Reinforcement learning: the good, the bad and the ugly. Export

@article{citeulike:3444330, abstract = {Reinforcement learning provides both qualitative and quantitative frameworks for understanding and modeling adaptive decision-making in the face of rewards and punishments. Here we review the latest dispatches from the forefront of this field, and map out some of the territories where lie monsters.}, author = {Dayan, Peter and Niv, Yael}, citeulike-article-id = {3444330}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.conb.2008.08.003}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0959438808000767}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18708140}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18708140}, doi = {10.1016/j.conb.2008.08.003}, issn = {0959-4388}, journal = {Current opinion in neurobiology}, keywords = {human\_neuropsychology, neuroeconomics, optimal\_control\_theory, reinforcement}, month = {April}, number = {2}, pages = {185--196}, posted-at = {2009-10-26 08:41:48}, priority = {2}, title = {Reinforcement learning: the good, the bad and the ugly.}, url = {http://dx.doi.org/10.1016/j.conb.2008.08.003}, volume = {18}, year = {2008} }

TY - JOUR ID - citeulike:3444330 L3 - citeulike-article-id:3444330 N2 - Reinforcement learning provides both qualitative and quantitative frameworks for understanding and modeling adaptive decision-making in the face of rewards and punishments. Here we review the latest dispatches from the forefront of this field, and map out some of the territories where lie monsters. IS - 2 JF - Current opinion in neurobiology SN - 0959-4388 EP - 196 TI - Reinforcement learning: the good, the bad and the ugly. VL - 18 SP - 185 KW - human_neuropsychology KW - neuroeconomics KW - optimal_control_theory KW - reinforcement AU - Dayan, Peter AU - Niv, Yael PY - 2008/04// UR - http://dx.doi.org/10.1016/j.conb.2008.08.003 ER -