Reinforcement learning, fast and slow

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works

Pattern Recognition and Machine Learning

(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824 · see citing works

Playing Atari with Deep Reinforcement Learning

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works

A neural substrate of prediction and reward

Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512 · see citing works

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory

McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256 · see citing works

WaveNet: a generative model for raw audio

Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911 · see citing works

Matching Networks for One Shot LearningPreprint

Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04080 · Cited by 4,874 · see citing works

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · see citing works

Toward an instance theory of automatization.

Logan, G. D. (1988) · Psychological Review · doi:10.1037/0033-295x.95.4.492 · Cited by 3,466 · see citing works

The economy of brain network organization

Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284 · see citing works

Evolving Neural Networks through Augmenting Topologies

Stanley, K. O., & Miikkulainen, R. P. (2002) · Evolutionary Computation · doi:10.1162/106365602320169811 · Cited by 3,269 · see citing works

Untitled work

(2017) · International Conference on Learning Representations · Cited by 3,248 · see citing works

Human-level concept learning through probabilistic program induction

Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817 · see citing works

Building machines that learn and think like people

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533 · see citing works

International Conference on Artificial Neural Networks

Boubacar, H. A., & Lecoeuche, S. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90065-s · Cited by 2,088 · see citing works

A framework for mesencephalic dopamine systems based on predictive Hebbian learning

Montague, P., Dayan, P., & Sejnowski, T. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-05-01936.1996 · Cited by 2,066 · see citing works

Organization, development and function of complex brain networks

SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.07.008 · Cited by 2,055 · see citing works

The formation of learning sets.

Harlow, H. F. (1949) · Psychological Review · doi:10.1037/h0062474 · Cited by 1,959 · see citing works

Rainbow: Combining Improvements in Deep Reinforcement Learning

Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11796 · Cited by 1,846 · see citing works

How to grow a mind: Statistics, structure, and abstraction

Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815 · 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

Using goal-driven deep learning models to understand sensory cortex

Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · see citing works

Temporal difference learning and TD-Gammon

Tesauro, G. (1995) · Communications of the ACM · doi:10.1145/203330.203343 · Cited by 1,618 · cited 3× · see citing works

Goal-directed instrumental action: contingency and incentive learning and their cortical substrates

Balleine, B. W., & Dickinson, A. (1998) · Neuropharmacology · doi:10.1016/s0028-3908(98)00033-1 · Cited by 1,541 · see citing works

Meta-learning with memory-augmented neural networks

Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016) · International Conference on Machine Learning · Cited by 1,504 · see citing works

Learning to learn by gradient descent by gradient descent

Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., & de Freitas, N. (2016) · arXiv · doi:10.48550/arxiv.1606.04474 · Cited by 1,486 · see citing works

Toward a modern theory of adaptive networks: Expectation and prediction.

Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450 · see citing works

Hybrid computing using a neural network with dynamic external memory

Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature · doi:10.1038/nature20101 · Cited by 1,412 · see citing works

Learning to Learn

Thrun, S., & Pratt, L. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2 · Cited by 1,291 · see citing works

Schemas and Memory Consolidation.

Tse, D., Langston, R. F., Kakeyama, M., Bethus, I., Spooner, P. A., Wood, E. R., Witter, M. P., & Morris, R. G. M. (2007) · Science · doi:10.1126/science.1135935 · Cited by 1,244 · see citing works

Deep supervised, but not unsupervised, models may explain IT cortical representation

Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204 · 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

Frontal cortex and reward-guided learning and decision-making

Rushworth, M. F. S., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.014 · Cited by 1,111 · see citing works

Core knowledge

Spelke, E. S., & Kinzler, K. D. (2007) · Developmental Science · doi:10.1111/j.1467-7687.2007.00569.x · Cited by 1,089 · see citing works

Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

O’Reilly, R. C., & Frank, M. J. (2005) · Neural Computation · doi:10.1162/089976606775093909 · Cited by 1,066 · see citing works

What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior

Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.002 · Cited by 1,055 · 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

Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis

Glimcher, P. W. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1014269108 · Cited by 955 · see citing works

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationPreprint

Kulkarni, T. D., Narasimhan, K., Saeedi, A., & Tenenbaum, J. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06057 · Cited by 895 · see citing works

Deep Learning: A Critical Appraisal

Marcus, G. (2018) · arXiv · doi:10.48550/arxiv.1801.00631 · Cited by 853 · see citing works

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

Moravčík, M., Schmid, M., BURCH, N. R., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., & Bowling, M. (2017) · Science · doi:10.1126/science.aam6960 · Cited by 800 · see citing works

Temporal Difference Learning and TD-Gammon

Tesauro, G. (1995) · ICGA Journal · doi:10.3233/icg-1995-18207 · Cited by 770 · see citing works

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated

Kumaran, D., Hassabis, D., & McClelland, J. L. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.05.004 · Cited by 736 · see citing works

Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises

Kawamoto, A. H., McClelland, J. L., & Rumelhart, D. E. (1989) · The American Journal of Psychology · doi:10.2307/1423065 · Cited by 725 · see citing works

How Learning Can Guide Evolution.

Hinton, G. E., & Nowlan, S. J. (1987) · Cited by 682 · see citing works

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al. (2019) · Science · doi:10.1126/science.aau6249 · Cited by 662 · see citing works

A neural network that finds a naturalistic solution for the production of muscle activity

Sussillo, D., Churchland, M. M., Kaufman, M. T., & Shenoy, K. V. (2015) · Nature Neuroscience · doi:10.1038/nn.4042 · Cited by 598 · see citing works

Prefrontal cortex as a meta-reinforcement learning system

Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0147-8 · Cited by 596 · see citing works

Learning to reinforcement learn

Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv · doi:10.48550/arxiv.1611.05763 · Cited by 579 · see citing works

Learning to Learn Using Gradient Descent

Hochreiter, S., Younger, A. S., & Conwell, P. R. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44668-0_13 · Cited by 575 · see citing works

Probabilistic models of cognition: exploring representations and inductive biases

Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568 · see citing works

Learning to Learn: Introduction and Overview

Thrun, S., & Pratt, L. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_1 · Cited by 521 · see citing works

Train faster, generalize better: stability of stochastic gradient descent

Hardt, M., Recht, B., & Singer, Y. (2016) · International Conference on Machine Learning · Cited by 516 · see citing works

Neural scene representation and rendering

Eslami, S. M. A., Rezende, D. J., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., et al. (2018) · Science · doi:10.1126/science.aar6170 · Cited by 511 · see citing works

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., & Abbeel, P. (2016) · arXiv · doi:10.48550/arxiv.1611.02779 · Cited by 503 · see citing works

Reinforcement learning and episodic memory in humans and animals: an integrative framework

Gershman, S. J., & Daw, N. D. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033625 · Cited by 446 · see citing works

Value, search, persistence and model updating in anterior cingulate cortex

Kolling, N., Wittmann, M. K., Behrens, T. E. J., Boorman, E. D., Mars, R. B., & Rushworth, M. F. S. (2016) · Nature Neuroscience · doi:10.1038/nn.4382 · Cited by 439 · see citing works

Neural Circuitry of Reward Prediction Error

Watabe-Uchida, M., Eshel, N., & Uchida, N. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031109 · Cited by 437 · see citing works

Learning a synaptic learning rule

Bengio, Y., Bengio, S., & Cloutier, J. (1991) · IJCNN-91-Seattle International Joint Conference on Neural Networks · doi:10.1109/ijcnn.1991.155621 · Cited by 402 · see citing works

Prefrontal cortex and flexible cognitive control: Rules without symbols

Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502455102 · Cited by 397 · see citing works

Train faster, generalize better: Stability of stochastic gradient descentPreprint

Hardt, M., Recht, B., & Singer, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.01240 · Cited by 358 · see citing works

Dopamine transients are sufficient and necessary for acquisition of model-based associations

Sharpe, M. J., Chang, C. Y., Liu, M. A., Batchelor, H. M., Mueller, L. E., Jones, J. L., Niv, Y., & Schoenbaum, G. M. (2017) · Nature Neuroscience · doi:10.1038/nn.4538 · Cited by 284 · see citing works

Population Based Training of Neural Networks

Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., et al. (2017) · arXiv · doi:10.48550/arxiv.1711.09846 · Cited by 284 · see citing works

Network architecture of the long-distance pathways in the macaque brain

Modha, D. S., & Singh, R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008054107 · Cited by 283 · see citing works

Recasting Gradient-Based Meta-Learning as Hierarchical BayesPreprint

Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.08930 · Cited by 280 · see citing works

Meta-learning in Reinforcement Learning

Schweighofer, N., & Doya, K. (2003) · Neural Networks · doi:10.1016/s0893-6080(02)00228-9 · Cited by 261 · see citing works

Dorsal hippocampus contributes to model-based planning

Miller, K. J., Botvinick, M., & Brody, C. D. (2017) · Nature Neuroscience · doi:10.1038/nn.4613 · Cited by 243 · see citing works

Neural Episodic ControlPreprint

Pritzel, A., Uría, B., Srinivasan, S., Puigdomènech, A., Vinyals, O., Hassabis, D., Wierstra, D., & Blundell, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01988 · Cited by 235 · see citing works

Reminders of past choices bias decisions for reward in humans

Bornstein, A. M., Khaw, M. W., Daphna Shohamy, A., & Daw, N. D. (2017) · Nature Communications · doi:10.1038/ncomms15958 · Cited by 222 · see citing works

Darla Improving zero-shot transfer in reinforcement learning

Higgins, I., Pal, A., Rusu, A. A., Matthey, L., Burgess, C. P., Pritzel, A., Botvinick, M., Blundell, C., & Lerchner, A. (2017) · arXiv · doi:10.48550/arxiv.1707.08475 · Cited by 219 · see citing works

Graded modality-specific specialisation in semantics: A computational account of optic aphasia

Plaut, D. C. (2002) · Cognitive Neuropsychology · doi:10.1080/02643290244000112 · Cited by 209 · see citing works

Control of exploitation–exploration meta-parameter in reinforcement learning

Ishii, S., Yoshida, W., & Yoshimoto, J. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00056-4 · Cited by 209 · see citing works

Reward-based training of recurrent neural networks for cognitive and value-based tasks

Song, H., Yang, G. R., & Wang, X. (2017) · eLife · doi:10.7554/elife.21492 · Cited by 169 · see citing works

Untitled work

(2007) · MPG.PuRe (Max Planck Society) · Cited by 169 · see citing works

Decoupled Neural Interfaces using Synthetic GradientsPreprint

Jaderberg, M., Czarnecki, W. M., Osindero, S., Vinyals, O., Graves, A., Silver, D., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.05343 · Cited by 166 · see citing works

Model-Free Episodic Control

Blundell, C., Uria, B., Pritzel, A., Li, Y., Ruderman, A., Leibo, J. Z., Rae, J., Wierstra, D., & Hassabis, D. (2016) · arXiv · doi:10.48550/arxiv.1606.04460 · Cited by 162 · see citing works

Reinstated episodic context guides sampling-based decisions for reward

Bornstein, A. M., & Norman, K. A. (2017) · Nature Neuroscience · doi:10.1038/nn.4573 · Cited by 161 · see citing works

Midbrain dopamine neurons compute inferred and cached value prediction errors in a common framework

Sadacca, B. F., Jones, J. L., & Schoenbaum, G. M. (2016) · eLife · doi:10.7554/elife.13665 · Cited by 137 · see citing works

Unsupervised Predictive Memory in a Goal-Directed AgentPreprint

Wayne, G., Hung, C.-C., Amos, D., Mirza, M., Ahuja, A., Grabska‐Barwińska, A., Rae, J. W., Mirowski, P., Leibo, J. Z., Santoro, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10760 · Cited by 137 · see citing works

Toward an integration of deep learning and neuroscience

Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works

Theoretical Models of Learning to Learn

Baxter, J. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2_4 · Cited by 111 · see citing works

Multilevel structure in behaviour and in the brain: a model of Fuster's hierarchy

Botvinick, M. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2056 · Cited by 110 · see citing works

A dynamic code for economic object valuation in prefrontal cortex neurons

Tsutsui, K., Fabian Grabenhorst, B., Kobayashi, S., & Schultz, W. (2016) · Nature Communications · doi:10.1038/ncomms12554 · Cited by 83 · see citing works

Metalearning

Schaul, T., & Schmidhuber, J. (2010) · Scholarpedia · doi:10.4249/scholarpedia.4650 · Cited by 73 · see citing works

Hippocampal pattern separation supports reinforcement learning

Ballard, I. C., Wagner, A. D., & McClure, S. M. (2019) · Nature Communications · doi:10.1038/s41467-019-08998-1 · Cited by 65 · see citing works

Been There, Done That: Meta-Learning with Episodic RecallPreprint

Ritter, S., Wang, J. X., Kurth-Nelson, Z. L., Jayakumar, S. M., Blundell, C., Pascanu, R., & Botvinick, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09692 · Cited by 60 · see citing works

Human-level performance in first-person multiplayer games with population-based deep reinforcement learningPreprint

Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.01281 · Cited by 59 · see citing works

Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters

Khamassi, M., Enel, P., Dominey, P. F., & Procyk, E. (2013) · Progress in brain research · doi:10.1016/b978-0-444-62604-2.00022-8 · Cited by 54 · see citing works

Evolving Intrinsic Motivations for Altruistic Behavior

Wang, J. X., Hughes, E., Fernando, C., Czarnecki, W. M., Duéñez‐Guzmán, E. A., & Leibo, J. Z. (2019) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/vtgr9509 · Cited by 51 · see citing works

Reinforcement learning, efficient coding, and the statistics of natural tasks

Botvinick, M., Weinstein, A., Solway, A., & Barto, A. G. (2015) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2015.08.009 · Cited by 50 · see citing works

Human Learning in Atari.

Tsividis, P. A., Pouncy, T., Xu, J. L., Tenenbaum, J. B., & Gershman, S. J. (2017) · Other univ. web domain · Cited by 44 · see citing works

Meta-learning by the baldwin effect

Fernando, C., Sygnowski, J., Osindero, S., Wang, J., Schaul, T., Teplyashin, D., Sprechmann, P., Pritzel, A., & Rusu, A. A. (2018) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3205651.3205763 · Cited by 25 · see citing works

Episodic Control as Meta-Reinforcement LearningPreprint

Ritter, S., Wang, J. S., Kurth-Nelson, Z. L., & Botvinick, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/360537 · Cited by 19 · see citing works

Meta-learning by the Baldwin effect

Fernando, C., Sygnowski, J., Osindero, S., Wang, J., Schaul, T., Teplyashin, D., Sprechmann, P., Pritzel, A., & Rusu, A. A. (2018) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3205651.3208249 · Cited by 18 · see citing works

Untitled work

(2018) · doi:10.4230/lipics.cp.2025.27

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0520-2 · Cited by 1,027 · see citing works

Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Alber, M., Tepole, A. B., Cannon, W. R., De, S., Durá-Bernal, S., Garikipati, K., Karniadakis, G. E., Lytton, W. W., Perdikaris, P. G., Petzold, L. R., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0193-y · Cited by 643 · see citing works

Artificial intelligence in recommender systems

Zhang, Q., Lu, J., & Jin, Y. (2020) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00212-w · Cited by 403 · see citing works

A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework

Kasongo, S. M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.12.010 · Cited by 462 · see citing works

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Benti, N. E., Chaka, M. D., & Semie, A. G. (2023) · Sustainability · doi:10.3390/su15097087 · Cited by 445 · see citing works

Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning

Wang, J., Hu, J. W., Min, G., Zomaya, A. Y., & Georgalas, N. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3014896 · Cited by 420 · see citing works

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387 · see citing works

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng, G. C. Y., Alber, M., Tepole, A. B., Cannon, W. R., De, S., Durá-Bernal, S., Garikipati, K., Karniadakis, G. E., Lytton, W. W., Perdikaris, P. G., et al. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09405-5 · Cited by 358 · see citing works

A survey on data‐efficient algorithms in big data era

Adadi, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00419-9 · Cited by 331 · see citing works

DMRO: A Deep Meta Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing

Qu, G., Wu, H., Li, R., & Jiao, P. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3087258 · Cited by 303 · see citing works

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

Abadal, S., Jain, A., Guirado, R., López-Alonso, J., & Alarcón, E. (2021) · ACM Computing Surveys · doi:10.1145/3477141 · Cited by 293 · see citing works

Deep reinforcement learning: a survey

Wang, H.-N., Liu, N., Zhang, Y., Feng, D., Huang, F., Li, D., & Zhang, Y. (2020) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900533 · Cited by 286 · see citing works

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269 · see citing works

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem, M., Rizvi, S. T. H., & Coronato, A. (2020) · IEEE Access · doi:10.1109/access.2020.3038605 · Cited by 264 · see citing works

Scope of machine learning in materials research—A review

Mobarak, M. H., Mimona, M. A., Islam, M. A., Hossain, N., Zohura, F. T., Imtiaz, I., & Rimon, M. I. H. (2023) · Applied Surface Science Advances · doi:10.1016/j.apsadv.2023.100523 · Cited by 243 · see citing works

Deep Reinforcement Learning for Traffic Signal Control: A Review

Rasheed, F., Yau, K. A., Noor, R. M., Wu, C., & Low, Y.-C. (2020) · IEEE Access · doi:10.1109/access.2020.3034141 · Cited by 181 · see citing works

Next-generation deep learning based on simulators and synthetic data

de Melo, C. M., Torralba, A. B., Guibas, L. J., DiCarlo, J. J., Chellappa, R., & Hodgins, J. K. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.11.008 · Cited by 173 · see citing works

Meta-learning in natural and artificial intelligence

Wang, J. X. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.01.002 · Cited by 171 · see citing works

A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment

Talaat, F. M., Saraya, M. S., Saleh, A. I., Ali, H. A., & Ali, S. H. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01768-8 · Cited by 158 · see citing works

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Li, B., Yang, Z.-P., Chen, D., Liang, S., & Ma, H. (2020) · Defence Technology · doi:10.1016/j.dt.2020.11.014 · Cited by 157 · see citing works

Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision

Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116512 · Cited by 157 · see citing works

Premembering Experience: A Hierarchy of Time-Scales for Proactive Attention

Nobre, A. C., & Stokes, M. G. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.030 · Cited by 143 · see citing works

Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues

Tran‐Dang, H., Bhardwaj, S., Rahim, T., Musaddiq, A., & Kim, D. (2022) · Journal of Communications and Networks · doi:10.23919/jcn.2021.000041 · Cited by 134 · see citing works

The computational roots of positivity and confirmation biases in reinforcement learning

Palminteri, S., & Lebreton, M. P. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.04.005 · Cited by 127 · see citing works

Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles

Liu, Y., Zhou, J., Tian, D., Sheng, Z., Duan, X., Qu, G., & Leung, V. C. M. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3082539 · Cited by 124 · see citing works

Deep learning in electrical utility industry: A comprehensive review of a decade of research

Mishra, M., Nayak, J., Naik, B., & Abraham, A. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.104000 · Cited by 123 · see citing works

Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review

Chen, Q., & Folly, K. A. (2022) · Energies · doi:10.3390/en16010146 · Cited by 116 · see citing works

A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings

Lei, Y., Zhan, S., Ono, E., Peng, Y., Zhang, Z., Hasama, T., & Chong, A. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119742 · Cited by 114 · see citing works

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

Zhang, W., Valencia, A., & Chang, N. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3106777 · Cited by 112 · see citing works

Artificial intelligence (AI) in textile industry operational modernization

Sikka, M. P., Sarkar, A., & Garg, S. (2022) · Research Journal of Textile and Apparel · doi:10.1108/rjta-04-2021-0046 · Cited by 111 · see citing works

Combined mechanistic modeling and machine-learning approaches in systems biology – A systematic literature review

Procopio, A., Cesarelli, G., Donisi, L., Merola, A., Amato, F., & Cosentino, C. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107681 · Cited by 111 · see citing works

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid, A. Y., Majid, A. Y., Saaybi, S., van Rietbergen, T., François-Lavet, V., Prasad, R. R. V., & Verhoeven, C. J. M. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3264540 · Cited by 108 · see citing works

Deep learning in electron microscopy

Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 105 · see citing works

A Systematic Review of Robotic Rehabilitation for Cognitive Training

Yuan, F., Klavon, E., Liu, Z., Lopez, R. P., & Zhao, X. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.605715 · Cited by 105 · see citing works

Asynchronous Multithreading Reinforcement-Learning-Based Path Planning and Tracking for Unmanned Underwater Vehicle

He, Z., Dong, L., Sun, C., & Wang, J. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3050960 · Cited by 102 · see citing works

The neuroscience of active learning and direct instruction

Dubinsky, J. M., & Hamid, A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105737 · Cited by 101 · see citing works

Attention for action in visual working memory

Olivers, C. N. L., & Roelfsema, P. R. (2020) · Cortex · doi:10.1016/j.cortex.2020.07.011 · Cited by 99 · see citing works

Deliberating trade-offs with the future

Bulley, A., & Schacter, D. L. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0834-9 · Cited by 97 · see citing works

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente, J., António, J., Mora, C., & Jardim, S. (2023) · Journal of Imaging · doi:10.3390/jimaging9100207 · Cited by 95 · see citing works

How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach

Singh, V. K., Nanavati, B., Kar, A. K., & Gupta, A. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10314-0 · Cited by 92 · see citing works

AI Applications to Reduce Loneliness Among Older Adults: A Systematic Review of Effectiveness and Technologies

Yang, Y., Wang, C., Xiang, X., & An, R. (2025) · Healthcare · doi:10.3390/healthcare13050446 · Cited by 89 · see citing works

How Outcome Uncertainty Mediates Attention, Learning, and Decision-Making

Monosov, I. E. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.009 · Cited by 87 · see citing works

Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system

Talaat, F. M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12223-5 · Cited by 87 · see citing works

The Best Laid Plans: Computational Principles of Anterior Cingulate Cortex

Holroyd, C. B., & Verguts, T. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.01.008 · Cited by 84 · see citing works

Meta-learning, social cognition and consciousness in brains and machines

Langdon, A., Botvinick, M., Nakahara, H., Tanaka, K., Matsumoto, M., & Kanai, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.10.004 · Cited by 84 · see citing works

From Machine Learning to Robotics: Challenges and Opportunities for Embodied IntelligencePreprint

Roy, N., Posner, I., Barfoot, T., Beaudoin, P., Bengio, Y., Bohg, J., Brock, O., Depatie, I., Fox, D., Koditschek, D. E., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15245 · Cited by 83 · see citing works

A novel reinforcement learning method for improving occupant comfort via window opening and closing

Han, M., May, R., Zhang, X., Wang, X., Pan, S., Yan, D., & Jin, Y. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102247 · Cited by 82 · see citing works

Environmental drivers of systematicity and generalization in a situated agent

Hill, F., Lampinen, A., Schneider, R., Clark, S., Botvinick, M., McClelland, J. L., & Santoro, A. (2019) · arXiv · doi:10.48550/arxiv.1910.00571 · Cited by 81 · see citing works

Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

Stergiou, C. L., & Psannis, K. E. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.05.003 · Cited by 80 · see citing works

Joint computation offloading and resource provisioning for e dge‐cloud computing environment: A machine learning‐based approach

Shahidinejad, A., & Ghobaei‐Arani, M. (2020) · Software Practice and Experience · doi:10.1002/spe.2888 · Cited by 79 · see citing works

Machine Learning for Smart and Energy-Efficient Buildings

Das, H. P., Lin, Y.-W., Agwan, U., Spangher, L., Devonport, A., Yang, Y., Drgoňa, J., Chong, A., Schiavon, S., & Spanos, C. J. (2024) · Environmental Data Science · doi:10.1017/eds.2023.43 · Cited by 78 · see citing works

A scoping review of methodologies for applying artificial intelligence to physical activity interventions

An, R., Shen, J., Wang, J., & Yang, Y. (2023) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2023.09.010 · Cited by 78 · see citing works

Capturing the objects of vision with neural networks

Peters, B., & Kriegeskorte, N. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01194-6 · Cited by 77 · see citing works

A neural network model of when to retrieve and encode episodic memories

Lu, Q., Hasson, U., & Norman, K. A. (2022) · eLife · doi:10.7554/elife.74445 · Cited by 77 · see citing works

Using Artificial Intelligence for Space Challenges: A Survey

Russo, A., & Lax, G. (2022) · Applied Sciences · doi:10.3390/app12105106 · Cited by 77 · see citing works

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00136 · Cited by 76 · see citing works

Optimizing agent behavior over long time scales by transporting value

Hung, C.-C., Lillicrap, T., Abramson, J., Wu, Y., Mirza, M., Carnevale, F., Ahuja, A., & Wayne, G. (2019) · Nature Communications · doi:10.1038/s41467-019-13073-w · Cited by 75 · see citing works

A deep Q-learning portfolio management framework for the cryptocurrency market

Lucarelli, G., & Borrotti, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05359-8 · Cited by 75 · see citing works

Prioritized experience replays on a hippocampal predictive map for learning

Igata, H., Ikegaya, Y., & Sasaki, T. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011266118 · Cited by 75 · see citing works

Wind farm control technologies: from classical control to reinforcement learning

Dong, H., Xie, J., & Zhao, X. (2022) · Progress in Energy · doi:10.1088/2516-1083/ac6cc1 · Cited by 75 · see citing works

Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments

Cross, L., Cockburn, J., Yue, Y., & O’Doherty, J. P. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.021 · Cited by 73 · see citing works

How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective

Yoo, A. H., & Collins, A. G. E. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01808 · Cited by 73 · see citing works

Hydrogen reduction-based energy management strategy of hybrid fuel cell/PV/battery/supercapacitor renewable energy system

Rezk, H., & Fathy, A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111316 · Cited by 72 · see citing works

Meta-reinforcement learning via orbitofrontal cortex

Hattori, R., Hedrick, N. G., Jain, A., Chen, S., You, H., Hattori, M., Choi, J., Lim, B. K., Yasuda, R., & Komiyama, T. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01485-3 · Cited by 70 · see citing works

Distributional Reinforcement Learning in the Brain

Lowet, A. S., Qiao, Z., Matias, S., Drugowitsch, J., & Uchida, N. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.004 · Cited by 69 · see citing works

What is new with Artificial Intelligence? Human–agent interactions through the lens of social agency

Pagliari, M., Chambon, V., & Berberian, B. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.954444 · Cited by 68 · see citing works

Memory as a Computational Resource

Dasgupta, I., & Gershman, S. J. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.12.008 · Cited by 67 · see citing works

Foundations of Erobotics

Dubé, S., & Anctil, D. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-020-00706-0 · Cited by 66 · see citing works

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

Huang, R., Chen, Y., Yin, T., Huang, Q., Tan, J., Yu, W., Li, X., Li, A., & Du, Y. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3155117 · Cited by 65 · see citing works

Discovering cognitive strategies with tiny recurrent neural networks

Ji-An, L., Benna, M. K., & Mattar, M. G. (2025) · Nature · doi:10.1038/s41586-025-09142-4 · Cited by 64 · see citing works

Human representation learning

Radulescu, A., Shin, Y. S., & Niv, Y. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092920-120559 · Cited by 63 · see citing works

Reward Prediction Error and Declarative Memory

Ergo, K., Loof, E. D., & Verguts, T. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.02.009 · Cited by 63 · see citing works

Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems

Sharma, N., Ghosh, A., Misra, R., & Das, S. K. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3264901 · Cited by 62 · see citing works

Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review

Yassir, A., Andaloussi, S. J., Ouchetto, O., Mamza, K., & Serghini, M. (2023) · Fisheries Research · doi:10.1016/j.fishres.2023.106790 · Cited by 62 · see citing works

Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology

Jiang, Y., Wang, C., & Zhou, S. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.005 · Cited by 62 · see citing works

Emergent behaviour and neural dynamics in artificial agents tracking odour plumes

Singh, S. H., van Breugel, F., Rao, R. P. N., & Brunton, B. W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-022-00599-w · Cited by 61 · see citing works

The role of executive function in shaping reinforcement learning

Rmus, M., McDougle, S. D., & Collins, A. G. E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.10.003 · Cited by 61 · see citing works

A Predictive Processing Model of Episodic Memory and Time Perception

Fountas, Z., Sylaidi, A., Nikiforou, K., Seth, A. K., Shanahan, M., & Roseboom, W. (2022) · Neural Computation · doi:10.1162/neco_a_01514 · Cited by 61 · see citing works

The evolving view of replay and its functions in wake and sleep

Findlay, G., Tononi, G., & Cirelli, C. (2020) · SLEEP Advances · doi:10.1093/sleepadvances/zpab002 · Cited by 61 · see citing works

Intelligent routing method based on Dueling DQN reinforcement learning and network traffic state prediction in SDN

Huang, L., Ye, M., Xue, X., Wang, Y., Qiu, H., & Deng, X. (2022) · Wireless Networks · doi:10.1007/s11276-022-03066-x · Cited by 60 · see citing works

Data dissemination for Internet of vehicle based on 5G communications: A survey

Benalia, E., Bitam, S., & Mellouk, A. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3881 · Cited by 58 · see citing works

Reinforcement learning and its connections with neuroscience and psychology

Subramanian, A., Chitlangia, S., & Baths, V. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.10.003 · Cited by 57 · see citing works

A cortical circuit mechanism for structural knowledge-based flexible sensorimotor decision-making

Liu, Y., Xin, Y., & Xu, N.-L. (2021) · Neuron · doi:10.1016/j.neuron.2021.04.014 · Cited by 57 · see citing works

AI Advancements: Comparison of Innovative Techniques

Taherdoost, H., & Madanchian, M. (2023) · AI · doi:10.3390/ai5010003 · Cited by 57 · see citing works

Machine learning/artificial intelligence in sports medicine: state of the art and future directions

Pareek, A., Ro, D. H., Karlsson, J., & Martin, R. K. (2024) · Journal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine · doi:10.1016/j.jisako.2024.01.013 · Cited by 57 · see citing works

The neural basis of consciousness

Frith, C. (2019) · Psychological Medicine · doi:10.1017/s0033291719002204 · Cited by 57 · see citing works

RALF: an adaptive reinforcement learning framework for teaching dyslexic students

Minoofam, S. A. H., Bastanfard, A., & Keyvanpour, M. R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11806-y · Cited by 55 · see citing works

An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Wan, K., Wu, D., Zhai, Y., Li, B., Gao, X., & Hu, Z. (2021) · Entropy · doi:10.3390/e23111433 · Cited by 54 · see citing works

How to study the neural mechanisms of multiple tasks

Yang, G. R., Cole, M. W., & Rajan, K. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.07.001 · Cited by 54 · see citing works

Building compositional tasks with shared neural subspaces

Tafazoli, S., Bouchacourt, F., Ardalan, A., Markov, N. T., Uchimura, M., Mattar, M. G., Daw, N. D., & Buschman, T. J. (2025) · Nature · doi:10.1038/s41586-025-09805-2 · Cited by 53 · see citing works

A model-based reinforcement learning approach for maintenance optimization of degrading systems in a large state space

Zhang, P., Zhu, X., & Xie, M. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107622 · Cited by 52 · see citing works

A neural circuit architecture for rapid learning in goal-directed navigation

Dan, C., Hulse, B. K., Kappagantula, R., Jayaraman, V., & Hermundstad, A. M. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.036 · Cited by 51 · see citing works

Cache-Assisted Collaborative Task Offloading and Resource Allocation Strategy: A Metareinforcement Learning Approach

Chen, S., Rui, L., Gao, Z., Li, W., & Qiu, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3168885 · Cited by 51 · see citing works

Functions and applications of artificial intelligence in droplet microfluidics

Liu, H., Nan, L., Chen, F., Zhao, Y., & Zhao, Y. (2023) · Lab on a Chip · doi:10.1039/d3lc00224a · Cited by 51 · see citing works

Building Human-Like Artificial Agents: A General Cognitive Algorithm for Emulating Human Decision-Making in Dynamic Environments

González, C. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231196766 · Cited by 51 · see citing works

Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training

Sharma, P. K., Zaroukian, E. G., Fernandez, R., Basak, A., & Asher, D. E. (2021) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2585808 · Cited by 51 · see citing works

How much intelligence is there in artificial intelligence? A 2020 update

van der Maas, H. L. J., Snoek, L., & Stevenson, C. E. (2021) · Intelligence · doi:10.1016/j.intell.2021.101548 · Cited by 50 · see citing works

Image quality assessment for machine learning tasks using meta-reinforcement learning

Saeed, S. U., Fu, Y., Stavrinides, V., Baum, Z. M. C., Yang, Q., Rusu, M., Fan, R. E., Sonn, G. A., Noble, J. A., Barratt, D. C., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102427 · Cited by 50 · see citing works

Large sequence models for sequential decision-making: a survey

Wen, M., Lin, R., Wang, H., Yang, Y., Wen, Y., Mai, L., Wang, Y.-X., Zhang, H., & Zhang, W. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-023-2689-5 · Cited by 50 · see citing works

The Neuroscience of Spatial Navigation and the Relationship to Artificial Intelligence

Bermudez-Contreras, E. J., Clark, B. J., & Wilber, A. A. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00063 · Cited by 50 · see citing works

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269 · see citing works

Understanding efficient reinforcement learning in humans and machines

Blakeman, S. (2021) · BIROn (Birkbeck, University of London) · doi:10.18743/pub.00046202

Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture

Mitsopoulos, K., Somers, S., Schooler, J. N., Lebiere, C. J., Pirolli, P. L. T., & Thomson, R. (2021) · Topics in Cognitive Science · doi:10.1111/tops.12573 · Cited by 17 · see citing works

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

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