Reinforcement learning: bringing together computation and cognition
暂无分享,去创建一个
[1] K. Doya,et al. Representation of Action-Specific Reward Values in the Striatum , 2005, Science.
[2] James A. Waltz,et al. Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia , 2017, Biological Psychiatry.
[3] J. Wickens,et al. A cellular mechanism of reward-related learning , 2001, Nature.
[4] M. D’Esposito,et al. Frontal Cortex and the Discovery of Abstract Action Rules , 2010, Neuron.
[5] Michael J Frank,et al. Human EEG Uncovers Latent Generalizable Rule Structure during Learning , 2014, The Journal of Neuroscience.
[6] H. Harlow,et al. The formation of learning sets. , 1949, Psychological review.
[7] M. Frank,et al. Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning , 2016, Cognition.
[8] David Badre,et al. Functional Magnetic Resonance Imaging Evidence for a Hierarchical Organization of the Prefrontal Cortex , 2007, Journal of Cognitive Neuroscience.
[9] M. Gluck,et al. Interactive memory systems in the human brain , 2001, Nature.
[10] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[11] C. Summerfield,et al. A Neural Representation of Prior Information during Perceptual Inference , 2008, Neuron.
[12] Anne Gabrielle Eva Collins,et al. The Cost of Structure Learning , 2017, Journal of Cognitive Neuroscience.
[13] Anne G E Collins,et al. Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. , 2014, Psychological review.
[14] Etienne Koechlin,et al. Foundations of human reasoning in the prefrontal cortex , 2014, Science.
[15] David Badre,et al. Learning and transfer of working memory gating policies , 2017, Cognition.
[16] A G Barto,et al. Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.
[17] P. Dayan,et al. Model-based influences on humans’ choices and striatal prediction errors , 2011, Neuron.
[18] Richard S. Sutton,et al. Dimensions of Reinforcement Learning , 1998 .
[19] Charles Kemp,et al. How to Grow a Mind: Statistics, Structure, and Abstraction , 2011, Science.
[20] Andrew G. Barto,et al. Reinforcement learning , 1998 .
[21] Michael L. Littman,et al. State Abstractions for Lifelong Reinforcement Learning , 2018, ICML.
[22] M. A. MacIver,et al. Neuroscience Needs Behavior: Correcting a Reductionist Bias , 2017, Neuron.
[23] Ethan S. Bromberg-Martin,et al. Midbrain Dopamine Neurons Signal Preference for Advance Information about Upcoming Rewards , 2009, Neuron.
[24] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[25] Denise M Werchan,et al. Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants , 2016, The Journal of Neuroscience.
[26] Peter L. Bartlett,et al. RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning , 2016, ArXiv.
[27] Anne G. E. Collins. The tortoise and the hare: interactions between reinforcement learning and working memory , 2017 .
[28] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[29] Mehdi Khamassi,et al. Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving task , 2017, Behavioural Brain Research.
[30] L. Wilbrecht,et al. Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value , 2012, Nature Neuroscience.
[31] Samuel Ritter,et al. Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study , 2017, ICML.
[32] Daan Wierstra,et al. One-shot Learning with Memory-Augmented Neural Networks , 2016, ArXiv.
[33] Joel Z. Leibo,et al. Prefrontal cortex as a meta-reinforcement learning system , 2018, bioRxiv.
[34] M Botvinick,et al. Episodic Control as Meta-Reinforcement Learning , 2018, bioRxiv.
[35] Peter Dayan,et al. Improving Generalization for Temporal Difference Learning: The Successor Representation , 1993, Neural Computation.
[36] M. Botvinick,et al. Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective , 2009, Cognition.
[37] Anne G E Collins,et al. How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis , 2012, The European journal of neuroscience.
[38] David Badre,et al. Working Memory Load Strengthens Reward Prediction Errors , 2017, The Journal of Neuroscience.
[39] Brad E. Pfeiffer,et al. Reverse Replay of Hippocampal Place Cells Is Uniquely Modulated by Changing Reward , 2016, Neuron.
[40] Anne G E Collins,et al. Cognitive control over learning: creating, clustering, and generalizing task-set structure. , 2013, Psychological review.
[41] David J. Foster. Replay Comes of Age. , 2017, Annual review of neuroscience.
[42] Denise M Werchan,et al. 8-Month-Old Infants Spontaneously Learn and Generalize Hierarchical Rules , 2015, Psychological science.
[43] G. E. Alexander,et al. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. , 1986, Annual review of neuroscience.
[44] P. Dayan,et al. A framework for mesencephalic dopamine systems based on predictive Hebbian learning , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[45] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[46] Marcin Andrychowicz,et al. One-Shot Imitation Learning , 2017, NIPS.
[47] Q. Huys. Bayesian Approaches to Learning and Decision-Making , 2018 .
[48] Mel W. Khaw,et al. Reminders of past choices bias decisions for reward in humans , 2017, Nature Communications.
[49] K. Norman,et al. Reinstated episodic context guides sampling-based decisions for reward , 2017, Nature Neuroscience.
[50] Anne Gabrielle Eva Collins,et al. Motor Demands Constrain Cognitive Rule Structures , 2016, PLoS Comput. Biol..
[51] M. Botvinick,et al. The successor representation in human reinforcement learning , 2016, bioRxiv.
[52] David Badre,et al. Learning and transfer of working memory gating policies , 2017 .
[53] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[54] Joshua B Tenenbaum,et al. Toward the neural implementation of structure learning , 2016, Current Opinion in Neurobiology.
[55] B. Averbeck,et al. Ventral Striatum Lesions Do Not Affect Reinforcement Learning With Deterministic Outcomes on Slow Time Scales , 2017, Behavioral neuroscience.
[56] Michael J. Frank,et al. Compositional clustering in task structure learning , 2017, bioRxiv.
[57] Michael J. Frank,et al. A Control Theoretic Model of Adaptive Learning in Dynamic Environments , 2018, Journal of Cognitive Neuroscience.
[58] A. Markman,et al. Journal of Experimental Psychology : General Retrospective Revaluation in Sequential Decision Making : A Tale of Two Systems , 2012 .
[59] E. Koechlin,et al. Reasoning, Learning, and Creativity: Frontal Lobe Function and Human Decision-Making , 2012, PLoS biology.
[60] Krzysztof J. Gorgolewski,et al. Reward Learning over Weeks Versus Minutes Increases the Neural Representation of Value in the Human Brain , 2018, The Journal of Neuroscience.
[61] Eytan Ruppin,et al. Actor-critic models of the basal ganglia: new anatomical and computational perspectives , 2002, Neural Networks.
[62] Michael J. Frank,et al. Within and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory , 2017, bioRxiv.
[63] Sergio Gomez Colmenarejo,et al. Hybrid computing using a neural network with dynamic external memory , 2016, Nature.
[64] Jonathan D. Cohen,et al. Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement , 2008, NIPS.
[65] D. Hassabis,et al. Neuroscience-Inspired Artificial Intelligence , 2017, Neuron.