{"id":20773209,"url":"https://github.com/panos108/gp-rto-ei","last_synced_at":"2025-10-20T12:05:13.668Z","repository":{"id":144127140,"uuid":"273515134","full_name":"panos108/GP-RTO-EI","owner":"panos108","description":"Real-time optimization using exploration strategies","archived":false,"fork":false,"pushed_at":"2021-02-19T16:45:20.000Z","size":81196,"stargazers_count":5,"open_issues_count":1,"forks_count":4,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-30T17:11:15.273Z","etag":null,"topics":["bayesian-optimization","dynamic-optimization","gaussian-processes","real-time-operating-systems"],"latest_commit_sha":null,"homepage":"","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/panos108.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":"2020-06-19T14:35:56.000Z","updated_at":"2025-01-09T04:25:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"93bd4209-bc3c-4ddf-bb38-4fdd23398a47","html_url":"https://github.com/panos108/GP-RTO-EI","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/panos108%2FGP-RTO-EI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/panos108%2FGP-RTO-EI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/panos108%2FGP-RTO-EI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/panos108%2FGP-RTO-EI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/panos108","download_url":"https://codeload.github.com/panos108/GP-RTO-EI/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251727825,"owners_count":21634092,"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":["bayesian-optimization","dynamic-optimization","gaussian-processes","real-time-operating-systems"],"created_at":"2024-11-17T12:24:54.122Z","updated_at":"2025-10-20T12:05:08.625Z","avatar_url":"https://github.com/panos108.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GP-RTO-EI\n We present an RTO algorithm that relies on trust-region ideas in order to expedite and robustify convergence, and uses Bayesian optimization through Gaussian processes as a workhorse. We explore Expected Improvement and Upper Confidece Bound as adquisition functions, and adjust the size of the trust region based on the Gaussian processes’ ability to capture the plant-model mismatch in the cost and constraints. We draw parallels to expensive black-box optimization, hybrid modelling, reinforcement learning, and dual control which are all present in the proposed approach. Finally, we illustrate this new modifier-adaptation scheme on benchmark problems.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpanos108%2Fgp-rto-ei","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpanos108%2Fgp-rto-ei","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpanos108%2Fgp-rto-ei/lists"}