Relevant bibliographies by topics / Reinforcement Motor Learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Reinforcement Motor Learning'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Reinforcement Motor Learning"

1

Vassiliadis, Pierre, Gerard Derosiere, Cecile Dubuc, et al. "Reward boosts reinforcement-based motor learning." iScience 24, no. 7 (2021): 102821. http://dx.doi.org/10.1016/j.isci.2021.102821.

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Uehara, Shintaro, Firas Mawase, Amanda S. Therrien, Kendra M. Cherry-Allen, and Pablo Celnik. "Interactions between motor exploration and reinforcement learning." Journal of Neurophysiology 122, no. 2 (2019): 797–808. http://dx.doi.org/10.1152/jn.00390.2018.

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Motor exploration, a trial-and-error process in search for better motor outcomes, is known to serve a critical role in motor learning. This is particularly relevant during reinforcement learning, where actions leading to a successful outcome are reinforced while unsuccessful actions are avoided. Although early on motor exploration is beneficial to finding the correct solution, maintaining high levels of exploration later in the learning process might be deleterious. Whether and how the level of exploration changes over the course of reinforcement learning, however, remains poorly understood. H
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Palidis, Dimitrios J., Heather R. McGregor, Andrew Vo, Penny A. MacDonald, and Paul L. Gribble. "Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference." Journal of Neurophysiology 126, no. 1 (2021): 47–67. http://dx.doi.org/10.1152/jn.00696.2020.

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Motor adaptation relies on multiple processes including reinforcement of successful actions. Cognitive reinforcement learning is impaired by levodopa-induced disruption of dopamine function. We administered levodopa to healthy adults who participated in multiple motor adaptation tasks. We found no effects of levodopa on any component of motor adaptation. This suggests that motor adaptation may not depend on the same dopaminergic mechanisms as cognitive forms or reinforcement learning that have been shown to be impaired by levodopa.
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Sistani, Mohammad Bagher Naghibi, and Sadegh Hesari. "Decreasing Induction Motor Loss Using Reinforcement Learning." Journal of Automation and Control Engineering 3, no. 6 (2015): 13–17. http://dx.doi.org/10.12720/joace.4.1.13-17.

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IZAWA, Jun, Toshiyuki KONDO, and Koji ITO. "Motor Learning Model through Reinforcement Learning with Neural Internal Model." Transactions of the Society of Instrument and Control Engineers 39, no. 7 (2003): 679–87. http://dx.doi.org/10.9746/sicetr1965.39.679.

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Palidis, Dimitrios J., Heather R. McGregor, Andrew Vo, Penny A. MacDonald, and Paul L. Gribble. "Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference." Journal of Neurophysiology 126, no. 1 (2021): 47–67. https://doi.org/10.5281/zenodo.7641270.

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Motor adaptation relies on multiple processes including reinforcement of successful actions. Cognitive reinforcement learning is impaired by levodopa-induced disruption of dopamine function. We administered levodopa to healthy adults who participated in multiple motor adaptation tasks. We found no effects of levodopa on any component of motor adaptation. This suggests that motor adaptation may not depend on the same dopaminergic mechanisms as cognitive forms or reinforcement learning that have been shown to be impaired by levodopa. © American Physiological Society. Deposited by shareyourpaper.
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Peters, Jan, and Stefan Schaal. "Reinforcement learning of motor skills with policy gradients." Neural Networks 21, no. 4 (2008): 682–97. http://dx.doi.org/10.1016/j.neunet.2008.02.003.

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Cai, Yuhuan, Liye Zhao, Xingyu Chen, and Zhenjun Li. "Deep Reinforcement Learning-Based Motion Control Optimization for Defect Detection System." Actuators 14, no. 4 (2025): 180. https://doi.org/10.3390/act14040180.

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The X-ray defect detection system for weld seams in deep-sea manned spherical shells is nonlinear and complex, posing challenges such as motor parameter variations, external disturbances, coupling effects, and high-precision dual-motor coordination requirements. To address these challenges, this study proposes a deep reinforcement learning-based control scheme, leveraging DRL’s capabilities to optimize system performance. Specifically, the TD3 algorithm, featuring a dual-critic structure, is employed to enhance control precision within predefined state and action spaces. A composite reward mec
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Tian, Mengqi, Ke Wang, Hongyu Lv, and Wubin Shi. "Reinforcement learning control method of torque stability of three-phase permanent magnet synchronous motor." Journal of Physics: Conference Series 2183, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1742-6596/2183/1/012024.

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Abstract Regarding the control strategy of the permanent magnet synchronous motor, the field-oriented control based on the PI controller have the instability of the output torque. In order to stabilize the output torque of the permanent magnet synchronous motor, this paper adopts reinforcement learning to improve traditional PI controller. Finally, in the MATLAB/Simulink simulation environment, a new control method based on reinforcement learning is established. The simulation results show that the reinforcement learning control method used in this paper can improve the stability of the output
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Babič, Jan, Tjasa Kunavar, Erhan Oztop, and Mitsuo Kawato. "Success-efficient/failure-safe strategy for hierarchical reinforcement motor learning." PLOS Computational Biology 21, no. 5 (2025): e1013089. https://doi.org/10.1371/journal.pcbi.1013089.

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Our study explores how ecological aspects of motor learning enhance survival by improving movement efficiency and mitigating injury risks during task failures. Traditional motor control theories mainly address isolated body movements and often overlook these ecological factors. We introduce a novel computational motor control approach, incorporating ecological fitness and a strategy that alternates between success-driven movement efficiency and failure-driven safety, akin to win-stay/lose-shift tactics. In our experiments, participants performed squat-to-stand movements under novel force pertu
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Dissertations / Theses on the topic "Reinforcement Motor Learning"

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Zhang, Fangyi. "Learning real-world visuo-motor policies from simulation." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121471/1/Fangyi%20Zhang%20Thesis.pdf.

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This thesis explores how simulation can be used to create the large amount of data required to teach a robot certain hand-eye coordination skills. It advances the state-of-the-art of deep visuo-motor policy learning by introducing a new modular architecture, a novel reinforcement learning exploration strategy, and adversarial discriminative transfer.
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De, La Bourdonnaye François. "Learning sensori-motor mappings using little knowledge : application to manipulation robotics." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC037/document.

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La thèse consiste en l'apprentissage d'une tâche complexe de robotique de manipulation en utilisant très peu d'aprioris. Plus précisément, la tâche apprise consiste à atteindre un objet avec un robot série. L'objectif est de réaliser cet apprentissage sans paramètres de calibrage des caméras, modèles géométriques directs, descripteurs faits à la main ou des démonstrations d'expert. L'apprentissage par renforcement profond est une classe d'algorithmes particulièrement intéressante dans cette optique. En effet, l'apprentissage par renforcement permet d’apprendre une compétence sensori-motrice en
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Wang, Jiexin. "Policy Hyperparameter Exploration for Behavioral Learning of Smartphone Robots." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225744.

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Frömer, Romy. "Learning to throw." Doctoral thesis, Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17427.

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Feedback, Trainingsplan und individuelle Unterschiede zwischen Lernern sind drei Faktoren die den motorischen Fertigkeitserwerb beeinflussen und wurden in der vorliegenden Dissertation untersucht. Ein besonderer Fokus lag auf den zugrundeliegenden Gehirnprozessen von Feedbackverarbeitung und Handlungsvorbereitung, die mittels ereigniskorrelierter Potenziale (EKPs) untersucht wurden. 120 Teilnehmer trainierten auf virtuelle Zielscheiben zu werfen und wurden in einer Folgesitzung auf Abruf und Transfer getestet. Der Trainingsplan verursachte entweder hohe contextual interference (CI) (randomisie
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PAQUIER, Williams. "Apprentissage ouvert de representations et de fonctionnalites en robotique : anayse, modeles et implementation." Phd thesis, Université Paul Sabatier - Toulouse III, 2004. http://tel.archives-ouvertes.fr/tel-00009324.

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L'acquisition autonome de representations et de fonctionnalites en robotique pose de nombreux problemes theoriques. Aujourd'hui, les systemes robotiques autonomes sont concus autour d'un ensemble de fonctionnalites. Leurs representations du monde sont issues de l'analyse d'un probleme et d'une modelisation prealablement donnees par les concepteurs. Cette approche limite les capacites d'apprentissage. Nous proposons dans cette these un systeme ouvert de representations et de fonctionnalites. Ce systeme apprend en experimentant son environnement et est guide par l'augmentation d'une fonction de
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Trska, Robert. "Motor expectancy: the modulation of the reward positivity in a reinforcement learning motor task." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/9992.

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An adage posits that we learn from our mistakes; however, this is not entirely true. According to reinforcement learning theory, we learn when the expectation of our actions differs from outcomes. Here, we examined whether expectancy driven learning lends a role in motor learning. Given the vast amount of overlapping anatomy and circuitry within the brain with respect to reward and motor processes, it is appropriate to examine both motor control and expectancy processes within a singular task. In the current study, participants performed a line drawing task via tablet under conditions of chang
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Sendhilnathan, Naveen. "The role of the cerebellum in reinforcement learning." Thesis, 2021. https://doi.org/10.7916/d8-p13c-3955.

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How do we learn to establish associations between arbitrary visual cues (like a red light) and movements (like braking the car)? We investigated the neural correlates of visuomotor association learning in the mid-lateral cerebellum. Although cerebellum has been considered to be a motor control center involved in monitoring and correcting the motor error through supervised learning, in this thesis, we show that its role can also be extended to non-motor learning. Specifically, when primates learned to associate arbitrary visual cues with well-learned stereotypic movements, the simple spikes of
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Singh, Puneet. "The Role of Basal Ganglia and Redundancy in Supervised Motor Learning." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4176.

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Human sensorimotor control can achieve highly reliable movements under circumstances of noise, redundancy, uncertainty, and sensory delays. Our ability to achieve reliable and accurate movements is in the fact we have a nervous system that learns these limitations and continuously compensates for them. The purpose of the thesis is to understand brain mechanisms and computations underlying supervised motor learning, its interaction with reinforcement learning and study its relation to motor variability. To address these issues, we have investigated factors influencing supervised motor learning
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Krigolson, Olave. "Hierarchical error processing during motor control." Thesis, 2007. http://hdl.handle.net/1828/239.

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The successful execution of goal-directed movement requires the evaluation of many levels of errors. On one hand, the motor system needs to be able to evaluate ‘high-level’ errors indicating the success or failure of a given movement. On the other hand, as a movement is executed the motor system also has to be able to correct for ‘low-level’ errors - an error in the initial motor command or change in the motor command necessary to compensate for an unexpected change in the movement environment. The goal of the present research was to provide electroencephalographic evidence that error processi
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Books on the topic "Reinforcement Motor Learning"

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The contextual interference effect in learning an open motor skill. 1988.

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The contextual interference effect in learning an open motor skill. 1986.

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The effect of competitive anxiety and reinforcement on the performance of collegiate student-athletes. 1990.

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The effect of competitive anxiety and reinforcement on the performance of collegiate student-athletes. 1991.

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Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.

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This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant cond
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Effects of cognitive learning strategies and reinforcement on the acquisition of closed motor skills in older adults. 1991.

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Effects of cognitive learning strategies and reinforcement on the acquisition of closed motor skills in older adults. 1991.

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Effects of cognitive learning strategies and reinforcement on the acquisition of closed motor skills in older adults. 1990.

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Effects of cognitive learning strategies and reinforcement: On the acquisition of closed motor skills in older adults. 1991.

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Yun, Chi-Hong. Pre- and post-knowledge of results intervals and motor performance of mentally retarded individuals. 1989.

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Book chapters on the topic "Reinforcement Motor Learning"

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Mannes, Christian. "Learning Sensory-Motor Coordination by Experimentation and Reinforcement Learning." In Konnektionismus in Artificial Intelligence und Kognitionsforschung. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-76070-9_10.

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Manjunatha, Hemanth, and Ehsan T. Esfahani. "Application of Reinforcement and Deep Learning Techniques in Brain–Machine Interfaces." In Advances in Motor Neuroprostheses. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38740-2_1.

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Lohse, Keith, Matthew Miller, Mariane Bacelar, and Olav Krigolson. "Errors, rewards, and reinforcement in motor skill learning." In Skill Acquisition in Sport. Routledge, 2019. http://dx.doi.org/10.4324/9781351189750-3.

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Lane, Stephen H., David A. Handelman, and Jack J. Gelfand. "Modulation of Robotic Motor Synergies Using Reinforcement Learning Optimization." In Neural Networks in Robotics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_29.

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Kober, Jens, and Jan Peters. "Reinforcement Learning to Adjust Parametrized Motor Primitives to New Situations." In Springer Tracts in Advanced Robotics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03194-1_5.

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Kober, Jens, Betty Mohler, and Jan Peters. "Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05181-4_10.

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Patil, Gaurav, Patrick Nalepka, Lillian Rigoli, Rachel W. Kallen, and Michael J. Richardson. "Dynamical Perceptual-Motor Primitives for Better Deep Reinforcement Learning Agents." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85739-4_15.

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Carminatti, Laurène, Lucio Condro, Alexa Riehle, Sonja Grün, Thomas Brochier, and Emmanuel Daucé. "Non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71533-4_20.

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Coulom, Rémi. "Feedforward Neural Networks in Reinforcement Learning Applied to High-Dimensional Motor Control." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36169-3_32.

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Tran, Xuan Khanh, Duy Tung Le, Duc Thuan Tran, and Phuong Nam Dao. "A Reinforcement Learning Method for Control Scheme of Permanent Magnet Synchronous Motor." In Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50818-9_15.

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Conference papers on the topic "Reinforcement Motor Learning"

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Prestes, Gustavo X., William K. Moreira, Filipe P. Scalcon, Cassiano Rech, Andrew M. Knight, and Rodrigo P. Vieira. "Reinforcement Learning-Based Current Controller for Switched Reluctance Motor Drives." In 2025 IEEE International Electric Machines & Drives Conference (IEMDC). IEEE, 2025. https://doi.org/10.1109/iemdc60492.2025.11061049.

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Ahmed, Shoaib, Usman Tariq, Ammar Hasan, and Habibur Rehman. "A Deep Reinforcement Learning Paradigm for DC Motor Speed Control." In 2025 IEEE International Electric Machines & Drives Conference (IEMDC). IEEE, 2025. https://doi.org/10.1109/iemdc60492.2025.11060983.

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Yang, Fan, Yuening Huang, Chao Zhai, and Hehong Zhang. "Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space." In 2025 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2025. https://doi.org/10.1109/icit63637.2025.10965330.

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Chen, Meng, and Huihui Fu. "Permanent Magnet Synchronous Motor Speed Control by Attention Mechanism Reinforcement Learning." In 2024 13th International Conference of Information and Communication Technology (ICTech). IEEE, 2024. https://doi.org/10.1109/ictech63197.2024.00039.

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Shi, Haojie, Tingguang Li, Qingxu Zhu, Jiapeng Sheng, Lei Han, and Max Q. H. Meng. "An Efficient Model-Based Approach on Learning Agile Motor Skills without Reinforcement." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611560.

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Aung, Htoo Wai, Jiao Jiao Li, Yang An, and Steven W. Su. "Enhancing EEG Motor Imagery Time Point Signal Classification through Reinforcement Learning and Graph Neural Networks." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00024.

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Guo, Wencan, Yunjian Peng, and Jinkun Ma. "Reinforcement Learning-Based Nonlinear Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662044.

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Tazawa, Ryunosuke, Takuma Torii, and Shohei Hidaka. "Speed over Accuracy in Curriculum Learning of Throwing: Analysis with Deep Reinforcement Motor Learning for SingleLink Robot The Impact of Speed-first Curriculum Design." In 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2024. https://doi.org/10.1109/iciibms62405.2024.10792875.

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Lee, Jeonghan, and Jae Suk Lee. "Reinforcement learning-based development of time-optimal current trajectories for permanent magnet synchronous motor drives under voltage and current constraints." In 2024 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2024. https://doi.org/10.1109/ecce55643.2024.10861363.

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Omrani, Pouria, and Hamid Khaloozadeh. "A Comparative Study of LQT Controller Design Using Reinforcement Learning Methods: A Case Study on Speed Control of the PMDC Motor." In 2024 10th International Conference on Control, Instrumentation and Automation (ICCIA). IEEE, 2024. https://doi.org/10.1109/iccia65044.2024.10768119.

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