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Journal articles on the topic 'Reinforcement Motor Learning'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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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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2

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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7

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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Uehara, Shintaro, Firas Mawase, and Pablo Celnik. "Learning Similar Actions by Reinforcement or Sensory-Prediction Errors Rely on Distinct Physiological Mechanisms." Cerebral Cortex 28, no. 10 (2017): 3478–90. http://dx.doi.org/10.1093/cercor/bhx214.

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Abstract Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participa

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Sidarta, Ananda, John Komar, and David J. Ostry. "Clustering analysis of movement kinematics in reinforcement learning." Journal of Neurophysiology 127, no. 2 (2022): 341–53. http://dx.doi.org/10.1152/jn.00229.2021.

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The choice of exploration versus exploitation is a fundamental problem in learning new motor skills through reinforcement. In this study, we employed a data-driven approach to characterize movements on a trial-by-trial basis with an unsupervised clustering algorithm. Using this technique, we found that changes in task demands and, in particular, in the required accuracy of movements, influenced the ratio of exploration to exploitation. This analysis framework provides an attractive tool to investigate mechanisms of explorative and exploitative behavior while studying motor learning.

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13

Pyle, Ryan, and Robert Rosenbaum. "A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity." Neural Computation 31, no. 7 (2019): 1430–61. http://dx.doi.org/10.1162/neco_a_01198.

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Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopa

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Sidarta, Ananda, Floris T. van Vugt, and David J. Ostry. "Somatosensory working memory in human reinforcement-based motor learning." Journal of Neurophysiology 120, no. 6 (2018): 3275–86. http://dx.doi.org/10.1152/jn.00442.2018.

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Recent studies using visuomotor adaptation and sequence learning tasks have assessed the involvement of working memory in the visuospatial domain. The capacity to maintain previously performed movements in working memory is perhaps even more important in reinforcement-based learning to repeat accurate movements and avoid mistakes. Using this kind of task in the present work, we tested the relationship between somatosensory working memory and motor learning. The first experiment involved separate memory and motor learning tasks. In the memory task, the participant’s arm was displaced in differe

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15

Gao, Depeng, Shuai Wang, Yuwei Yang, et al. "An Intelligent Control Method for Servo Motor Based on Reinforcement Learning." Algorithms 17, no. 1 (2023): 14. http://dx.doi.org/10.3390/a17010014.

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Servo motors play an important role in automation equipment and have been used in several manufacturing fields. However, the commonly used control methods need their parameters to be set manually, which is rather difficult, and this means that these methods generally cannot adapt to changes in operation conditions. Therefore, in this study, we propose an intelligent control method for a servo motor based on reinforcement learning and that can train an agent to produce a duty cycle according to the servo error between the current state and the target speed or torque. The proposed method can adj

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16

Yu, Wanming, Chuanyu Yang, Christopher McGreavy, et al. "Identifying important sensory feedback for learning locomotion skills." Nature Machine Intelligence 5, no. 8 (2023): 919–32. http://dx.doi.org/10.1038/s42256-023-00701-w.

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AbstractRobot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state–action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neural networks, human insights and engineering efforts are often required to select appropriate states through qualitative approaches, such as ablation studies, without a quantitative analysis of the state importance. Here we present a systematic saliency analysis that quantitatively evaluates the relative importance of different feedba

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17

Holland, Peter, Olivier Codol, and Joseph M. Galea. "Contribution of explicit processes to reinforcement-based motor learning." Journal of Neurophysiology 119, no. 6 (2018): 2241–55. http://dx.doi.org/10.1152/jn.00901.2017.

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Despite increasing interest in the role of reward in motor learning, the underlying mechanisms remain ill defined. In particular, the contribution of explicit processes to reward-based motor learning is unclear. To address this, we examined subjects’ ( n = 30) ability to learn to compensate for a gradually introduced 25° visuomotor rotation with only reward-based feedback (binary success/failure). Only two-thirds of subjects ( n = 20) were successful at the maximum angle. The remaining subjects initially followed the rotation but after a variable number of trials began to reach at an insuffici

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18

Warlaumont, Anne S., Gert Westermann, Eugene H. Buder, and D. Kimbrough Oller. "Prespeech motor learning in a neural network using reinforcement." Neural Networks 38 (February 2013): 64–75. http://dx.doi.org/10.1016/j.neunet.2012.11.012.

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19

Therrien, Amanda S., Daniel M. Wolpert, and Amy J. Bastian. "Increasing Motor Noise Impairs Reinforcement Learning in Healthy Individuals." eneuro 5, no. 3 (2018): ENEURO.0050–18.2018. http://dx.doi.org/10.1523/eneuro.0050-18.2018.

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20

Hahnloser, Richard, and Anja Zai. "A computational view on motor exploration during reinforcement learning." IBRO Reports 6 (September 2019): S50. http://dx.doi.org/10.1016/j.ibror.2019.07.155.

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21

Paul, T., V. M. Wiemer, S. T. Grafton, G. R. Fink, and L. J. Volz. "Reinforcement feedback modulates motor adaptation learning in acute stroke." Clinical Neurophysiology 159 (March 2024): e23. http://dx.doi.org/10.1016/j.clinph.2023.12.061.

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22

Yin, Fengyuan, Xiaoming Yuan, Zhiao Ma, and Xinyu Xu. "Vector Control of PMSM Using TD3 Reinforcement Learning Algorithm." Algorithms 16, no. 9 (2023): 404. http://dx.doi.org/10.3390/a16090404.

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Permanent magnet synchronous motor (PMSM) drive systems are commonly utilized in mobile electric drive systems due to their high efficiency, high power density, and low maintenance cost. To reduce the tracking error of the permanent magnet synchronous motor, a reinforcement learning (RL) control algorithm based on double delay deterministic gradient algorithm (TD3) is proposed. The physical modeling of PMSM is carried out in Simulink, and the current controller controlling id-axis and iq-axis in the current loop is replaced by a reinforcement learning controller. The optimal control network pa

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23

McDougle, Samuel D., Matthew J. Boggess, Matthew J. Crossley, Darius Parvin, Richard B. Ivry, and Jordan A. Taylor. "Credit assignment in movement-dependent reinforcement learning." Proceedings of the National Academy of Sciences 113, no. 24 (2016): 6797–802. http://dx.doi.org/10.1073/pnas.1523669113.

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When a person fails to obtain an expected reward from an object in the environment, they face a credit assignment problem: Did the absence of reward reflect an extrinsic property of the environment or an intrinsic error in motor execution? To explore this problem, we modified a popular decision-making task used in studies of reinforcement learning, the two-armed bandit task. We compared a version in which choices were indicated by key presses, the standard response in such tasks, to a version in which the choices were indicated by reaching movements, which affords execution failures. In the ke

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Maia, T. "A Reinforcement-learning Account of Tourette Syndrome." European Psychiatry 41, S1 (2017): S10. http://dx.doi.org/10.1016/j.eurpsy.2017.01.083.

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BackgroundTourette syndrome (TS) has long been thought to involve dopaminergic disturbances, given the effectiveness of antipsychotics in diminishing tics. Molecular-imaging studies have, by and large, confirmed that there are specific alterations in the dopaminergic system in TS. In parallel, multiple lines of evidence have implicated the motor cortico-basal ganglia-thalamo-cortical (CBGTC) loop in TS. Finally, several studies demonstrate that patients with TS exhibit exaggerated habit learning. This talk will present a computational theory of TS that ties together these multiple findings.Met

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25

Cho, Nam Jun, Sang Hyoung Lee, Jong Bok Kim, and Il Hong Suh. "Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning." Applied Sciences 10, no. 8 (2020): 2719. http://dx.doi.org/10.3390/app10082719.

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We propose a framework based on imitation learning and self-learning to enable robots to learn, improve, and generalize motor skills. The peg-in-hole task is important in manufacturing assembly work. Two motor skills for the peg-in-hole task are targeted: “hole search” and “peg insertion”. The robots learn initial motor skills from human demonstrations and then improve and/or generalize them through reinforcement learning (RL). An initial motor skill is represented as a concatenation of the parameters of a hidden Markov model (HMM) and a dynamic movement primitive (DMP) to classify input signa

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26

Dai, Lihong, Peng Hu, Tianyou Wang, Guosheng Bian, and Haoye Liu. "Optimal Rule-Interposing Reinforcement Learning-Based Energy Management of Series—Parallel-Connected Hybrid Electric Vehicles." Sustainability 16, no. 16 (2024): 6848. http://dx.doi.org/10.3390/su16166848.

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P2–P3 series–parallel hybrid electric vehicles exhibit complex configurations with multiple power sources and operational modes, presenting a difficulty in developing efficient energy management strategies. This paper takes a P2–P3 series–parallel hybrid power system-KunTye 2DHT system as the research object and proposes a deep reinforcement learning framework based on pre-optimized energy management to improve the energy consumption performance of the hybrid electric vehicles. Firstly, a control-oriented model is established based on its system configuration and characteristics. Then, the opt

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Fan, Jiang, Zhu Yunpu, Zou Quan, and Wang Manyi. "Reinforcement Learning based position control of shell-fetching manipulator with extreme random trees." Journal of Physics: Conference Series 2460, no. 1 (2023): 012160. http://dx.doi.org/10.1088/1742-6596/2460/1/012160.

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Abstract The automatic loading system of artillery includes the gun-fetching manipulator, which is crucial. The stability and control accuracy of the manipulator, however, are comparatively subpar when following the position trajectory as a result of the changes in the settings of the permanent magnet synchronous motor. This research suggests a reinforcement learning-based strategy for controlling a gun manipulator’s motor behavior in light of the current scenario. In this algorithm, the state variable is the feedback output of the control variable, and the reward function is utilized to calcu

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Bucur, C. "Artificial intelligence driven speed controller for DC motor in series." Scientific Bulletin of Naval Academy XIV, no. 2 (2021): 83–88. http://dx.doi.org/10.21279/1454-864x-21-i2-007.

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Recently a lot of work have been done to implement artificial intelligence controllers in the field of electrical motors. This paper presents a novel speed controller, developed through Reinforcement learning techniques, applied to series dc motors. We emphasize the ease of developed controller in available off the shelf hardware for industrial use. We used the open- source Python package gym-electric-motor [1] for environment setup, pytorch framework for developing the controller and .NET for performance evaluation.

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Guzman, Luis, Vassilios Morellas, and Nikolaos Papanikolopoulos. "Robotic Embodiment of Human-Like Motor Skills via Reinforcement Learning." IEEE Robotics and Automation Letters 7, no. 2 (2022): 3711–17. http://dx.doi.org/10.1109/lra.2022.3147453.

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30

Chai, Jiazheng, and Mitsuhiro Hayashibe. "Motor Synergy Development in High-Performing Deep Reinforcement Learning Algorithms." IEEE Robotics and Automation Letters 5, no. 2 (2020): 1271–78. http://dx.doi.org/10.1109/lra.2020.2968067.

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31

Arie, Hiroaki, Tetsuya Ogata, Jun Tani, and Shigeki Sugano. "Reinforcement learning of a continuous motor sequence with hidden states." Advanced Robotics 21, no. 10 (2007): 1215–29. http://dx.doi.org/10.1163/156855307781389365.

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Lu, Huimin, Yujie Li, Shenglin Mu, Dong Wang, Hyoungseop Kim, and Seiichi Serikawa. "Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning." IEEE Internet of Things Journal 5, no. 4 (2018): 2315–22. http://dx.doi.org/10.1109/jiot.2017.2737479.

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33

Kober, Jens, Andreas Wilhelm, Erhan Oztop, and Jan Peters. "Reinforcement learning to adjust parametrized motor primitives to new situations." Autonomous Robots 33, no. 4 (2012): 361–79. http://dx.doi.org/10.1007/s10514-012-9290-3.

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34

Yang, Yuguang, Michael A. Bevan, and Bo Li. "Micro/Nano Motor Navigation and Localization via Deep Reinforcement Learning." Advanced Theory and Simulations 3, no. 6 (2020): 2000034. http://dx.doi.org/10.1002/adts.202000034.

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Korivand, Soroush, Nader Jalili, and Jiaqi Gong. "Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling." Sensors 23, no. 5 (2023): 2698. http://dx.doi.org/10.3390/s23052698.

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Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies

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Bacon, Pierre-Luc, and Doina Precup. "Constructing Temporal Abstractions Autonomously in Reinforcement Learning." AI Magazine 39, no. 1 (2018): 39–50. http://dx.doi.org/10.1609/aimag.v39i1.2780.

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The idea of temporal abstraction, i.e. learning, planning and representing the world at multiple time scales, has been a constant thread in AI research, spanning sub-fields from classical planning and search to control and reinforcement learning. For example, programming a robot typically involves making decisions over a set of controllers, rather than working at the level of motor torques. While temporal abstraction is a very natural concept, learning such abstractions with no human input has proved quite daunting. In this paper, we present a general architecture, called option-critic, which

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37

Dominey, Peter F. "Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning." Biological Cybernetics 73, no. 3 (1995): 265–74. http://dx.doi.org/10.1007/bf00201428.

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Hwangbo, Jemin, Joonho Lee, Alexey Dosovitskiy, et al. "Learning agile and dynamic motor skills for legged robots." Science Robotics 4, no. 26 (2019): eaau5872. http://dx.doi.org/10.1126/scirobotics.aau5872.

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Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is

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Mulian, Hadar, Segev Shlomov, Lior Limonad, Alessia Noccaro, and Silvia Buscaglione. "Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23224–31. http://dx.doi.org/10.1609/aaai.v38i21.30369.

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Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effective, can be time-consuming and inconsistent. With the rise of advanced technologies like robotics and artificial intelligence, there is increasing interest in automating such teaching processes. In this study, we examine the potential of a virtual AI teacher in emulating the techniques of human educators for motor skill acquisition. We introduce an AI teacher model that captures the distinct characteristics of huma

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Wang, Quan, Juan Ying Qin, and Jun Hua Zhou. "Reinforcement Learning Based Self-Constructing Fuzzy Neural Network Controller for AC Motor Drives." Advanced Materials Research 139-141 (October 2010): 1763–68. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1763.

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A self-constructing fuzzy neural network (SCFNN) based on reinforcement learning is proposed in this study. In the SCFNN, structure and parameter learning are implemented simultaneously. Structure learning is based on uniform division of the input space and distribution of membership function. The structure and membership parameters are organized as real value chromosomes, and the chromosomes are trained by the reinforcement learning based on genetic algorithm. This paper uses Matlab/Simulink to establish simulation platform and several simulations are provided to demonstrate the effectiveness

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Celemin, Carlos, Guilherme Maeda, Javier Ruiz-del-Solar, Jan Peters, and Jens Kober. "Reinforcement learning of motor skills using Policy Search and human corrective advice." International Journal of Robotics Research 38, no. 14 (2019): 1560–80. http://dx.doi.org/10.1177/0278364919871998.

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Robot learning problems are limited by physical constraints, which make learning successful policies for complex motor skills on real systems unfeasible. Some reinforcement learning methods, like Policy Search, offer stable convergence toward locally optimal solutions, whereas interactive machine learning or learning-from-demonstration methods allow fast transfer of human knowledge to the agents. However, most methods require expert demonstrations. In this work, we propose the use of human corrective advice in the actions domain for learning motor trajectories. Additionally, we combine this hu

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42

Md Zuki, Muhammad Aiman, Nazlena Mohamad Ali, and Jun Kit Chaw. "REINFORCEMENT LEARNING: METHODS AND RECENT APPLICATIONS." Journal of Information System and Technology Management 9, no. 36 (2024): 67–89. https://doi.org/10.35631/jistm.936005.

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This comprehensive analysis highlights the potential of Reinforcement Learning (RL) to transform intelligent decision-making systems by examining its techniques and applications in a variety of disciplines. The study offers a thorough examination of the advantages and disadvantages of several reinforcement learning (RL) approaches, such as Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods, and Model-Based RL. The paper explores RL applications in several domains, including robotics, autonomous systems, and healthcare, showcasing its adaptability in handling intricate decision-making a

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Wang, Jinsung, Yuming Lei, and Jeffrey R. Binder. "Performing a reaching task with one arm while adapting to a visuomotor rotation with the other can lead to complete transfer of motor learning across the arms." Journal of Neurophysiology 113, no. 7 (2015): 2302–8. http://dx.doi.org/10.1152/jn.00974.2014.

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The extent to which motor learning is generalized across the limbs is typically very limited. Here, we investigated how two motor learning hypotheses could be used to enhance the extent of interlimb transfer. According to one hypothesis, we predicted that reinforcement of successful actions by providing binary error feedback regarding task success or failure, in addition to terminal error feedback, during initial training would increase the extent of interlimb transfer following visuomotor adaptation ( experiment 1). According to the other hypothesis, we predicted that performing a reaching ta

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Naros, G., I. Naros, F. Grimm, U. Ziemann та A. Gharabaghi. "Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance". NeuroImage 134 (липень 2016): 142–52. http://dx.doi.org/10.1016/j.neuroimage.2016.03.016.

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Colino, Francisco L., Matthew Heath, Cameron D. Hassall, and Olave E. Krigolson. "Electroencephalographic evidence for a reinforcement learning advantage during motor skill acquisition." Biological Psychology 151 (March 2020): 107849. http://dx.doi.org/10.1016/j.biopsycho.2020.107849.

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46

Pantoja-Garcia, Luis, Vicente Parra-Vega, Rodolfo Garcia-Rodriguez, and Carlos Ernesto Vázquez-García. "A Novel Actor—Critic Motor Reinforcement Learning for Continuum Soft Robots." Robotics 12, no. 5 (2023): 141. http://dx.doi.org/10.3390/robotics12050141.

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Reinforcement learning (RL) is explored for motor control of a novel pneumatic-driven soft robot modeled after continuum media with a varying density. This model complies with closed-form Lagrangian dynamics, which fulfills the fundamental structural property of passivity, among others. Then, the question arises of how to synthesize a passivity-based RL model to control the unknown continuum soft robot dynamics to exploit its input–output energy properties advantageously throughout a reward-based neural network controller. Thus, we propose a continuous-time Actor–Critic scheme for tracking tas

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Manohar, Sanjay G. "Tremor in Parkinson's disease inverts the effect of dopamine on reinforcement." Brain 143, no. 11 (2020): 3178–80. http://dx.doi.org/10.1093/brain/awaa363.

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Mashiri, Tapiwa, and Mbika Muteba. "A Review of Advances in Brushless Synchronous Motor Drive’s Control Techniques." Eng 6, no. 1 (2025): 8. https://doi.org/10.3390/eng6010008.

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In this paper, a review of the advances in brushless synchronous motors is presented because there has been an increasing interest in advanced motor control and to address the weaknesses of conventional motor control. The traditional motor control strategies, for example, proportional plus integral controllers (PIs), are simple and easy to maintain. On the contrary, they require accurate tuning and are affected by motor parameter variations. To address these challenges and many others (power factors, torque ripple, current limit, voltage limit, speed limit), advanced control methods are requir

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Ting, Chih-Chung, Stefano Palminteri, Jan B. Engelmann, and Maël Lebreton. "Robust valence-induced biases on motor response and confidence in human reinforcement learning." Cognitive, Affective, & Behavioral Neuroscience 20, no. 6 (2020): 1184–99. http://dx.doi.org/10.3758/s13415-020-00826-0.

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AbstractIn simple instrumental-learning tasks, humans learn to seek gains and to avoid losses equally well. Yet, two effects of valence are observed. First, decisions in loss-contexts are slower. Second, loss contexts decrease individuals’ confidence in their choices. Whether these two effects are two manifestations of a single mechanism or whether they can be partially dissociated is unknown. Across six experiments, we attempted to disrupt the valence-induced motor bias effects by manipulating the mapping between decisions and actions and imposing constraints on response times (RTs). Our goal

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Rusanen, Anna-Mari, Otto Lappi, Jesse Kuokkanen, and Jami Pekkanen. "Action control, forward models and expected rewards: representations in reinforcement learning." Synthese 199, no. 5-6 (2021): 14017–33. http://dx.doi.org/10.1007/s11229-021-03408-w.

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AbstractThe fundamental cognitive problem for active organisms is to decide what to do next in a changing environment. In this article, we analyze motor and action control in computational models that utilize reinforcement learning (RL) algorithms. In reinforcement learning, action control is governed by an action selection policy that maximizes the expected future reward in light of a predictive world model. In this paper we argue that RL provides a way to explicate the so-called action-oriented views of cognitive systems in representational terms.

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