{"id":"W3102089800","doi":"10.24963/ijcai.2021/294","title":"Deep Reinforcement Learning for Navigation in AAA Video Games","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ubisoft (Canada)","funders":"","keywords":"Reinforcement learning; Computer science; Variety (cybernetics); Video game; Representation (politics); Point (geometry); Artificial intelligence; Graph; Human–computer interaction; Multimedia; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005351566,0.0002146186,0.0002785082,0.0001517939,0.00007530465,0.0005012903,0.0009361085,0.0002106517,0.00007255712],"category_scores_gemma":[0.0003416578,0.0002218395,0.0001445593,0.0002201894,0.00003470526,0.0003448281,0.001376067,0.0005051741,0.00003274749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001874074,"about_ca_system_score_gemma":0.0001823526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003757508,"about_ca_topic_score_gemma":0.0002288926,"domain_scores_codex":[0.9980066,0.00007844209,0.0005744322,0.0007016211,0.0003095971,0.000329282],"domain_scores_gemma":[0.9985913,0.0002505265,0.0002154976,0.0006271434,0.0002566111,0.00005892265],"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":[0.000003984367,0.00002130255,0.0004472649,0.00009245857,0.00001461226,0.000009166884,0.003763278,0.8851333,0.0001697639,0.016591,0.0000901866,0.09366366],"study_design_scores_gemma":[0.0000430355,0.00004008739,0.0001141446,0.0002462177,0.000004262384,0.000001952667,0.0005895941,0.968007,0.01726464,0.01237367,0.001055411,0.0002599584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.012018,0.000207062,0.9833254,0.0007180667,0.0008585025,0.0005531218,9.29092e-8,0.0001701267,0.002149599],"genre_scores_gemma":[0.9004984,0.00005735773,0.09749329,0.0002048215,0.0001029775,0.0002726612,0.00004576719,0.00001485782,0.001309816],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8884804,"threshold_uncertainty_score":0.9046353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04080469939508952,"score_gpt":0.3152047365387844,"score_spread":0.2744000371436949,"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."}}