{"id":"W2991419354","doi":"10.1109/itsc.2019.8916928","title":"Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Reinforcement learning; Abstraction; Computer science; Modular design; Hierarchy; Motion (physics); Motion planning; Artificial intelligence; State space; Action (physics); Set (abstract data type); Human–computer interaction; Robot","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005102392,0.0005248567,0.0003778934,0.0002365364,0.0002499209,0.0004349863,0.0009642908,0.0005092089,0.001088837],"category_scores_gemma":[0.001147298,0.0003043364,0.0004360306,0.0001528571,0.0008260368,0.0005827306,0.0009072112,0.0009256715,0.0001896981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007212675,"about_ca_system_score_gemma":0.001075128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00679703,"about_ca_topic_score_gemma":0.007876249,"domain_scores_codex":[0.999747,0.00006187338,0.00001357094,0.0000604406,0.00007152019,0.00004558099],"domain_scores_gemma":[0.9995483,0.0001697715,0.00007331641,0.00007167509,0.00007522388,0.00006176576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000405754,0.00005087188,0.0006889126,0.00002726094,0.00002306125,0.00005853409,0.00006820107,0.9604934,0.004367879,0.005328838,0.0002421009,0.02861024],"study_design_scores_gemma":[0.000005570099,0.00002573231,0.00009430439,0.000002528064,0.000003819419,0.000006382558,0.000003867176,0.9967239,0.0005756897,0.002391601,0.0001635582,0.000003030918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04418853,0.00008255646,0.9522822,0.0001107479,0.00001650285,0.00005718824,0.00001964939,0.000618197,0.002624514],"genre_scores_gemma":[0.9130532,0.00005340072,0.08539353,0.00003739644,0.000007348599,0.00006793404,0.00003973494,0.00002821125,0.001319075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00679703,"threshold_uncertainty_score":0.01351488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03906097747933342,"score_gpt":0.2925437606726954,"score_spread":0.253482783193362,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}