{"id":21739592,"url":"https://github.com/berkeleyautomation/rlqp","last_synced_at":"2025-08-13T05:06:08.732Z","repository":{"id":104388226,"uuid":"383861752","full_name":"BerkeleyAutomation/rlqp","owner":"BerkeleyAutomation","description":"Accelerating Quadratic Optimization with Reinforcement Learning","archived":false,"fork":false,"pushed_at":"2021-10-30T03:45:16.000Z","size":320,"stargazers_count":87,"open_issues_count":0,"forks_count":16,"subscribers_count":10,"default_branch":"master","last_synced_at":"2025-01-25T21:26:32.584Z","etag":null,"topics":["admm-algorithm","osqp","quadratic-programming","reinforcement-learning","td3"],"latest_commit_sha":null,"homepage":"https://BerkeleyAutomation.github.io/rlqp","language":null,"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/BerkeleyAutomation.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":"2021-07-07T16:31:15.000Z","updated_at":"2024-10-24T08:10:32.000Z","dependencies_parsed_at":"2023-03-14T00:45:40.974Z","dependency_job_id":null,"html_url":"https://github.com/BerkeleyAutomation/rlqp","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BerkeleyAutomation%2Frlqp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BerkeleyAutomation%2Frlqp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BerkeleyAutomation%2Frlqp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BerkeleyAutomation%2Frlqp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BerkeleyAutomation","download_url":"https://codeload.github.com/BerkeleyAutomation/rlqp/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244717336,"owners_count":20498283,"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":["admm-algorithm","osqp","quadratic-programming","reinforcement-learning","td3"],"created_at":"2024-11-26T06:09:33.747Z","updated_at":"2025-03-21T00:45:08.283Z","avatar_url":"https://github.com/BerkeleyAutomation.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# RLQP: Accelerating Quadratic Optimization with RL\n\n\u003cimg src=\"https://raw.githubusercontent.com/BerkeleyAutomation/rlqp/gh_pages/assets/rlqp-img/conceptual_figure.png?token=AADOZWQXPT5BO5YXHRI7GULA5XWUG\"\u003e\n\nWe demonstrate reinforcement learning can significantly accelerate first-order optimization, outperforming state-of-the-art solvers by up to 3x. RLQP avoids suboptimal heuristics within solvers by tuning the internal parameters of the ADMM algorithm. By decomposing the policy as a multi-agent partially observed problem, RLQP adapts to unseen problem classes and to larger problems than seen during training.\n\n## Getting Started\nRLQP is composed of a few submodules, namely to (a) train the RL policy on a specific class of problems (source in `rlqp_train/`) and (b) evaluate the policy on a test problem. Most users will want to start by using RLQP's policy to accelerate optimization of their problems.\n\n### Prerequisites\n\n### Installation (evaluation)\nTo install the Python package to *evaluate* a pre-trained policy, run:\n```\npip install git+https://github.com/berkeleyautomation/rlqp-python.git@55f378e496979bd00e84cea4583ac37bfaa571a9\n```\n\nThis package contains a pre-trained model which should improve convergence beyond OSQP. The interface is identical to OSQP.\n\n### Installation (training)\nPlease follow the instructions in the `rlqp_train/` directory to setup and run training code. This code is still in *preview* mode as we work to release features like our TD3 policy.\n\n## Citation\n```\n@article{ichnowski2021rlqp,\n  title={Accelerating Quadratic Optimization with Reinforcement Learning},\n  author={Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato,\n    and Goran Banjac, Michael Luo, Francesco Borrelli\n    and Joseph E. Gonzalez, Ion Stoica, Ken Goldberg},\n  year={2021},\n  journal={arXiv preprint}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fberkeleyautomation%2Frlqp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fberkeleyautomation%2Frlqp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fberkeleyautomation%2Frlqp/lists"}