{"id":20828866,"url":"https://github.com/timothewt/deeprl","last_synced_at":"2025-03-12T07:44:44.445Z","repository":{"id":198015852,"uuid":"699882980","full_name":"timothewt/DeepRL","owner":"timothewt","description":"Implementation of some Deep Reinforcement Learning algorithms and environments.","archived":false,"fork":false,"pushed_at":"2023-10-26T13:18:38.000Z","size":115,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-18T18:41:55.373Z","etag":null,"topics":["a2c","action-masking","deep-reinforcement-learning","dqn","gym","multiagent","multiagent-reinforcement-learning","pettingzoo","ppo","reinforcement-learning","torch"],"latest_commit_sha":null,"homepage":"","language":"Python","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/timothewt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-10-03T14:25:54.000Z","updated_at":"2024-09-22T06:18:35.000Z","dependencies_parsed_at":"2023-10-10T13:29:48.699Z","dependency_job_id":"16934591-fd5a-4436-ab8f-e7abae7a0b8b","html_url":"https://github.com/timothewt/DeepRL","commit_stats":{"total_commits":58,"total_committers":2,"mean_commits":29.0,"dds":0.4482758620689655,"last_synced_commit":"25eead6b4247722c1978c1314b5bff54c2f18c1a"},"previous_names":["timothewt/rl_algorithms","timothewt/deeprl"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timothewt%2FDeepRL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timothewt%2FDeepRL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timothewt%2FDeepRL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timothewt%2FDeepRL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/timothewt","download_url":"https://codeload.github.com/timothewt/DeepRL/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243179814,"owners_count":20249179,"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":["a2c","action-masking","deep-reinforcement-learning","dqn","gym","multiagent","multiagent-reinforcement-learning","pettingzoo","ppo","reinforcement-learning","torch"],"created_at":"2024-11-17T23:18:39.404Z","updated_at":"2025-03-12T07:44:44.419Z","avatar_url":"https://github.com/timothewt.png","language":"Python","readme":"# DeepRL\n\nImplementation of some Deep Reinforcement Learning algorithms and environments.\n\n## Description\n\nThe goal of this project is to have complete modularity with the algorithms and models used.\n\nThe implementations are completely made in PyTorch. \n\nThe environments used can either be single-agent using the Gymnasium\nAPI, or multi-agents using the PettingZoo Parallel API. Most algorithms also support action masking.\n\n## Getting Started\n\n### Technologies used\n\n* Python 3.11\n* PyTorch 2.1.0\n* Install all the requirements using `pip install -r requirements.txt`\n\n### Usage\n\n* Change the algorithm and the environment in the `main.py` file.\n\n### Algorithms\n\nThe following algorithms are currently available:\n* PPO (discrete [supports action masking] and continuous actions)\n* A2C (discrete [supports action masking] and continuous actions)\n* DQN (discrete actions)\n\n### Environments\n\nThe following environments have been implemented:\n* Snake\n* Minesweeper\n\nAny Gymnasium Env or PettingZoo ParallelEnv can be used.\n\n## Authors\n\n* Timothé Watteau (@timothewt)\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE.md file for details\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimothewt%2Fdeeprl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftimothewt%2Fdeeprl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimothewt%2Fdeeprl/lists"}