All Relations between reward and rl

Publication Sentence Publish Date Extraction Date Species
Xingche Guo, Donglin Zeng, Yuanjia Wan. A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders. Journal of the American Statistical Association. vol 119. issue 545. 2024-05-06. PMID:38706706. motivated by the probabilistic reward task (prt) experiment in the embarc study, we propose a semiparametric inverse reinforcement learning (rl) approach to characterize the reward-based decision-making of mdd patients. 2024-05-06 2024-05-08 human
Le Huu Binh, Thuy-Van T Duon. A novel and effective method for solving the router nodes placement in wireless mesh networks using reinforcement learning. PloS one. vol 19. issue 4. 2024-04-10. PMID:38598499. the rnp problem is modeled as an rl model with environment, agent, action, and reward are equivalent to the network system, routers, coordinate adjustment, and connectivity of the rnp problem, respectively. 2024-04-10 2024-04-13 Not clear
Alexander Kensert, Pieter Libin, Gert Desmet, Deirdre Caboote. Deep reinforcement learning for the direct optimization of gradient separations in liquid chromatography. Journal of chromatography. A. vol 1720. 2024-03-05. PMID:38442496. this paper therefore aims to introduce rl, specifically proximal policy optimization (ppo), in liquid chromatography, and evaluate whether it can be trained to optimize separations directly, based solely on the outcome of a single generic separation as input, and a reward signal based on the resolution between peak pairs (taking a value between [-1,1]). 2024-03-05 2024-03-08 Not clear
Thang M Le, Takeyuki Oba, Luke Couch, Lauren McInerney, Chiang-Shan R L. The neural correlates of individual differences in reinforcement learning during pain avoidance and reward seeking. eNeuro. 2024-02-16. PMID:38365840. reinforcement learning (rl) offers a critical framework to understand individual differences in this associative learning by assessing learning rate, action bias, pavlovian factor (i.e., the extent to which action values are influenced by stimulus values), and subjective impact of outcomes (i.e., motivation to seek reward and avoid punishment). 2024-02-16 2024-02-19 human
Xuan-Kun Li, Jian-Xu Ma, Xiang-Yu Li, Jun-Jie Hu, Chuan-Yang Ding, Feng-Kai Han, Xiao-Min Guo, Xi Tan, Xian-Min Ji. High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit. Nature communications. vol 15. issue 1. 2024-02-05. PMID:38316815. by introducing similarity information into the reward function of the rl model, pic-rl successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. 2024-02-05 2024-02-09 Not clear
Jyun-Wei Li, Yu-Chieh Teng, Shinji Nimura, Yibeltal Chanie Manie, Kamya Yekeh Yazdandoost, Kazuki Tanaka, Ryo Inohara, Takehiro Tsuritani, Peng-Chun Pen. Reinforcement learning-based adaptive beam alignment in a photodiode-integrated array antenna module. Optics letters. vol 49. issue 3. 2024-02-01. PMID:38300085. in our proposed scheme, the three key elements of rl: state, action, and reward, are represented as the phase values in the photonic array antenna, phase changes with specified steps, and an obtained error vector magnitude (evm) value, respectively. 2024-02-01 2024-02-03 Not clear
Zeyong Wei, Honghua Chen, Liangliang Nan, Jun Wang, Jing Qin, Mingqiang We. PathNet: Path-Selective Point Cloud Denoising. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2024-01-19. PMID:38241116. first, to leverage geometry expertise and benefit from training data, we propose a noise- and geometry-aware reward function to train the routing agent in rl. 2024-01-19 2024-01-22 Not clear
Antoine Théberge, Christian Desrosiers, Arnaud Boré, Maxime Descoteaux, Pierre-Marc Jodoi. What matters in reinforcement learning for tractography. Medical image analysis. vol 93. 2024-01-14. PMID:38219499. in this work, we thoroughly explore the different components of the proposed framework, such as the choice of the rl algorithm, seeding strategy, the input signal and reward function, and shed light on their impact. 2024-01-14 2024-01-17 Not clear
Antoine Théberge, Christian Desrosiers, Arnaud Boré, Maxime Descoteaux, Pierre-Marc Jodoi. What matters in reinforcement learning for tractography. Medical image analysis. vol 93. 2024-01-14. PMID:38219499. as such, we ultimately propose a series of recommendations concerning the choice of rl algorithm, the input to the agents, the reward function and more to help future work using reinforcement learning for tractography. 2024-01-14 2024-01-17 Not clear
Qiming Zou, Einoshin Suzuk. Compact Goal Representation Learning via Information Bottleneck in Goal-Conditioned Reinforcement Learning. IEEE transactions on neural networks and learning systems. vol PP. 2024-01-09. PMID:38190683. goal-conditioned rl learns a policy from reward signals to predict actions for reaching desired goals. 2024-01-09 2024-01-10 Not clear
Qiming Zou, Einoshin Suzuk. Compact Goal Representation Learning via Information Bottleneck in Goal-Conditioned Reinforcement Learning. IEEE transactions on neural networks and learning systems. vol PP. 2024-01-09. PMID:38190683. however, in goal-conditioned rl, it is difficult to balance the tradeoff between task-relevant information and task-irrelevant information because of the sparse and delayed learning signals, i.e., reward signals, and the inevitable task-relevant information sacrifice caused by information compression. 2024-01-09 2024-01-10 Not clear
Yang Lin, Liang Chu, Jincheng Hu, Zhuoran Hou, Jihao Li, Jingjing Jiang, Yuanjian Zhan. Progress and summary of reinforcement learning on energy management of MPS-EV. Heliyon. vol 10. issue 1. 2024-01-01. PMID:38163106. this paper first summarizes the previous applications of rl in ems from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. 2024-01-01 2024-01-05 Not clear
Elizabeth A Bauer, Brandon K Watanabe, Annmarie MacNamar. Reinforcement learning and the reward positivity with aversive outcomes. Psychophysiology. 2023-11-23. PMID:37994210. the reinforcement learning (rl) theory of the reward positivity (rewp), an event-related potential (erp) component that measures reward responsivity, suggests that the rewp should be largest when positive outcomes are unexpected and has been supported by work using appetitive outcomes (e.g., money). 2023-11-23 2023-11-29 human
Elizabeth A Bauer, Brandon K Watanabe, Annmarie MacNamar. Reinforcement learning and the reward positivity with aversive outcomes. Psychophysiology. 2023-11-23. PMID:37994210. here, we tested the predictions of the rl theory by manipulating expectancy in an active/choice-based threat-of-shock doors task that was previously found to elicit the rewp as a reward signal. 2023-11-23 2023-11-29 human
Elizabeth A Bauer, Brandon K Watanabe, Annmarie MacNamar. Reinforcement learning and the reward positivity with aversive outcomes. Psychophysiology. 2023-11-23. PMID:37994210. therefore, the rewp appears to reflect the additive (not interactive) effects of reward and expectancy, challenging the rl theory of the rewp, at least when reward is defined as the absence of an aversive outcome. 2023-11-23 2023-11-29 human
Mythra V Balakuntala, Glebys T Gonzalez, Juan P Wachs, Richard M Voyle. ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery. Military medicine. vol 188. issue Supplement_6. 2023-11-10. PMID:37948233. this article presents a method for learning from demonstration, combining knowledge from demonstrations to eliminate reward shaping in reinforcement learning (rl). 2023-11-10 2023-11-20 human
Jingda Wu, Yanxin Zhou, Haohan Yang, Zhiyu Huang, Chen L. Human-Guided Reinforcement Learning With Sim-to-Real Transfer for Autonomous Navigation. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2023-09-13. PMID:37703148. meanwhile, the training of rl on navigation tasks is difficult, which requires a carefully-designed reward function and a large number of interactions, yet rl navigation can still fail due to many corner cases. 2023-09-13 2023-10-07 human
Jingda Wu, Yanxin Zhou, Haohan Yang, Zhiyu Huang, Chen L. Human-Guided Reinforcement Learning With Sim-to-Real Transfer for Autonomous Navigation. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2023-09-13. PMID:37703148. an innovative human-guided rl algorithm is proposed that utilizes a series of mechanisms to improve the effectiveness of human guidance, including human-guided learning objective, prioritized human experience replay, and human intervention-based reward shaping. 2023-09-13 2023-10-07 human
Maria Cecilia Serafini, Nicolas Rosales, Fabricio Garell. Auto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization. Computer methods in biomechanics and biomedical engineering. 2023-08-07. PMID:37545465. in this work, the performance of two reinforcement learning (rl) agents trained under both piecewise and continuous reward functions is evaluated 2023-08-07 2023-08-14 Not clear
Hongliang Zeng, Ping Zhang, Fang Li, Chubin Lin, Junkang Zho. AHEGC: Adaptive Hindsight Experience Replay With Goal-Amended Curiosity Module for Robot Control. IEEE transactions on neural networks and learning systems. vol PP. 2023-08-01. PMID:37527323. with shaped reward functions, reinforcement learning (rl) has recently been successfully applied to several robot control tasks. 2023-08-01 2023-08-14 Not clear