Meta-learning in Reinforcement Learning

Predictive reward signal of dopamine neurons

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Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle

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An opponent-process theory of motivation. I. Temporal dynamics of affect

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Reinforcement Learning in Continuous Time and Space

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Opponent interactions between serotonin and dopamine

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Metalearning and neuromodulation

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5-HT and motor control: a hypothesis

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On the Complexity of Solving Markov Decision ProblemsPreprint

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A stochastic reinforcement learning algorithm for learning real-valued functions

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The role of exploration in learning control

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Control of exploitation–exploration meta-parameter in reinforcement learning

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Adapting bias by gradient descent: an incremental version of delta-bar-delta

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D2‐like dopamine receptor activation excites rat dorsal raphe 5‐HT neurons in vitro

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Dopaminergic regulation of cortical acetylcholine release: Effects of dopamine receptor agonists

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Monoaminergic interaction in the central nervous system: A morphological analysis in the locus coeruleus of the rat

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A model of cerebellar metaplasticity.Preprint

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Meta-learning with memory-augmented neural networks

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Meta-Learning in Neural Networks: A Survey

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Deep Reinforcement Learning: A Survey

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Reinforcement learning, fast and slow

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Neuroeconomics: Decision Making and the Brain

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Learning to reinforcement learn

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Prefrontal cortex as a meta-reinforcement learning system

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One-shot Learning with Memory-Augmented Neural NetworksPreprint

Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06065 · Cited by 435 · see citing works

Medial and orbital frontal cortex in decision-making and flexible behavior

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Deep reinforcement learning in computer vision: a comprehensive survey

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Low-Serotonin Levels Increase Delayed Reward Discounting in Humans

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The Exploration-Exploitation Dilemma: A Multidisciplinary Framework

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Deep reinforcement learning in medical imaging: A literature review

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Dual Adaptation Supports a Parallel Architecture of Motor Memory

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A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales

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Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning

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Twenty-Five Lessons from Computational Neuromodulation

Dayan, P. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.027 · Cited by 190 · see citing works

Meta-learning in natural and artificial intelligence

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Emotion in reinforcement learning agents and robots: a survey

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Teaching recurrent neural networks to infer global temporal structure from local examples

Kim, J. Z., Lu, Z., Nozari, E., Pappas, G. J., & Bassett, D. S. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00321-2 · Cited by 143 · see citing works

Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review

Al Sayed, K., Boodi, A., Sadeghian Broujeny, R., & Beddiar, K. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110085 · Cited by 140 · see citing works

A Biophysically Based Neural Model of Matching Law Behavior: Melioration by Stochastic Synapses

Soltani, A., & Wang, X. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.5159-05.2006 · Cited by 127 · see citing works

Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner

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A Survey of Brain-Inspired Intelligent Robots: Integration of Vision, Decision, Motion Control, and Musculoskeletal Systems

Qiao, H., Chen, J., & Huang, X. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3071312 · Cited by 119 · see citing works

Federated Learning and Meta Learning: Approaches, Applications, and Directions

Liu, X., Deng, Y., Nallanathan, A., & Bennis, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3330910 · Cited by 116 · see citing works

Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty

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Adaptive properties of differential learning rates for positive and negative outcomes

Cazé, R. D., & van der Meer, M. A. A. (2013) · Biological Cybernetics · doi:10.1007/s00422-013-0571-5 · Cited by 100 · see citing works

Dopamine blockade impairs the exploration-exploitation trade-off in rats

Cinotti, F., Fresno, V., Aklil, N., Coutureau, É., Girard, B., Marchand, A. R., & Khamassi, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43245-z · Cited by 94 · see citing works

Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model

Khamassi, M., Lallée, S., Enel, P., Procyk, E., & Dominey, P. F. (2011) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2011.00001 · Cited by 96 · see citing works

Sex differences in learning from exploration

Chen, C. S., Knep, E., Han, A., Ebitz, R. B., & Grissom, N. M. (2021) · eLife · doi:10.7554/elife.69748 · Cited by 90 · see citing works

Behavioral Regulation and the Modulation of Information Coding in the Lateral Prefrontal and Cingulate Cortex

Khamassi, M., Quilodran, R., Enel, P., Dominey, P. F., & Procyk, E. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu114 · Cited by 83 · see citing works

Use It and Improve It or Lose It: Interactions between Arm Function and Use in Humans Post-stroke

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Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network

Kusy, M., & Zajdel, R. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2376703 · Cited by 86 · see citing works

Efficient reinforcement learning: computational theories, neuroscience and robotics

Kawato, M., & Samejima, K. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2007.03.004 · Cited by 80 · see citing works

The new robotics—towards human‐centered machines

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Humans Can Adopt Optimal Discounting Strategy under Real-Time Constraints

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Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

Gautam, M. (2023) · Electricity · doi:10.3390/electricity4040020 · Cited by 82 · see citing works

Neural mechanism for stochastic behaviour during a competitive game

Soltani, A., Lee, D., & Wang, X.-J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.044 · Cited by 77 · see citing works

Meta-seg: A survey of meta-learning for image segmentation

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A multifaceted vision of the human-AI collaboration: a comprehensive review

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The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning

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Meta-control: From psychology to computational neuroscience

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Meta-reinforcement learning via orbitofrontal cortex

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Cortical mechanisms for reinforcement learning in competitive games

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Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects

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Serotonin and the Evaluation of Future Rewards

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Reinforcement learning for the traveling salesman problem with refueling

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Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters

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An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

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DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning

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GASTO: A Fast Adaptive Graph Learning Framework for Edge Computing Empowered Task Offloading

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Affect, Anticipation, and Adaptation: Affect-Controlled Selection of Anticipatory Simulation in Artificial Adaptive Agents

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Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review

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Catecholaminergic modulation of meta-learning

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A reinforcement learning model of joy, distress, hope and fear

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Robot Fast Adaptation to Changes in Human Engagement During Simulated Dynamic Social Interaction With Active Exploration in Parameterized Reinforcement Learning

Khamassi, M., Velentzas, G., Tsitsimis, T., & Tzafestas, C. S. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2843122 · Cited by 43 · see citing works

Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification

Kusy, M., & Zajdel, R. (2014) · Applied Intelligence · doi:10.1007/s10489-014-0562-9 · Cited by 41 · see citing works

Meta-reinforcement learning for adaptive spacecraft guidance during finite-thrust rendezvous missions

Federici, L., Scorsoglio, A., Zavoli, A., & Furfaro, R. (2022) · Acta Astronautica · doi:10.1016/j.actaastro.2022.08.047 · Cited by 41 · see citing works

Adaptable automation with modular deep reinforcement learning and policy transfer

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Goals, usefulness and abstraction in value-based choice

Martino, B. D., & Cortese, A. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.11.001 · Cited by 41 · see citing works

Meta-learning within Projective Simulation

Makmal, A., Melnikov, A. A., Dunjko, V., & Briegel, H. J. (2016) · IEEE Access · doi:10.1109/access.2016.2556579 · Cited by 37 · see citing works

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models

Dağlarlı, E. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.92172 · Cited by 38 · see citing works

Individual variability in visual discrimination and reversal learning performance in common marmosets

Takemoto, A., Miwa, M., Koba, R., Yamaguchi, C., Suzuki, H., & Nakamura, K. (2014) · Neuroscience Research · doi:10.1016/j.neures.2014.10.001 · Cited by 34 · see citing works

Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization

Barsce, J. C., Palombarini, J. A., & Martínez, E. (2017) · Latin American Computer Conference (CLEI) · doi:10.1109/clei.2017.8226439 · Cited by 36 · see citing works

Tuning of reinforcement learning parameters applied to SOP using the Scott–Knott method

Ottoni, A. L. C., Nepomuceno, E. G., de Oliveira, M. S., & de Oliveira, D. C. R. (2019) · Soft Computing · doi:10.1007/s00500-019-04206-w · Cited by 34 · see citing works

Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems

Munir, M. S., Tran, N. H. K., Saad, W., & Hong, C. S. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3057960 · Cited by 33 · see citing works

A Comprehensive Overview and Survey of Recent Advances in Meta-LearningPreprint

Peng, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.11149 · Cited by 33 · see citing works

Learning environment-specific learning rates

Simoens, J., Verguts, T., & Braem, S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011978 · Cited by 31 · see citing works

MetaInjury: Meta-learning framework for reusing the risk knowledge of different construction accidents

Li, X., Zhu, R., Ye, H., Jiang, C., & Benslimane, A. (2021) · Safety Science · doi:10.1016/j.ssci.2021.105315 · Cited by 31 · see citing works

Active Exploration and Parameterized Reinforcement Learning Applied to a Simulated Human-Robot Interaction Task

Khamassi, M., Velentzas, G., Tsitsimis, T., & Tzafestas, C. S. (2017) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2017.33 · Cited by 29 · see citing works

Image-based Deep Reinforcement Learning for Autonomous Lunar Landing

Scorsoglio, A., Furfaro, R., Linares, R., & Gaudet, B. (2020) · AIAA Scitech 2020 Forum · doi:10.2514/6.2020-1910 · Cited by 31 · see citing works

An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization

Gao, M., Feng, X., Yu, H., & Li, X. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04574-9 · Cited by 28 · see citing works

Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging

Sun, Y., Pang, S., Zhao, Z., & Zhang, Y.-A. (2024) · Natural Resources Research · doi:10.1007/s11053-024-10396-4 · Cited by 28 · see citing works

Computational Modeling of Catecholamines Dysfunction in Alzheimer’s Disease at Pre-Plaque Stage

Caligiore, D., Silvetti, M., D’Amelio, M., Puglisi‐Allegra, S., & Baldassarre, G. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200276 · Cited by 28 · see citing works

MetaFollower: Adaptable personalized autonomous car following

Chen, X., Chen, K., Zhu, M., Yang, H., Shen, S., Wang, X., & Wang, Y. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104872 · Cited by 27 · see citing works

Relative salience signaling within a thalamo-orbitofrontal circuit governs learning rate

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Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance

Sugiyama, T., Schweighofer, N., & Izawa, J. (2023) · Nature Communications · doi:10.1038/s41467-023-39536-9 · Cited by 27 · see citing works

Strategies for Affect-Controlled Action-Selection in Soar-RL

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Intrinsic Motivation and Introspection in Reinforcement Learning

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Behavioral Game Theory and the Neural Basis of Strategic Choice

Camerer, C. F. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374176-9.00013-0 · Cited by 26 · see citing works

On Affect and Self-adaptation: Potential Benefits of Valence-Controlled Action-Selection

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Fast Task Adaptation Based on the Combination of Model-Based and Gradient-Based Meta Learning

Xu, Z., Chen, X., & Cao, L. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3028378 · Cited by 25 · see citing works

Analysis of Amino Acid Mixtures by Voltammetric Electronic Tongues and Artificial Neural Networks

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Best to go with what you know?

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C2S : Class-aware client selection for effective aggregation in federated learning

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Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

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Online meta-learning by parallel algorithm competition

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Robust interplanetary trajectory design under multiple uncertainties via meta-reinforcement learning

Federici, L., & Zavoli, A. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.10.018 · Cited by 21 · see citing works

A Response Surface Model Approach to Parameter Estimation of Reinforcement Learning for the Travelling Salesman Problem

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Meta-Reinforcement Learning for Spacecraft Proximity Operations Guidance and Control in Cislunar Space

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Brain-inspired meta-reinforcement learning cognitive control in conflictual inhibition decision-making task for artificial agents

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