Добірка наукової літератури з теми "Improper reinforcement learning"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Improper reinforcement learning".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Improper reinforcement learning"
Dass, Shuvalaxmi, and Akbar Siami Namin. "Reinforcement Learning for Generating Secure Configurations." Electronics 10, no. 19 (September 30, 2021): 2392. http://dx.doi.org/10.3390/electronics10192392.
Повний текст джерелаZhai, Peng, Jie Luo, Zhiyan Dong, Lihua Zhang, Shunli Wang, and Dingkang Yang. "Robust Adversarial Reinforcement Learning with Dissipation Inequation Constraint." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5431–39. http://dx.doi.org/10.1609/aaai.v36i5.20481.
Повний текст джерелаChen, Ya-Ling, Yan-Rou Cai, and Ming-Yang Cheng. "Vision-Based Robotic Object Grasping—A Deep Reinforcement Learning Approach." Machines 11, no. 2 (February 12, 2023): 275. http://dx.doi.org/10.3390/machines11020275.
Повний текст джерелаHurtado-Gómez, Julián, Juan David Romo, Ricardo Salazar-Cabrera, Álvaro Pachón de la Cruz, and Juan Manuel Madrid Molina. "Traffic Signal Control System Based on Intelligent Transportation System and Reinforcement Learning." Electronics 10, no. 19 (September 28, 2021): 2363. http://dx.doi.org/10.3390/electronics10192363.
Повний текст джерелаZiwei Pan, Ziwei Pan. "Design of Interactive Cultural Brand Marketing System based on Cloud Service Platform." 網際網路技術學刊 23, no. 2 (March 2022): 321–34. http://dx.doi.org/10.53106/160792642022032302012.
Повний текст джерелаKim, Byeongjun, Gunam Kwon, Chaneun Park, and Nam Kyu Kwon. "The Task Decomposition and Dedicated Reward-System-Based Reinforcement Learning Algorithm for Pick-and-Place." Biomimetics 8, no. 2 (June 6, 2023): 240. http://dx.doi.org/10.3390/biomimetics8020240.
Повний текст джерелаRitonga, Mahyudin, and Fitria Sartika. "Muyûl al-Talâmidh fî Tadrîs al-Qirâ’ah." Jurnal Alfazuna : Jurnal Pembelajaran Bahasa Arab dan Kebahasaaraban 6, no. 1 (December 21, 2021): 36–52. http://dx.doi.org/10.15642/alfazuna.v6i1.1715.
Повний текст джерелаLikas, Aristidis. "A Reinforcement Learning Approach to Online Clustering." Neural Computation 11, no. 8 (November 1, 1999): 1915–32. http://dx.doi.org/10.1162/089976699300016025.
Повний текст джерелаYing-Ming Shi, Ying-Ming Shi, and Zhiyuan Zhang Ying-Ming Shi. "Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning." 電腦學刊 33, no. 3 (June 2022): 187–94. http://dx.doi.org/10.53106/199115992022063303015.
Повний текст джерелаSantos, John Paul E., Joseph A. Villarama, Joseph P. Adsuara, Jordan F. Gundran, Aileen G. De Guzman, and Evelyn M. Ben. "Students’ Time Management, Academic Procrastination, and Performance during Online Science and Mathematics Classes." International Journal of Learning, Teaching and Educational Research 21, no. 12 (December 30, 2022): 142–61. http://dx.doi.org/10.26803/ijlter.21.12.8.
Повний текст джерелаДисертації з теми "Improper reinforcement learning"
BRUCHON, NIKY. "Feasibility Investigation on Several Reinforcement Learning Techniques to Improve the Performance of the FERMI Free-Electron Laser." Doctoral thesis, Università degli Studi di Trieste, 2021. http://hdl.handle.net/11368/2982117.
Повний текст джерелаKreutmayr, Fabian, and Markus Imlauer. "Application of machine learning to improve to performance of a pressure-controlled system." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71076.
Повний текст джерелаZaki, Mohammadi. "Algorithms for Online Learning in Structured Environments." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6080.
Повний текст джерелаChi, Lu-cheng, and 紀律呈. "An Improved Deep Reinforcement Learning with Sparse Rewards." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/eq94pr.
Повний текст джерела國立中山大學
電機工程學系研究所
107
In reinforcement learning, how an agent explores in an environment with sparse rewards is a long-standing problem. An improved deep reinforcement learning described in this thesis encourages an agent to explore unvisited environmental states in an environment with sparse rewards. In deep reinforcement learning, an agent directly uses an image observation from environment as an input to the neural network. However, some neglected observations from environment, such as depth, might provide valuable information. An improved deep reinforcement learning described in this thesis is based on the Actor-Critic algorithm and uses the convolutional neural network as a hetero-encoder between an image input and other observations from environment. In the environment with sparse rewards, we use these neglected observations from environment as a target output of supervised learning and provide an agent denser training signals through supervised learning to bootstrap reinforcement learning. In addition, we use the loss from supervised learning as the feedback for an agent’s exploration behavior in an environment, called the label reward, to encourage an agent to explore unvisited environmental states. Finally, we construct multiple neural networks by Asynchronous Advantage Actor-Critic algorithm and learn the policy with multiple agents. An improved deep reinforcement learning described in this thesis is compared with other deep reinforcement learning in an environment with sparse rewards and achieves better performance.
Hsin-Jung, Huang, and 黃信榮. "Applying Reinforcement Learning to Improve NPC game Character Intelligence." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/38802886766630465543.
Повний текст джерела大葉大學
資訊管理學系碩士班
95
Today, video games are the most popular entertainment for young people. With rapidly developed computer technology, the quality and complexity of AI (Artificial In-telligence) used in computer games are gradually increasing. Today, AI has become a vital element of computer games. Intelligent NPC (Non-Player Character) which can act as playmates is becoming the essential element for most video games. How to enhance the intelligence of game characters has become an important research topic. This study proposes a cooperative reinforcement learning structure of NPC agents that share the common global states and the overall reward mechanism. Agents trained through our reinforcement learning mechanism will be able to develop an action strat-egy to complete their missions in the virtual game environment. Our empirical result has shown some promising result. Even the NPC agents are tested in different game level environments, all agents that share with the same goal will learn to perform ap-propriate actions and achieve the common goal reasonably.
Chen, Chia-Hao, and 陳家豪. "Improve Top ASR Hypothesis with Re-correction by Reinforcement Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/zde779.
Повний текст джерела國立中央大學
資訊工程學系
107
In real situations, utterances are transcribed by ASR(Automatic Speech Recognition) systems, which usually propose multiple candidate transcriptions(hypothesis). Most of the time, the first hypothesis is the best and most commonly used. But the first hypothesis of ASR in a noisy environment often misses some words that are important to the LU(Language Understanding), and these words can be found among second hypothesis. But on the whole, the first ASR hypothesis is significantly better than the second ASR hypothesis. It is not the best choice if we abandon the first ASR hypothesis because it lacks some words. If we can refer to the 2th ASR hypothesis to modify the missing or redundant words of the first ASR hypothesis, we can get utterances closer to the user's true intentions. In this paper we propose a method to automatically correct the 1th ASR hypothesis by the reinforcement learning model. It can correct the first hypothesis word by word by other hypothesis. Our method raises the bleu score of 1th ASR hypothesis from 70.18 to 76.74.
Hsu, Yung-Chi, and 徐永吉. "Improved Safe Reinforcement Learning Based Self Adaptive Evolutionary Algorithms for Neuro-Fuzzy Controller Design." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/43659775487135397105.
Повний текст джерела國立交通大學
電機與控制工程系所
97
In this dissertation, improved safe reinforcement learning based self adaptive evolutionary algorithms (ISRL-SAEAs) are proposed for TSK-type neuro-fuzzy controller design. The ISRL-SAEAs can improve not only the reinforcement signal designed but also traditional evolutionary algorithms. There are two parts in the proposed ISRL-SAEAs. In the first part, the SAEAs are proposed to solve the following problems: 1) all the fuzzy rules are encoded into one chromosome; 2) the number of fuzzy rules has to be assigned in advance; and 3) the population cannot evaluate each fuzzy rule locally. The second part of the ISRL-SAEAs is the ISRL. In the ISRL, two different strategies (judgment and evaluation) are used to design the reinforcement signal. Moreover the Lyapunov stability is considered in ISRL. To demonstrate the performance of the proposed method, the inverted pendulum control system and tandem pendulum control system are presented. As shown in simulation, the ISRL-SAEAs perform better than other reinforcement evolution methods.
Lin, Ching-Pin, and 林敬斌. "Using Reinforcement Learning to Improve a Simple Intra-day Trading System of Taiwan Stock Index Future." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/34369847383488676186.
Повний текст джерела國立臺灣大學
資訊工程學研究所
97
This thesis applied Q-learning algorithm of reinforcement learning to improve a simple intra-day trading system of Taiwan stock index future. We simulate the performance of the original strategy by back-testing it with historical data. Furthermore, we use historical information as training data for reinforcement learning and examine the improved achievement. The training data are the tick data of every trading day from 2003 to 2007 and the testing period is from January 2008 to May 2009. The original strategy is a trend-following channel breakout system. We take the result of reinforcement learning to determine whether to do trend following or countertrend trading every time the system plans to make position.
Книги з теми "Improper reinforcement learning"
Urtāns, Ēvalds. Function shaping in deep learning. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226854.
Повний текст джерелаRohsenow, Damaris J., and Megan M. Pinkston-Camp. Cognitive-Behavioral Approaches. Edited by Kenneth J. Sher. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199381708.013.010.
Повний текст джерелаCarmo, Mafalda. Education Applications & Developments VI. inScience Press, 2021. http://dx.doi.org/10.36315/2021eadvi.
Повний текст джерелаЧастини книг з теми "Improper reinforcement learning"
Wang, Kunfu, Ruolin Xing, Wei Feng, and Baiqiao Huang. "A Method of UAV Formation Transformation Based on Reinforcement Learning Multi-agent." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 187–95. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_20.
Повний текст джерелаSingh, Moirangthem Tiken, Aninda Chakrabarty, Bhargab Sarma, and Sourav Dutta. "An Improved On-Policy Reinforcement Learning Algorithm." In Advances in Intelligent Systems and Computing, 321–30. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7394-1_30.
Повний текст джерелаMa, Ping, and Hong-Li Zhang. "Improved Artificial Bee Colony Algorithm Based on Reinforcement Learning." In Intelligent Computing Theories and Application, 721–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42294-7_64.
Повний текст джерелаDai, Zixiang, and Mingyan Jiang. "An Improved Lion Swarm Algorithm Based on Reinforcement Learning." In Advances in Intelligent Automation and Soft Computing, 76–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_10.
Повний текст джерелаKim, Jongrae. "Improved Robustness Analysis of Reinforcement Learning Embedded Control Systems." In Robot Intelligence Technology and Applications 6, 104–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97672-9_10.
Повний текст джерелаReid, Mark, and Malcolm Ryan. "Using ILP to Improve Planning in Hierarchical Reinforcement Learning." In Inductive Logic Programming, 174–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44960-4_11.
Повний текст джерелаCallegari, Daniel Antonio, and Flávio Moreira de Oliveira. "Applying Reinforcement Learning to Improve MCOE, an Intelligent Learning Environment for Ecology." In Lecture Notes in Computer Science, 284–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720076_26.
Повний текст джерелаFountain, Jake, Josiah Walker, David Budden, Alexandre Mendes, and Stephan K. Chalup. "Motivated Reinforcement Learning for Improved Head Actuation of Humanoid Robots." In RoboCup 2013: Robot World Cup XVII, 268–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44468-9_24.
Повний текст джерелаLiu, Jun, Yi Zhou, Yimin Qiu, and Zhongfeng Li. "An Improved Multi-objective Optimization Algorithm Based on Reinforcement Learning." In Lecture Notes in Computer Science, 501–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09677-8_42.
Повний текст джерелаZhong, Chen, Chutong Ye, Chenyu Wu, and Ao Zhan. "An Improved Dynamic Spectrum Access Algorithm Based on Reinforcement Learning." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 13–25. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30237-4_2.
Повний текст джерелаТези доповідей конференцій з теми "Improper reinforcement learning"
Narvekar, Sanmit. "Curriculum Learning in Reinforcement Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/757.
Повний текст джерелаWang, Zhaodong, and Matthew E. Taylor. "Improving Reinforcement Learning with Confidence-Based Demonstrations." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/422.
Повний текст джерелаVuong, Tung-Long, Do-Van Nguyen, Tai-Long Nguyen, Cong-Minh Bui, Hai-Dang Kieu, Viet-Cuong Ta, Quoc-Long Tran, and Thanh-Ha Le. "Sharing Experience in Multitask Reinforcement Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/505.
Повний текст джерелаGabel, Thomas, Christian Lutz, and Martin Riedmiller. "Improved neural fitted Q iteration applied to a novel computer gaming and learning benchmark." In 2011 Ieee Symposium On Adaptive Dynamic Programming And Reinforcement Learning. IEEE, 2011. http://dx.doi.org/10.1109/adprl.2011.5967361.
Повний текст джерелаWu, Yuechen, Wei Zhang, and Ke Song. "Master-Slave Curriculum Design for Reinforcement Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/211.
Повний текст джерелаQin, Yunxiao, Weiguo Zhang, Jingping Shi, and Jinglong Liu. "Improve PID controller through reinforcement learning." In 2018 IEEE CSAA Guidance, Navigation and Control Conference (GNCC). IEEE, 2018. http://dx.doi.org/10.1109/gncc42960.2018.9019095.
Повний текст джерелаDESHPANDE, PRATHAMESH P., KAREN J. DEMILLE, AOWABIN RAHMAN, SUSANTA GHOSH, ASHLEY D. SPEAR, and GREGORY M. ODEGARD. "DESIGNING AN IMPROVED INTERFACE IN GRAPHENE/POLYMER COMPOSITES THROUGH MACHINE LEARNING." In Proceedings for the American Society for Composites-Thirty Seventh Technical Conference. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/asc37/36458.
Повний текст джерелаEaglin, Gerald, and Joshua Vaughan. "Leveraging Conventional Control to Improve Performance of Systems Using Reinforcement Learning." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3307.
Повний текст джерелаSong, Haolin, Mingxiao Feng, Wengang Zhou, and Houqiang Li. "MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/470.
Повний текст джерелаZhu, Hanhua. "Generalized Representation Learning Methods for Deep Reinforcement Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/748.
Повний текст джерелаЗвіти організацій з теми "Improper reinforcement learning"
Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber, and Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7570567.bard.
Повний текст джерелаA Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning. SAE International, December 2020. http://dx.doi.org/10.4271/2020-01-5154.
Повний текст джерела