl-algorithms"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pvnieo.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":"2019-01-12T12:09:26.000Z","updated_at":"2024-06-19T02:47:58.000Z","dependencies_parsed_at":"2024-08-27T14:16:55.868Z","dependency_job_id":"36a818c0-9f08-41a4-a41d-1eb14d2cab17","html_url":"https://github.com/pvnieo/beating-atari","commit_stats":null,"previous_names":["pvnieo/beating-atari"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pvnieo%2Fbeating-atari","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pvnieo%2Fbeating-atari/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pvnieo%2Fbeating-atari/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pvnieo%2Fbeating-atari/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pvnieo","download_url":"https://codeload.github.com/pvnieo/beating-atari/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248089807,"owners_count":21045969,"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","double-dqn","dqn","pytorch","reinforcement-learning","rl-algorithms"],"created_at":"2024-12-23T13:44:37.062Z","updated_at":"2026-05-07T08:36:42.981Z","avatar_url":"https://github.com/pvnieo.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# beating-atari\nModularized implementation of popular deep RL algorithms in PyTorch to beat Atari 2600 games. Easy switch between algorithms and challenging games.\n\nImplemented algorithms:\n - [x] Nips DQN [[1]](#references)\n - [x] Nature DQN [[2]](#references)\n - [x] Double DQN [[3]](#references)\n - [ ] Prioritised Experience Replay [[4]](#references)\n - [ ] Dueling Network Architecture [[5]](#references)\n\n## Requirement\n - gym\n - PyTorch\n - OpenCV\n - tensorboard\n\nThis project runs on python \u003e= 3.6, use pip to install dependencies:\n```\npip3 install -r requirements.txt\n```\n\n### Project report\nSee project report [here](https://www.researchgate.net/publication/335392857_The_genesis_of_beating_Atari_games).\n\nReferences\n----------\n\n[1] [Playing Atari with Deep Reinforcement Learning](http://arxiv.org/abs/1312.5602)  \n[2] [Human-level control through deep reinforcement learning](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf)  \n[3] [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461)  \n[4] [Prioritized Experience Replay](http://arxiv.org/abs/1511.05952)  \n[5] [Dueling Network Architectures for Deep Reinforcement Learning](http://arxiv.org/abs/1511.06581)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpvnieo%2Fbeating-atari","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpvnieo%2Fbeating-atari","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpvnieo%2Fbeating-atari/lists"}