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klouie reinforcement_learning [21 articles]

最近 klouie さんのライブラリに追加された論文の中から タグ reinforcement_learning. You can also see everyone's reinforcement_learning.
  • Low-Serotonin Levels Increase Delayed Reward Discounting in Humans
    J. Neurosci., Vol. 28, No. 17. (23 April 2008), pp. 4528-4532.
    by Nicolas Schweighofer, Mathieu Bertin, Kazuhiro Shishida, Yasumasa Okamoto, Saori C Tanaka, Shigeto Yamawaki, Kenji Doya
  • A cellular mechanism of reward-related learning.
    Nature, Vol. 413, No. 6851. (6 September 2001), pp. 67-70.
    by JN Reynolds, BI Hyland, JR Wickens
  • By carrot or by stick: cognitive reinforcement learning in parkinsonism.
    Science, Vol. 306, No. 5703. (10 December 2004), pp. 1940-1943.
    by MJ Frank, LC Seeberger, RC O'reilly
  • Lateral Habenula Stimulation Inhibits Rat Midbrain Dopamine Neurons through a GABAA Receptor-Mediated Mechanism
    J. Neurosci., Vol. 27, No. 26. (27 June 2007), pp. 6923-6930.
    by Huifang Ji, Paul D Shepard
  • Solving the Distal Reward Problem through Linkage of STDP and Dopamine Signaling.
    Cereb Cortex (13 January 2007)
    by Eugene M M Izhikevich
  • Coincident but distinct messages of midbrain dopamine and striatal tonically active neurons.
    Neuron, Vol. 43, No. 1. (8 July 2004), pp. 133-143.
    by G Morris, D Arkadir, A Nevet, E Vaadia, H Bergman
  • notes Midbrain dopamine neurons encode decisions for future action
    Nature Neuroscience, Vol. 9, No. 8. (23 July 2006), pp. 1057-1063.
    by Genela Morris, Alon Nevet, David Arkadir, Eilon Vaadia, Hagai Bergman
  • DNA targeting of rhinal cortex D2 receptor protein reversibly blocks learning of cues that predict reward.
    Proc Natl Acad Sci U S A, Vol. 101, No. 33. (17 August 2004), pp. 12336-12341.
    by Z Liu, BJ Richmond, EA Murray, RC Saunders, S Steenrod, BK Stubblefield, DM Montague, EI Ginns
  • Representation and Timing in Theories of the Dopamine System
    Neural Comp., Vol. 18, No. 7. (1 July 2006), pp. 1637-1677.
    by Nathaniel D Daw, Aaron C Courville, David S Tourtezky
  • notes Linear-Nonlinear-Poisson models of primate choice dynamics.
    J Exp Anal Behav, Vol. 84, No. 3. (November 2005), pp. 581-617.
    by GS Corrado, LP Sugrue, HS Seung, WT Newsome
  • Dynamic response-by-response models of matching behavior in rhesus monkeys.
    J Exp Anal Behav, Vol. 84, No. 3. (November 2005), pp. 555-579.
    by B Lau, PW Glimcher
  • Between MDPs and semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
    Artificial Intelligence, No. 112. (1999), pp. 181-211.
    by RS Sutton, D Precup, S Singh
  • Representation of action-specific reward values in the striatum.
    Science, Vol. 310, No. 5752. (25 November 2005), pp. 1337-1340.
    by K Samejima, Y Ueda, K Doya, M Kimura
  • Addiction as a computational process gone awry.
    Science, Vol. 306, No. 5703. (10 December 2004), pp. 1944-1947.
    by AD Redish
  • Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network.
    J Neurosci, Vol. 25, No. 26. (29 June 2005), pp. 6235-6242.
    by WX Pan, R Schmidt, JR Wickens, BI Hyland
  • Midbrain dopamine neurons encode a quantitative reward prediction error signal.
    Neuron, Vol. 47, No. 1. (7 July 2005), pp. 129-141.
    by HM Bayer, PW Glimcher
  • Behavioral considerations suggest an average reward TD model of the dopamine system
    Neurocomputing, Vol. 32-33 (June 2000), pp. 679-684.
    by ND Daw, David S Touretzky
  • A neural substrate of prediction and reward.
    Science, Vol. 275, No. 5306. (14 March 1997), pp. 1593-1599.
    by W Schultz, P Dayan, PR Montague
  • Long-term reward prediction in TD models of the dopamine system.
    Neural Comput, Vol. 14, No. 11. (November 2002), pp. 2567-2583.
    by ND Daw, DS Touretzky
  • Temporal sequence learning, prediction, and control: a review of different models and their relation to biological mechanisms.
    Neural Comput, Vol. 17, No. 2. (February 2005), pp. 245-319.
  • Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
    (01 March 1998)
    by Richard S Sutton, Andrew G Barto
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