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Добірка наукової літератури з теми "Safe Reinforcement Learning"

Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Safe Reinforcement Learning"

1

Horie, Naoto, Tohgoroh Matsui, Koichi Moriyama, Atsuko Mutoh, and Nobuhiro Inuzuka. "Multi-objective safe reinforcement learning: the relationship between multi-objective reinforcement learning and safe reinforcement learning." Artificial Life and Robotics 24, no. 3 (2019): 352–59. http://dx.doi.org/10.1007/s10015-019-00523-3.

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2

Yang, Yongliang, Kyriakos G. Vamvoudakis, and Hamidreza Modares. "Safe reinforcement learning for dynamical games." International Journal of Robust and Nonlinear Control 30, no. 9 (2020): 3706–26. http://dx.doi.org/10.1002/rnc.4962.

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3

Xu, 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 (2022): 8753–60. http://dx.doi.org/10.1609/aaai.v36i8.20855.

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Анотація:

We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is more appealing for real world RL applications, in which data collection is costly or dangerous. Enforcing constraint satisfaction is non-trivial, especially in offline settings, as there is a potential large discrepancy between the policy distribution and the data distribution, causing errors in estimating the value of safety constraints. We s

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4

García, Javier, and Fernando Fernández. "Probabilistic Policy Reuse for Safe Reinforcement Learning." ACM Transactions on Autonomous and Adaptive Systems 13, no. 3 (2019): 1–24. http://dx.doi.org/10.1145/3310090.

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5

Mannucci, Tommaso, Erik-Jan van Kampen, Cornelis de Visser, and Qiping Chu. "Safe Exploration Algorithms for Reinforcement Learning Controllers." IEEE Transactions on Neural Networks and Learning Systems 29, no. 4 (2018): 1069–81. http://dx.doi.org/10.1109/tnnls.2017.2654539.

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Karthikeyan, P., Wei-Lun Chen, and Pao-Ann Hsiung. "Autonomous Intersection Management by Using Reinforcement Learning." Algorithms 15, no. 9 (2022): 326. http://dx.doi.org/10.3390/a15090326.

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Анотація:

Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent control system to reduce traffic accidents. We recommend deep reinforcement learning-inspired autonomous intersection management (DRLAIM) to improve traffic environment efficiency and safety. The three primary models used in this methodology are the priority assignment model, the intersection-control model learning, and safe brake control. The brake

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7

Mazouchi, Majid, Subramanya Nageshrao, and Hamidreza Modares. "Conflict-Aware Safe Reinforcement Learning: A Meta-Cognitive Learning Framework." IEEE/CAA Journal of Automatica Sinica 9, no. 3 (2022): 466–81. http://dx.doi.org/10.1109/jas.2021.1004353.

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Cowen-Rivers, Alexander I., Daniel Palenicek, Vincent Moens, et al. "SAMBA: safe model-based & active reinforcement learning." Machine Learning 111, no. 1 (2022): 173–203. http://dx.doi.org/10.1007/s10994-021-06103-6.

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Serrano-Cuevas, Jonathan, Eduardo F. Morales, and Pablo Hernández-Leal. "Safe reinforcement learning using risk mapping by similarity." Adaptive Behavior 28, no. 4 (2019): 213–24. http://dx.doi.org/10.1177/1059712319859650.

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Анотація:

Reinforcement learning (RL) has been used to successfully solve sequential decision problem. However, considering risk at the same time as the learning process is an open research problem. In this work, we are interested in the type of risk that can lead to a catastrophic state. Related works that aim to deal with risk propose complex models. In contrast, we follow a simple, yet effective, idea: similar states might lead to similar risk. Using this idea, we propose risk mapping by similarity (RMS), an algorithm for discrete scenarios which infers the risk of newly discovered states by analyzin

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Andersen, Per-Arne, Morten Goodwin, and Ole-Christoffer Granmo. "Towards safe reinforcement-learning in industrial grid-warehousing." Information Sciences 537 (October 2020): 467–84. http://dx.doi.org/10.1016/j.ins.2020.06.010.

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Дисертації з теми "Safe Reinforcement Learning"

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Magnusson, Björn, and Måns Forslund. "SAFE AND EFFICIENT REINFORCEMENT LEARNING." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-76588.

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Анотація:

Pre-programming a robot may be efficient to some extent, but since a human has code the robot it will only be as efficient as the programming. The problem can solved by using machine learning, which lets the robot learn the most efficient way by itself. This thesis is continuation of a previous work that covered the development of the framework Safe-To-Explore-State-Spaces (STESS) for safe robot manipulation. This thesis evaluates the efficiency of the Q-Learning with normalized advantage function (NAF), a deep reinforcement learning algorithm, when integrated with the safety framework ST

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2

Mason, George. "Safe reinforcement learning using formally verified abstract policies." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/22450/.

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Анотація:

Reinforcement learning (RL) is an artificial intelligence technique for finding optimal solutions for sequential decision-making problems modelled as Markov decision processes (MDPs). Objectives are represented as numerical rewards in the model where positive values represent achievements and negative values represent failures. An autonomous agent explores the model to locate rewards with the goal to learn behaviour which will cumulate the largest reward possible. Despite RL successes in applications ranging from robotics and planning systems to sensing, it has so far had little appeal in miss

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Iakovidis, Grigorios. "Safe Reinforcement Learning for Remote Electrical Tilt Optimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294161.

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Анотація:

The adjustment of the vertical tilt angle of Base Station (BS) antennas, also known as Remote Electrical Tilt (RET) optimization, is a simple and efficient method of optimizing modern telecommunications networks. Reinforcement Learning (RL) is a machine learning framework that can solve complex problems like RET optimization due to its capability to learn from experience and adapt to dynamic environments. However, conventional RL methods involve trial-and-error processes which can result in short periods of poor network performance which is unacceptable to mobile network operators. This unreli

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Geramifard, Alborz 1980. "Practical reinforcement learning using representation learning and safe exploration for large scale Markov decision processes." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/71455.

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Анотація:

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 157-168).<br>While creating intelligent agents who can solve stochastic sequential decision making problems through interacting with the environment is the promise of Reinforcement Learning (RL), scaling existing RL methods to realistic domains such as planning for multiple unmanned aerial vehicles (UAVs) has remained a challenge due to three main factors: 1) RL methods often require a plethora of data to find r

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Heidenreich, Caroline. "Safe learning for control: Combining disturbance estimation, reachability analysis and reinforcement learning with systematic exploration." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214080.

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Анотація:

Learning to control an uncertain system is a problem with a plethora ofapplications in various engineering elds. In the majority of practical scenarios,one wishes that the learning process terminates quickly and does not violatesafety limits on key variables. It is particularly appealing to learn the controlpolicy directly from experiments, since this eliminates the need to rst derivean accurate physical model of the system. The main challenge when using suchan approach is to ensure safety constraints during the learning process.This thesis investigates an approach to safe learning that relies

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Ohnishi, Motoya. "Safey-aware Adaptive Reinforcement Learning with Applications to Brushbot Navigation." Thesis, KTH, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226591.

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This thesis presents a safety-aware learning framework that employs an adaptivemodel learning method together with barrier certificates for systems withpossibly nonstationary agent dynamics. To extract the dynamic structure ofthe model, we use a sparse optimization technique, and the resulting modelwill be used in combination with control barrier certificates which constrainfeedback controllers only when safety is about to be violated. Under somemild assumptions, solutions to the constrained feedback-controller optimizationare guaranteed to be globally optimal, and the monotonic improvementof

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Ho, Chang-An, and 何長安. "Safe Reinforcement Learning based Sequential Perturbation Learning Algorithm." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/63234750154932788712.

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碩士<br>國立交通大學<br>電機與控制工程系所<br>97<br>This article is about sequential perturbation learning architecture through safe reinforcement learning (SRL-SP) which based on the concept of linear search to apply perturbations on each weight value of the neural network. The evaluation of value of function between pre-perturb and post-perturb network is executed after the perturbations are applied, so as to update the weights. Applying perturbations can avoid the solution form the phenomenon which falls into the hands of local solution and oscillating in the solution space that decreases the learning effic

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Everitt, Tom. "Towards Safe Artificial General Intelligence." Phd thesis, 2018. http://hdl.handle.net/1885/164227.

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Анотація:

The field of artificial intelligence has recently experienced a number of breakthroughs thanks to progress in deep learning and reinforcement learning. Computer algorithms now outperform humans at Go, Jeopardy, image classification, and lip reading, and are becoming very competent at driving cars and interpreting natural language. The rapid development has led many to conjecture that artificial intelligence with greater-than-human ability on a wide range of tasks may not be far. This in turn raises concerns whether we know how to control such systems, in

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Jayant, Ashish. "Model-based Safe Deep Reinforcement Learning and Empirical Analysis of Safety via Attribution." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5849.

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Анотація:

During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform a significant number of random exploratory steps, which in the real-world limit the practicality of these algorithms as this can lead to potentially dangerous behavior. Hence safe exploration is a critical issue in applying RL algorithms in the real world. This problem is well studied in the literature under the Constrained Markov Decision Process (CMDP) Framework, where in addition to single-stage rewards, state transitions receive single-stage costs as well. The prescribed cost function

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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.

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博士<br>國立交通大學<br>電機與控制工程系所<br>97<br>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

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Книги з теми "Safe Reinforcement Learning"

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Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes

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Частини книг з теми "Safe Reinforcement Learning"

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Zhang, Jianyi, and Paul Weng. "Safe Distributional Reinforcement Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94662-3_8.

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Neufeld, Emery A., Ezio Bartocci, and Agata Ciabattoni. "On Normative Reinforcement Learning via Safe Reinforcement Learning." In PRIMA 2022: Principles and Practice of Multi-Agent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21203-1_5.

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Fulton, Nathan, and André Platzer. "Verifiably Safe Off-Model Reinforcement Learning." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17462-0_28.

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Bragg, John, and Ibrahim Habli. "What Is Acceptably Safe for Reinforcement Learning?" In Developments in Language Theory. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99229-7_35.

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Bacci, Edoardo, and David Parker. "Probabilistic Guarantees for Safe Deep Reinforcement Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57628-8_14.

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Cheng, Jiangchang, Fumin Yu, Hongliang Zhang, and Yinglong Dai. "Skill Reward for Safe Deep Reinforcement Learning." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0468-4_15.

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Liu, Shaofan, and Shiliang Sun. "Safe Offline Reinforcement Learning Through Hierarchical Policies." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05936-0_30.

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Cohen, Max, and Calin Belta. "Safe Exploration in Model-Based Reinforcement Learning." In Adaptive and Learning-Based Control of Safety-Critical Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29310-8_8.

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Pecka, Martin, and Tomas Svoboda. "Safe Exploration Techniques for Reinforcement Learning – An Overview." In Modelling and Simulation for Autonomous Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13823-7_31.

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Cohen, Max, and Calin Belta. "Temporal Logic Guided Safe Model-Based Reinforcement Learning." In Adaptive and Learning-Based Control of Safety-Critical Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29310-8_9.

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Тези доповідей конференцій з теми "Safe Reinforcement Learning"

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Padakandla, Sindhu, Prabuchandran K. J, Sourav Ganguly, and Shalabh Bhatnagar. "Data Efficient Safe Reinforcement Learning." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945313.

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Isele, David, Alireza Nakhaei, and Kikuo Fujimura. "Safe Reinforcement Learning on Autonomous Vehicles." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593420.

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Calvo-Fullana, Miguel, Luiz F. O. Chamon, and Santiago Paternain. "Towards Safe Continuing Task Reinforcement Learning." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9482748.

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4

Jia, Yan, John Burden, Tom Lawton, and Ibrahim Habli. "Safe Reinforcement Learning for Sepsis Treatment." In 2020 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2020. http://dx.doi.org/10.1109/ichi48887.2020.9374367.

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Yang, Tsung-Yen, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, and Wenhao Yu. "Safe Reinforcement Learning for Legged Locomotion." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9982038.

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Kim, Dohyeong, Jaeseok Heo, and Songhwai Oh. "SafeTAC: Safe Tsallis Actor-Critic Reinforcement Learning for Safer Exploration." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9982140.

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Yang, Wen-Chi, Giuseppe Marra, Gavin Rens, and Luc De Raedt. "Safe Reinforcement Learning via Probabilistic Logic Shields." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/637.

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Анотація:

Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to prevent an RL agent from taking unsafe actions. However, traditional shielding techniques are difficult to integrate with continuous, end-to-end deep RL methods. To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). PLPG is a model-based Safe RL technique that uses probabilistic logic programming to model logical safety constraints as differentiable functions. Therefore, PLPG can be seamlessly applie

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Rahman, Md Asifur, Tongtong Liu, and Sarra Alqahtani. "Adversarial Behavior Exclusion for Safe Reinforcement Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/54.

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Learning by exploration makes reinforcement learning (RL) potentially attractive for many real-world applications. However, this learning process makes RL inherently too vulnerable to be used in real-world applications where safety is of utmost importance. Most prior studies consider exploration at odds with safety and thereby restrict it using either joint optimization of task and safety or imposing constraints for safe exploration. This paper migrates from the current convention to using exploration as a key to safety by learning safety as a robust behavior that completely excludes any behav

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Umemoto, Takumi, Tohgoroh Matsui, Atsuko Mutoh, Koichi Moriyama, and Nobuhiro Inuzuka. "Safe Reinforcement Learning in Continuous State Spaces." In 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). IEEE, 2019. http://dx.doi.org/10.1109/gcce46687.2019.9014637.

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Yang, Yongliang, Kyriakos G. Vamvoudakis, Hamidreza Modares, Wei He, Yixin Yin, and Donald C. Wunsch. "Safe Intermittent Reinforcement Learning for Nonlinear Systems." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9030210.

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Звіти організацій з теми "Safe Reinforcement Learning"

1

Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were st

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