{"id":"W2081239738","doi":"10.1109/adprl.2007.368191","title":"Opposition-Based Reinforcement Learning in the Management of Water Resources","year":2007,"lang":"en","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Exploit; Bellman equation; Opposition (politics); Artificial intelligence; Action learning; Operations research; Mathematical optimization; Engineering; Computer security; Mathematics; Law; Political science; Cooperative learning","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":[0.001434099,0.0003355558,0.000700085,0.000298289,0.0003008718,0.0005762653,0.0007886599,0.0008168786,0.0008704077],"category_scores_gemma":[0.002760827,0.0002407904,0.0003091254,0.0003401512,0.001555035,0.0009871616,0.0009508382,0.0009504044,0.0001450703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007047991,"about_ca_system_score_gemma":0.0005561204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001519077,"about_ca_topic_score_gemma":0.001151601,"domain_scores_codex":[0.99938,0.0003470941,0.00002170465,0.00005933447,0.0001425039,0.00004944044],"domain_scores_gemma":[0.9990119,0.00068569,0.0001155541,0.00004175595,0.0000992009,0.00004592472],"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":[0.00009740288,0.00004911457,0.0004215297,0.00005616147,0.0000319445,0.00008911277,0.00007019615,0.9109842,0.001982105,0.0541458,0.0004518271,0.0316207],"study_design_scores_gemma":[0.00001701758,0.00005003335,0.00005311403,0.000004957824,0.000004272827,0.00001546152,0.00000579383,0.9809003,0.0003868059,0.01790941,0.0006469133,0.0000060151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02243333,0.0004858521,0.973114,0.0003440907,0.00005364485,0.00003382667,0.000009112846,0.00008936353,0.003436844],"genre_scores_gemma":[0.9128029,0.0003887843,0.08356976,0.000144462,0.00005853218,0.0001122418,0.00001522873,0.00002595112,0.00288223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001519077,"threshold_uncertainty_score":0.007584333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01448153100697104,"score_gpt":0.2446827248964791,"score_spread":0.230201193889508,"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."}}