Journal articles on the topic 'Constrained RL'
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HasanzadeZonuzy, Aria, Archana Bura, Dileep Kalathil, and Srinivas Shakkottai. "Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7667–74. http://dx.doi.org/10.1609/aaai.v35i9.16937.
Full textZhang, Renchi, Runsheng Yu, and Wei Xia. "Constraint-aware Policy Optimization to Solve the Vehicle Routing Problem with Time Windows." Information Technology and Control 51, no. 1 (March 26, 2022): 126–38. http://dx.doi.org/10.5755/j01.itc.51.1.29924.
Full textHu, Jingwei, Zhu Liu, Chichuan Jin, and Weimin Yuan. "Relativistic Fe Kα line in the composite X-ray spectra of radio-loud active galactic nuclei." Monthly Notices of the Royal Astronomical Society 488, no. 3 (July 25, 2019): 4378–88. http://dx.doi.org/10.1093/mnras/stz2030.
Full textBhatia, Abhinav, Pradeep Varakantham, and Akshat Kumar. "Resource Constrained Deep Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 610–20. http://dx.doi.org/10.1609/icaps.v29i1.3528.
Full textGu, Shangding, Guang Chen, Lijun Zhang, Jing Hou, Yingbai Hu, and Alois Knoll. "Constrained Reinforcement Learning for Vehicle Motion Planning with Topological Reachability Analysis." Robotics 11, no. 4 (August 16, 2022): 81. http://dx.doi.org/10.3390/robotics11040081.
Full textWang, Ru-Min, Jin-Huan Sheng, Jie Zhu, Ying-Ying Fan, and Yuan-Guo Xu. "Decays $D^+_{(s)}\to \pi(K)^{+}\ell^+\ell^-$ and D0→ℓ+ℓ- in the MSSM with and without R-parity." International Journal of Modern Physics A 30, no. 12 (April 28, 2015): 1550063. http://dx.doi.org/10.1142/s0217751x15500633.
Full textWei, Honghao, Xin Liu, and Lei Ying. "A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 3868–76. http://dx.doi.org/10.1609/aaai.v36i4.20302.
Full textLee, Xian Yeow, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, and Soumik Sarkar. "Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4577–84. http://dx.doi.org/10.1609/aaai.v34i04.5887.
Full textDelgrange, Florent, Ann Nowé, and Guillermo A. Pérez. "Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6497–505. http://dx.doi.org/10.1609/aaai.v36i6.20602.
Full textDing, Zhenhuan, Xiaoge Huang, and Zhao Liu. "Active Exploration by Chance-Constrained Optimization for Voltage Regulation with Reinforcement Learning." Energies 15, no. 2 (January 16, 2022): 614. http://dx.doi.org/10.3390/en15020614.
Full textCiliegi, P., M. Elvis, B. J. Wilkes, B. J. Boyle, R. G. Mcmahon, and T. Maccacaro. "VLA Observations of the Cambridge-Cambridge Rosat Survey." Symposium - International Astronomical Union 175 (1996): 543–44. http://dx.doi.org/10.1017/s0074180900081791.
Full textMcMillan, Justin R., Jonathan Botts, and Jason E. Summers. "Deep reinforcement learning for cognitive active-sonar employment." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A101. http://dx.doi.org/10.1121/10.0010785.
Full textWang, Lehan, Jingzhou Sun, Yuxuan Sun, Sheng Zhou, and Zhisheng Niu. "A UoI-Optimal Policy for Timely Status Updates with Resource Constraint." Entropy 23, no. 8 (August 20, 2021): 1084. http://dx.doi.org/10.3390/e23081084.
Full textMutombo, Vially Kazadi, Seungyeon Lee, Jusuk Lee, and Jiman Hong. "EER-RL: Energy-Efficient Routing Based on Reinforcement Learning." Mobile Information Systems 2021 (April 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/5589145.
Full textZhan, Xianyuan, Haoran Xu, Yue Zhang, Xiangyu Zhu, Honglei Yin, and Yu Zheng. "DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4680–88. http://dx.doi.org/10.1609/aaai.v36i4.20393.
Full textXie, Zhihang, and Qiquan Lin. "Reinforcement Learning-Based Adaptive Position Control Scheme for Uncertain Robotic Manipulators with Constrained Angular Position and Angular Velocity." Applied Sciences 13, no. 3 (January 18, 2023): 1275. http://dx.doi.org/10.3390/app13031275.
Full textTurnbull, Matthew H., Romà Ogaya, Adrià Barbeta, Josep Peñuelas, Joana Zaragoza-Castells, Owen K. Atkin, Fernando Valladares, Teresa E. Gimeno, Beatriz Pías, and Kevin L. Griffin. "Light inhibition of foliar respiration in response to soil water availability and seasonal changes in temperature in Mediterranean holm oak (Quercus ilex) forest." Functional Plant Biology 44, no. 12 (2017): 1178. http://dx.doi.org/10.1071/fp17032.
Full textWang, Xun, and Hongbin Chen. "A Reinforcement Learning-Based Dynamic Clustering Algorithm for Compressive Data Gathering in Wireless Sensor Networks." Mobile Information Systems 2022 (May 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/2736734.
Full textPandit, Mohammad Khalid, Roohie Naaz Mir, and Mohammad Ahsan Chishti. "Adaptive task scheduling in IoT using reinforcement learning." International Journal of Intelligent Computing and Cybernetics 13, no. 3 (June 30, 2020): 261–82. http://dx.doi.org/10.1108/ijicc-03-2020-0021.
Full textMarchesini, Enrico, Davide Corsi, and Alessandro Farinelli. "Exploring Safer Behaviors for Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7701–9. http://dx.doi.org/10.1609/aaai.v36i7.20737.
Full textLiang, Enming, Zicheng Su, Chilin Fang, and Renxin Zhong. "OAM: An Option-Action Reinforcement Learning Framework for Universal Multi-Intersection Control." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4550–58. http://dx.doi.org/10.1609/aaai.v36i4.20378.
Full textJeffery, Robert P., Richard J. Simpson, Hans Lambers, Daniel R. Kidd, and Megan H. Ryan. "Plants in constrained canopy micro-swards compensate for decreased root biomass and soil exploration with increased amounts of rhizosphere carboxylates." Functional Plant Biology 44, no. 5 (2017): 552. http://dx.doi.org/10.1071/fp16398.
Full textJing, Mingxuan, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Chao Yang, Bin Fang, and Huaping Liu. "Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5109–16. http://dx.doi.org/10.1609/aaai.v34i04.5953.
Full textUllah, Zakir, Zhiwei Xu, Lei Zhang, Libo Zhang, and Waheed Ullah. "RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment." IEEE Access 6 (2018): 74557–68. http://dx.doi.org/10.1109/access.2018.2882875.
Full textTatavarti, Hari, Prashant Doshi, and Layton Hayes. "Data-Driven Decision-Theoretic Planning using Recurrent Sum-Product-Max Networks." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 606–14. http://dx.doi.org/10.1609/icaps.v31i1.16009.
Full textZong Chen, Joy Iong, and Kong-Long Lai. "Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method - A Study." December 2020 2, no. 4 (January 19, 2021): 236–45. http://dx.doi.org/10.36548/jscp.2020.4.005.
Full textShanmugam, Sivagurunathan, Muthu Ganeshan V., Prathapchandran K., and Janani T. "Mitigating Black Hole Attacks in Routing Protocols Using a Machine Learning-Based Trust Model." International Journal of Sociotechnology and Knowledge Development 14, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijskd.310067.
Full textJiang, Jianhua, Yangang Ren, Yang Guan, Shengbo Eben Li, Yuming Yin, Dongjie Yu, and Xiaoping Jin. "Integrated decision and control at multi-lane intersections with mixed traffic flow." Journal of Physics: Conference Series 2234, no. 1 (April 1, 2022): 012015. http://dx.doi.org/10.1088/1742-6596/2234/1/012015.
Full textChen, Tsing-Chang, Jenq-Dar Tsay, and William J. Gutowski. "A Comparison Study of Three Polar Grids." Journal of Applied Meteorology and Climatology 47, no. 11 (November 1, 2008): 2993–3007. http://dx.doi.org/10.1175/2008jamc1746.1.
Full textMa, Yecheng Jason, Andrew Shen, Osbert Bastani, and Jayaraman Dinesh. "Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5404–12. http://dx.doi.org/10.1609/aaai.v36i5.20478.
Full textKrishnan, Srivatsan, Behzad Boroujerdian, William Fu, Aleksandra Faust, and Vijay Janapa Reddi. "Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation." Machine Learning 110, no. 9 (July 7, 2021): 2501–40. http://dx.doi.org/10.1007/s10994-021-06006-6.
Full textKaymak, Çağrı, Ayşegül Uçar, and Cüneyt Güzeliş. "Development of a New Robust Stable Walking Algorithm for a Humanoid Robot Using Deep Reinforcement Learning with Multi-Sensor Data Fusion." Electronics 12, no. 3 (January 22, 2023): 568. http://dx.doi.org/10.3390/electronics12030568.
Full textXu, Haoran, Xianyuan Zhan, and Xiangyu Zhu. "Constraints Penalized Q-learning for Safe Offline Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8753–60. http://dx.doi.org/10.1609/aaai.v36i8.20855.
Full textMu, Tong, Georgios Theocharous, David Arbour, and Emma Brunskill. "Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7841–49. http://dx.doi.org/10.1609/aaai.v36i7.20753.
Full textShan, Nanliang, Zecong Ye, and Xiaolong Cui. "Collaborative Intelligence: Accelerating Deep Neural Network Inference via Device-Edge Synergy." Security and Communication Networks 2020 (September 7, 2020): 1–10. http://dx.doi.org/10.1155/2020/8831341.
Full textSpieker, Helge. "Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10037–38. http://dx.doi.org/10.1609/aaai.v33i01.330110037.
Full textLe, Nhat, A. B. Siddique, Fuad Jamour, Samet Oymak, and Vagelis Hristidis. "Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems." International Journal of Semantic Computing 15, no. 04 (December 2021): 419–39. http://dx.doi.org/10.1142/s1793351x21400109.
Full textLiu, Yongshuai, Jiaxin Ding, and Xin Liu. "IPO: Interior-Point Policy Optimization under Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4940–47. http://dx.doi.org/10.1609/aaai.v34i04.5932.
Full textNurkasanah, Ika. "Reinforcement Learning Approach for Efficient Inventory Policy in Multi-Echelon Supply Chain Under Various Assumptions and Constraints." Journal of Information Systems Engineering and Business Intelligence 7, no. 2 (October 28, 2021): 138. http://dx.doi.org/10.20473/jisebi.7.2.138-148.
Full textZeng, Weixin, Xiang Zhao, Jiuyang Tang, Xuemin Lin, and Paul Groth. "Reinforcement Learning–based Collective Entity Alignment with Adaptive Features." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–31. http://dx.doi.org/10.1145/3446428.
Full textTrella, Anna L., Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, and Susan A. Murphy. "Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines." Algorithms 15, no. 8 (July 22, 2022): 255. http://dx.doi.org/10.3390/a15080255.
Full textKoo, Seolwon, and Yujin Lim. "A Cluster-Based Optimal Computation Offloading Decision Mechanism Using RL in the IIoT Field." Applied Sciences 12, no. 1 (December 31, 2021): 384. http://dx.doi.org/10.3390/app12010384.
Full textLiang, Di, Rui Yin, and Rong Peng Liu. "Project Management Plan's Research and Application under Resource Constraints." Applied Mechanics and Materials 687-691 (November 2014): 4790–93. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4790.
Full textLobbezoo, Andrew, and Hyock-Ju Kwon. "Simulated and Real Robotic Reach, Grasp, and Pick-and-Place Using Combined Reinforcement Learning and Traditional Controls." Robotics 12, no. 1 (January 16, 2023): 12. http://dx.doi.org/10.3390/robotics12010012.
Full textXu, Feng, Shengyi Jiang, Hao Yin, Zongzhang Zhang, Yang Yu, Ming Li, Dong Li, and Wulong Liu. "Enhancing Context-Based Meta-Reinforcement Learning Algorithms via An Efficient Task Encoder (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15937–38. http://dx.doi.org/10.1609/aaai.v35i18.17965.
Full textXu, Yuan, Xinyu Fu, Thomas D. Sharkey, Yair Shachar-Hill, and and Berkley J. Walker. "The metabolic origins of non-photorespiratory CO2 release during photosynthesis: a metabolic flux analysis." Plant Physiology 186, no. 1 (February 16, 2021): 297–314. http://dx.doi.org/10.1093/plphys/kiab076.
Full textIbarz, Julian, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, and Sergey Levine. "How to train your robot with deep reinforcement learning: lessons we have learned." International Journal of Robotics Research 40, no. 4-5 (January 31, 2021): 698–721. http://dx.doi.org/10.1177/0278364920987859.
Full textXu, Shihao, Yingzi Guan, Changzhu Wei, Yulong Li, and Lei Xu. "Reinforcement-Learning-Based Tracking Control with Fixed-Time Prescribed Performance for Reusable Launch Vehicle under Input Constraints." Applied Sciences 12, no. 15 (July 24, 2022): 7436. http://dx.doi.org/10.3390/app12157436.
Full textLanglois, Eric D., and Tom Everitt. "How RL Agents Behave When Their Actions Are Modified." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11586–94. http://dx.doi.org/10.1609/aaai.v35i13.17378.
Full textZeng, Junjie, Long Qin, Yue Hu, Cong Hu, and Quanjun Yin. "Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder." Applied Sciences 9, no. 2 (January 17, 2019): 323. http://dx.doi.org/10.3390/app9020323.
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