{"id":"W4391775400","doi":"10.1016/j.jhydrol.2024.130904","title":"Multiobjective multihydropower reservoir operation optimization with transformer-based deep reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Water resources management and optimization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Transformer; Context (archaeology); Mathematical optimization; Artificial intelligence; Voltage; Engineering; Mathematics","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":[0.0008872473,0.000746911,0.001189726,0.0004047839,0.0002623781,0.0007683383,0.000918212,0.001245382,0.002007541],"category_scores_gemma":[0.002091287,0.0005773794,0.0005216584,0.000346075,0.0006637776,0.0008788874,0.001085514,0.001088454,0.0001962449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007523065,"about_ca_system_score_gemma":0.001122149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007171475,"about_ca_topic_score_gemma":0.006484447,"domain_scores_codex":[0.9997848,0.00007539,0.00001023404,0.00004623545,0.00004225899,0.00004108586],"domain_scores_gemma":[0.9990401,0.0006456265,0.00008607614,0.00003194725,0.0001389489,0.00005720569],"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":[0.00002028968,0.00001770146,0.0001934919,0.00001391217,0.00001201984,0.00001540219,0.00000594518,0.9945151,0.0001899721,0.000611745,0.0001243769,0.004280195],"study_design_scores_gemma":[0.000001921523,0.000005199952,0.00001315708,8.192504e-7,0.000001155455,0.000001115057,7.735844e-7,0.9997382,0.00002632566,0.000196611,0.00001411954,6.101927e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09189991,0.0005035786,0.9002676,0.0004120003,0.00008854517,0.0000668763,0.0001022936,0.0003789433,0.006280261],"genre_scores_gemma":[0.9726661,0.00008229401,0.02533866,0.00008000016,0.00002068184,0.00006559981,0.00005495861,0.00003020987,0.001661633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007171475,"threshold_uncertainty_score":0.01425946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005135948611331508,"score_gpt":0.2039069302753064,"score_spread":0.1987709816639749,"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."}}