{"id":"W2783392051","doi":"10.1609/aaai.v32i1.12095","title":"Planning With Pixels in (Almost) Real Time","year":2018,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Computer science; Pixel; Artificial intelligence; State (computer science); Computer vision; Machine learning; Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007165591,0.0007006016,0.0005623949,0.0002902809,0.0003018562,0.0009584319,0.001074006,0.0006768594,0.007292818],"category_scores_gemma":[0.002739101,0.0003307585,0.0003247119,0.0003170998,0.0009383981,0.001500366,0.001112,0.0008787618,0.001119835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005364275,"about_ca_system_score_gemma":0.0008671292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004359597,"about_ca_topic_score_gemma":0.005884976,"domain_scores_codex":[0.999496,0.0001341246,0.0000300709,0.0001604039,0.0001246589,0.00005482429],"domain_scores_gemma":[0.9990444,0.0004933947,0.00007975168,0.0002380911,0.00009166905,0.00005269455],"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":[0.001099053,0.0002335472,0.001648617,0.0001703801,0.00007787195,0.0002132884,0.0003284406,0.5075651,0.02075013,0.03183742,0.006748753,0.4293275],"study_design_scores_gemma":[0.00006868398,0.0001105134,0.0003420243,0.00001234784,0.00001653763,0.00004571794,0.00005583154,0.9609364,0.0100815,0.02520786,0.003110301,0.00001230625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07712866,0.0001726409,0.9031546,0.0003033643,0.00007766337,0.0001133655,0.0001670989,0.006506564,0.01237603],"genre_scores_gemma":[0.6255273,0.00009874673,0.3684592,0.000127958,0.00001922139,0.000132558,0.0002782939,0.000361442,0.004995363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007292818,"threshold_uncertainty_score":0.0243969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07757191859625165,"score_gpt":0.3079997685792846,"score_spread":0.230427849983033,"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."}}