{"id":"W630726055","doi":"","title":"Comprehensive Analysis of Reinforcement Learning Methods and Parameters for Adaptive Traffic Signal Control","year":2011,"lang":"en","type":"article","venue":"Transportation Research Board 90th Annual MeetingTransportation Research Board","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Microsimulation; Intersection (aeronautics); Computer science; Adaptive control; Controller (irrigation); SIGNAL (programming language); Control theory (sociology); Real-time computing; Control engineering; Control (management); Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002788583,0.0009089863,0.0007923428,0.0006299849,0.0003152842,0.001055597,0.0009820501,0.0009740136,0.002353058],"category_scores_gemma":[0.009472197,0.0004296344,0.0006450493,0.0005916271,0.0009345328,0.001359378,0.0007627088,0.001880578,0.0003033277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001388924,"about_ca_system_score_gemma":0.001152251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003649354,"about_ca_topic_score_gemma":0.001935531,"domain_scores_codex":[0.9990096,0.0003238495,0.00004889154,0.0001174337,0.0004319766,0.00006822353],"domain_scores_gemma":[0.9966499,0.002493685,0.0002300909,0.000160987,0.0004074467,0.00005788004],"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":[0.00002047723,0.00003144221,0.0003137845,0.00007740033,0.00002981821,0.00002530296,0.00002814319,0.9089833,0.0008763028,0.04034549,0.0003619347,0.04890649],"study_design_scores_gemma":[0.000003617159,0.00001774908,0.00007371214,0.00001116013,0.000004448566,0.00001342535,0.00000315045,0.9887171,0.0002952264,0.01018914,0.0006659674,0.000005245992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004371208,0.001302408,0.989942,0.0001420729,0.00002919155,0.00003122283,0.00001199485,0.0001021088,0.00406779],"genre_scores_gemma":[0.7509177,0.003572122,0.2372702,0.0001470528,0.0002142632,0.000317538,0.00007765932,0.0001594694,0.007324055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003649354,"threshold_uncertainty_score":0.01474762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0771221145176846,"score_gpt":0.3606040578369033,"score_spread":0.2834819433192187,"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."}}