{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11992944","patent":{"patent_number":"US-11992944","title":"Data-efficient hierarchical reinforcement learning","assignee":null,"inventors":[],"filing_date":"2019-05-17T00:00:00.000Z","publication_date":"2024-05-28T00:00:00.000Z","cpc_codes":["B25J","B25J","B25J","G06N","G06N","G05B","G06N"],"num_claims":20,"abstract":"Training and/or utilizing a hierarchical reinforcement learning (HRL) model for robotic control. The HRL model can include at least a higher-level policy model and a lower-level policy model. Some implementations relate to technique(s) that enable more efficient off-policy training to be utilized in training of the higher-level policy model and/or the lower-level policy model. Some of those implementations utilize off-policy correction, which re-labels higher-level actions of experience data, generated in the past utilizing a previously trained version of the HRL model, with modified higher-level actions. The modified higher-level actions are then utilized to off-policy train the higher-level policy model. This can enable effective off-policy training despite the lower-level policy model being a different version at training time (relative to the version when the experience data was collected)."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Data-efficient hierarchical reinforcement learning","description":"Training and/or utilizing a hierarchical reinforcement learning (HRL) model for robotic control. The HRL model can include at least a higher-level policy model and a lower-level policy model. Some imp","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11992944","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11992944","citation_suggestion":"Patentable. \"Data-efficient hierarchical reinforcement learning\" (US-11992944). https://patentable.app/patents/US-11992944","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11992944","json":"https://patentable.app/api/llm-context/US-11992944","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T13:44:47.757Z"}