Generalization and search in risky environments
暂无分享,去创建一个
Andreas Krause | Maarten Speekenbrink | Eric Schulz | Charley M. Wu | Quentin J. M. Huys | Andreas Krause | M. Speekenbrink | Q. Huys | Eric Schulz
[1] R. Shepard,et al. Toward a universal law of generalization for psychological science. , 1987, Science.
[2] A. Houston,et al. General results concerning the trade-off between gaining energy and avoiding predation , 1993 .
[3] Angela P. Schoellig,et al. Safe and robust learning control with Gaussian processes , 2015, 2015 European Control Conference (ECC).
[4] Andreas Krause,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.
[5] Peter Auer,et al. Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..
[6] Dominik R. Bach,et al. Heuristic and optimal policy computations in the human brain during sequential decision-making , 2018, Nature Communications.
[7] M. Zollo,et al. The neuro-scientific foundations of the exploration-exploitation dilemma , 2010 .
[8] S. Kakade,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2012, IEEE Transactions on Information Theory.
[9] Gregory L. Stuart,et al. Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). , 2002, Journal of experimental psychology. Applied.
[10] Samuel J. Gershman,et al. Compositional inductive biases in function learning , 2016, Cognitive Psychology.
[11] P. Chrisp. Mapping The Unknown , 1996 .
[12] E. Weber,et al. Affective and deliberative processes in risky choice: age differences in risk taking in the Columbia Card Task. , 2009, Journal of experimental psychology. Learning, memory, and cognition.
[13] M. Speekenbrink,et al. Putting bandits into context: How function learning supports decision making , 2016, bioRxiv.
[14] M. Frank,et al. Computational psychiatry as a bridge from neuroscience to clinical applications , 2016, Nature Neuroscience.
[15] H. Robbins,et al. Asymptotically efficient adaptive allocation rules , 1985 .
[16] Karl J. Friston,et al. Computational psychiatry , 2012, Trends in Cognitive Sciences.
[17] Thomas L. Griffiths,et al. Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic , 2015, Top. Cogn. Sci..
[18] Dominik R. Bach,et al. Maintaining Homeostasis by Decision-Making , 2015, PLoS Comput. Biol..
[19] Peter M. Todd,et al. A Game of Hide and Seek: Expectations of Clumpy Resources Influence Hiding and Searching Patterns , 2015, PloS one.
[20] J. Busemeyer,et al. Learning Functional Relations Based on Experience With Input-Output Pairs by Humans and Artificial Neural Networks , 2005 .
[21] Harold J. Kushner,et al. A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise , 1964 .
[22] Peter Dayan,et al. Interplay of approximate planning strategies , 2015, Proceedings of the National Academy of Sciences.
[23] Thomas L. Griffiths,et al. Modeling human function learning with Gaussian processes , 2008, NIPS.
[24] Jonathan D. Nelson,et al. Generalization guides human exploration in vast decision spaces , 2017, Nature Human Behaviour.
[25] J. Gittins. Bandit processes and dynamic allocation indices , 1979 .
[26] Andreas Krause,et al. Safe Model-based Reinforcement Learning with Stability Guarantees , 2017, NIPS.
[27] M. Botvinick,et al. Evidence integration in model-based tree search , 2015, Proceedings of the National Academy of Sciences.
[28] Andreas Krause,et al. A tutorial on Gaussian process regression with a focus on exploration-exploitation scenarios , 2016 .
[29] Thomas T. Hills,et al. Exploration versus exploitation in space, mind, and society , 2015, Trends in Cognitive Sciences.
[30] M. Lee,et al. A Bayesian analysis of human decision-making on bandit problems , 2009 .
[31] Adam N. Sanborn,et al. Bridging Levels of Analysis for Probabilistic Models of Cognition , 2012 .
[32] Paul M. Krueger,et al. Strategies for exploration in the domain of losses , 2017, Judgment and Decision Making.
[33] David Ardia,et al. DEoptim: An R Package for Global Optimization by Differential Evolution , 2009 .
[34] Nando de Freitas,et al. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.
[35] Christopher G. Lucas,et al. A rational model of function learning , 2015, Psychonomic Bulletin & Review.
[36] Samuel J. Gershman,et al. Structured Representations of Utility in Combinatorial Domains , 2017 .
[37] Alkis Gotovos,et al. Safe Exploration for Optimization with Gaussian Processes , 2015, ICML.
[38] Cliff Zou,et al. A GAME OF “HIDE AND SEEK” , 2022 .
[39] Dominik R. Bach,et al. Anxiety-Like Behavioural Inhibition Is Normative under Environmental Threat-Reward Correlations , 2015, PLoS Comput. Biol..
[40] N. Kolling,et al. (Reinforcement?) Learning to forage optimally , 2017, Current Opinion in Neurobiology.
[41] Naomi Ehrich Leonard,et al. Parameter Estimation in Softmax Decision-Making Models With Linear Objective Functions , 2015, IEEE Transactions on Automation Science and Engineering.
[42] Jonathan D. Nelson,et al. Mapping the unknown: The spatially correlated multi-armed bandit , 2017, bioRxiv.
[43] Vaibhav Srivastava,et al. Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits , 2013, Proceedings of the IEEE.
[44] Andreas Krause,et al. Better safe than sorry: Risky function exploitation through safe optimization , 2016, CogSci.
[45] P. Dayan,et al. Neural Prediction Errors Reveal a Risk-Sensitive Reinforcement-Learning Process in the Human Brain , 2012, The Journal of Neuroscience.