Multiple model-based reinforcement learning

Untitled work

(1995) · Cited by 10,558 · see citing works

Introduction to Reinforcement Learning

Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855 · see citing works

Adaptive Mixtures of Local Experts

Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.79 · Cited by 5,384 · see citing works

Learning to predict by the methods of temporal differences

Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148 · see citing works

Neuronlike adaptive elements that can solve difficult learning control problems

Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,238 · see citing works

Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning

Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119 · see citing works

Internal models in the cerebellum

Wolpert, D. M., Miall, R. C., & Kawato, M. (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01221-2 · Cited by 2,469 · see citing works

Reinforcement learning

Barto, A. (1998) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)38315-5 · Cited by 2,172 · see citing works

Multiple paired forward and inverse models for motor control

Wolpert, D. M., & Kawato, M. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00066-5 · Cited by 2,152 · see citing works

Computational principles of movement neuroscience

Wolpert, D. M., & Ghahramani, Z. (2000) · Nature Neuroscience · doi:10.1038/81497 · Cited by 2,122 · see citing works

10. Reinforcement learning

Zhang, B., Li, C., & Lin, N. (2020) · De Gruyter eBooks · doi:10.1515/9783110595567-011 · Cited by 1,939 · see citing works

Human cerebellar activity reflecting an acquired internal model of a new tool

Imamizu, H., Miyauchi, S., Tamada, T., Sasaki, Y., Takino, R., Pütz, B., Yoshioka, T., & Kawato, M. (2000) · Nature · doi:10.1038/35003194 · Cited by 1,000 · see citing works

Reinforcement Learning in Continuous Time and Space

Doya, K. (2000) · Neural Computation · doi:10.1162/089976600300015961 · Cited by 961 · see citing works

MOSAIC Model for Sensorimotor Learning and Control

Haruno, M., Wolpert, D. M., & Kawato, M. (2001) · Neural Computation · doi:10.1162/089976601750541778 · Cited by 710 · see citing works

Learning policies for partially observable environments: Scaling up

Littman, M. L., Cassandra, A. R., & Kaelbling, L. P. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50052-9 · Cited by 659 · see citing works

Reinforcement Learning with Hierarchies of Machines

Parr, R., & Russell, S. (1997) · Cited by 618 · see citing works

Feudal Reinforcement Learning

Dayan, P., & Hinton, G. E. (1992) · Cited by 488 · see citing works

Adaptation and learning using multiple models, switching, and tuning

Narendra, K. S., Balakrishnan, J., & Cılız, M. K. (1995) · IEEE Control Systems · doi:10.1109/37.387616 · Cited by 443 · see citing works

Transfer of learning by composing solutions of elemental sequential tasks

Singh, S. P. (1992) · Machine Learning · doi:10.1007/bf00992700 · Cited by 289 · see citing works

Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning

Morimoto, J., & Doya, K. (2001) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(01)00113-0 · Cited by 262 · see citing works

Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems.

Tani, J., & Nolfi, S. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00060-x · Cited by 259 · see citing works

HQ-Learning

Wiering, M., & Schmidhuber, J. (1997) · Adaptive Behavior · doi:10.1177/105971239700600202 · Cited by 163 · see citing works

Hierarchical MOSAIC for movement generation.

Haruno, M., Wolpert, D. M., & Kawato, M. (2003) · International Congress Series · doi:10.1016/s0531-5131(03)00190-0 · Cited by 132 · see citing works

Multiple paired forward-inverse models for human motor learning and control

Haruno, M., Wolpert, D. M., & Kawato, M. (1999) · Cited by 94 · see citing works

Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics

Pawelzik, K. R., Kohlmorgen, J., & Müller, K. (1996) · Neural Computation · doi:10.1162/neco.1996.8.2.340 · Cited by 93 · see citing works

Acquisition and contextual switching of multiple internal models for different viscous force fields

Wada, Y., Kawabata, Y., Kotosaka, S., Yamamoto, K., Kitazawa, S., & Kawato, M. (2003) · Neuroscience Research · doi:10.1016/s0168-0102(03)00094-4 · Cited by 78 · see citing works

Recognition of manipulated objects by motor learning with modular architecture networks

Gomi, H., & Kawato, M. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80053-x · Cited by 69 · see citing works

Mixtures of Controllers for Jump Linear and Non-Linear Plants

Cacciatore, T. W., & Nowlan, S. J. (1993) · Cited by 62 · see citing works

Inter-module credit assignment in modular reinforcement learning

Samejima, K., Doya, K., & Kawato, M. (2003) · Neural Networks · doi:10.1016/s0893-6080(02)00235-6 · Cited by 59 · see citing works

Improved Switching among Temporally Abstract Actions

Sutton, R. S., Singh, S. P., Precup, D., & Ravindran, B. (1998) · Cited by 33 · see citing works

From Isolation to Cooperation: An Alternative View of a System of Experts

Schaal, S., & Atkeson, C. G. (1995) · Cited by 33 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Overcoming catastrophic forgetting in neural networks

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899 · see citing works

Model-based influences on humans’ choices and striatal prediction errors

Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.027 · Cited by 1,779 · see citing works

A unifying computational framework for motor control and social interaction

Wolpert, D. M., Doya, K., & Kawato, M. (2003) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1238 · Cited by 1,228 · see citing works

Neuronal Reward and Decision Signals: From Theories to Data

Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126 · see citing works

States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning

Gläscher, J., Daw, N., Dayan, P., & O'Doherty, J. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.016 · Cited by 1,166 · see citing works

Intrinsic Motivation Systems for Autonomous Mental Development

Oudeyer, P.-Y., Kaplan, F., & Hafner, V. V. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2006.890271 · Cited by 1,126 · see citing works

Medial prefrontal cortex as an action-outcome predictor

Alexander, W. H., & Brown, J. W. (2011) · Nature Neuroscience · doi:10.1038/nn.2921 · Cited by 1,000 · see citing works

Goals and Habits in the Brain

Dolan, R. J., & Dayan, P. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.007 · Cited by 977 · see citing works

Formal theory of creativity, fun, and intrinsic motivation (1990–2010)

Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2056368 · Cited by 747 · see citing works

Neural computations underlying arbitration between model-based and model-free learning

Lee, S. W., Shimojo, S., & O’Doherty, J. P. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.028 · Cited by 642 · see citing works

Updating dopamine reward signals

Schultz, W. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.012 · Cited by 651 · see citing works

Reasoning, Learning, and Creativity: Frontal Lobe Function and Human Decision-Making

Collins, A., & Koechlin, E. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001293 · Cited by 558 · see citing works

The role of the ventromedial prefrontal cortex in abstract state-based inference during decision making in humans

Hampton, A. N., Bossaerts, P., & O’Doherty, J. P. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.1010-06.2006 · Cited by 498 · see citing works

Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms

Niv, Y., Daniel, R., Geana, A., Gershman, S. J., Leong, Y. C., Radulescu, A., & Wilson, R. C. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2978-14.2015 · Cited by 362 · see citing works

Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling

Redish, A. D., Jensen, S., Johnson, A., & Kurth-Nelson, Z. (2007) · Psychological Review · doi:10.1037/0033-295x.114.3.784 · Cited by 346 · see citing works

The basolateral amygdala in reward learning and addiction

Wassum, K. M., & Izquierdo, A. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.017 · Cited by 306 · see citing works

Model-based learning protects against forming habits

Gillan, C. M., Otto, A. R., Phelps, E. A., & Daw, N. D. (2015) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-015-0347-6 · Cited by 283 · see citing works

Reinforcement learning for clinical decision support in critical care: comprehensive review

Liu, S., See, K. C., Ngiam, K. Y., Celi, L. A., Sun, X., & Feng, M. (2020) · Journal of Medical Internet Research · doi:10.2196/18477 · Cited by 225 · see citing works

Towards Continual Reinforcement Learning: A Review and Perspectives

Khetarpal, K., Riemer, M., Rish, I., & Precup, D. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13673 · Cited by 224 · see citing works

Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta‐analysis

Fouragnan, E., Retzler, C., & Philiastides, M. G. (2018) · Human Brain Mapping · doi:10.1002/hbm.24047 · Cited by 176 · see citing works

Model-based predictions for dopamine

Langdon, A. J., Sharpe, M. J., Schoenbaum, G., & Niv, Y. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.10.006 · Cited by 161 · see citing works

Symbol emergence in robotics: a survey

Taniguchi, T., Nagai, T., Nakamura, T., Iwahashi, N., Ogata, T., & Asoh, H. (2016) · Advanced Robotics · doi:10.1080/01691864.2016.1164622 · Cited by 150 · see citing works

Hierarchical MOSAIC for movement generation.

Haruno, M., Wolpert, D. M., & Kawato, M. (2003) · International Congress Series · doi:10.1016/s0531-5131(03)00190-0 · Cited by 132 · see citing works

Human reinforcement learning subdivides structured action spaces by learning effector-specific values

Gershman, S. J., Pesaran, B., & Daw, N. D. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.2469-09.2009 · Cited by 118 · see citing works

The many faces of precision (Replies to commentaries on “Whatever next? Neural prediction, situated agents, and the future of cognitive science”)

Clark, A. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00270 · Cited by 108 · see citing works

Evidence for model-based computations in the human amygdala during Pavlovian conditioning

Prévost, C., McNamee, D., Jessup, R. K., Bossaerts, P., & O'Doherty, J. P. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002918 · Cited by 79 · see citing works

Contextual Markov Decision Processes

Hallak, A., Di Castro, D., & Mannor, S. (2015) · arXiv · doi:10.48550/arxiv.1502.02259 · Cited by 65 · see citing works

Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning

Igl, M., Farquhar, G., Luketina, J., Boehmer, W., & Whiteson, S. (2020) · arXiv · doi:10.48550/arxiv.2006.05826 · Cited by 17 · see citing works

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Colas, J. T., O’Doherty, J. P., & Grafton, S. T. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011950 · Cited by 6 · see citing works

Exploring Alternatives to Softmax Function

Banerjee, K., Vishak Prasad, C., Gupta, R., et al. (2021) · doi:10.5220/0010502000002996 · Cited by 56 · see citing works

Sparse-softmax: A Simpler and Faster Alternative Softmax TransformationPreprint

Sun, S., Zhang, Z., Huang, B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.12433 · Cited by 6 · see citing works

Improved softmax loss for deep learning‐based face and expression recognition

Zhou, J., Jia, X., Shen, L., et al. (2019) · Cognitive Computation and Systems · doi:10.1049/ccs.2019.0010 · Cited by 20 · see citing works

Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous DrivingPreprint

Kim, J. H., & Jeong, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05479

Behavior Acquisition on a Mobile Robot Using Reinforcement Learning With Continuous State Space

Arai, T., Toda, Y., & Kubota, N. (2019) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc48188.2019.8949181