Relevant bibliographies by topics / Artificial intelligence. Machine learning. Reinforcement learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial intelligence. Machine learning. Reinforcement learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Artificial intelligence. Machine learning. Reinforcement learning"

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Kaelbling, L. P., M. L. Littman, and A. W. Moore. "Reinforcement Learning: A Survey." Journal of Artificial Intelligence Research 4 (May 1, 1996): 237–85. http://dx.doi.org/10.1613/jair.301.

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This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinfo
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Evseenko, Alla, and Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.

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Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is m
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Barash, Guy, Mauricio Castillo-Effen, Niyati Chhaya, et al. "Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence." AI Magazine 40, no. 3 (2019): 67–78. http://dx.doi.org/10.1609/aimag.v40i3.4981.

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The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health I
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Suzuki, Kenji. "AI: A New Open Access Journal for Artificial Intelligence." AI 1, no. 2 (2020): 141–42. http://dx.doi.org/10.3390/ai1020007.

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As a branch of computer science, artificial intelligence (AI) attempts to understand the essence of intelligence, and produce new kinds of intelligent machines that can respond in a similar way to human intelligence, with broad research areas of machine and deep learning, data science, reinforcement learning, data mining, knowledge discovery, knowledge reasoning, speech recognition, natural language processing, language recognition, image recognition, computer vision, planning, robotics, gaming, and so on [...]
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Hashimoto, Daniel A., Elan Witkowski, Lei Gao, Ozanan Meireles, and Guy Rosman. "Artificial Intelligence in Anesthesiology." Anesthesiology 132, no. 2 (2020): 379–94. http://dx.doi.org/10.1097/aln.0000000000002960.

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Abstract Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (includi
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Qin, Yao, Hua Wang, Shanwen Yi, Xiaole Li, and Linbo Zhai. "Virtual machine placement based on multi-objective reinforcement learning." Applied Intelligence 50, no. 8 (2020): 2370–83. http://dx.doi.org/10.1007/s10489-020-01633-3.

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Orgován, László, Tamás Bécsi, and Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning." Periodica Polytechnica Transportation Engineering 49, no. 3 (2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.

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Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. Th
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Calabuig, J. M., H. Falciani, and E. A. Sánchez-Pérez. "Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets." Neurocomputing 398 (July 2020): 172–84. http://dx.doi.org/10.1016/j.neucom.2020.02.052.

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Meng, Terry Lingze, and Matloob Khushi. "Reinforcement Learning in Financial Markets." Data 4, no. 3 (2019): 110. http://dx.doi.org/10.3390/data4030110.

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Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and
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Bakakeu, Jupiter, Schirin Tolksdorf, Jochen Bauer, et al. "An Artificial Intelligence Approach for Online Optimization of Flexible Manufacturing Systems." Applied Mechanics and Materials 882 (July 2018): 96–108. http://dx.doi.org/10.4028/www.scientific.net/amm.882.96.

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This paper addresses the problem of efficiently operating a flexible manufacturing machine in an electricity micro-grid featuring a high volatility of electricity prices. The problem of finding the optimal control policy is formulated as a sequential decision making problem under uncertainty where, at every time step the uncertainty comes from the lack of knowledge about fu-ture electricity consumption and future weather dependent energy prices. We propose to address this problem using deep reinforcement learning. To this purpose, we designed a deep learning architecture to forecast the load p
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Dissertations / Theses on the topic "Artificial intelligence. Machine learning. Reinforcement learning"

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Ceylan, Hakan. "Using Reinforcement Learning in Partial Order Plan Space." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5232/.

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Partial order planning is an important approach that solves planning problems without completely specifying the orderings between the actions in the plan. This property provides greater flexibility in executing plans; hence making the partial order planners a preferred choice over other planning methodologies. However, in order to find partially ordered plans, partial order planners perform a search in plan space rather than in space of world states and an uninformed search in plan space leads to poor efficiency. In this thesis, I discuss applying a reinforcement learning method, called First-
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Mitchell, Matthew Winston 1968. "An architecture for situated learning agents." Monash University, School of Computer Science and Software Engineering, 2003. http://arrow.monash.edu.au/hdl/1959.1/5553.

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Qi, Dehu. "Multi-agent systems : integrating reinforcement learning, bidding and genetic algorithms /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3060133.

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Yang, Zhaoyuan Yang. "Adversarial Reinforcement Learning for Control System Design: A Deep Reinforcement Learning Approach." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu152411491981452.

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Beretta, Davide. "Experience Replay in Sparse Rewards Problems using Deep Reinforcement Techniques." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17531/.

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In questo lavoro si introduce il lettore al Reinforcement Learning, un'area del Machine Learning su cui negli ultimi anni è stata fatta molta ricerca. In seguito vengono presentate alcune modifiche ad ACER, un algoritmo noto e molto interessante che fa uso di Experience Replay. Lo scopo è quello di cercare di aumentarne le performance su problemi generali ma in particolar modo sugli sparse reward problem. Per verificare la bontà delle idee proposte è utilizzato Montezuma's Revenge, un gioco sviluppato per Atari 2600 e considerato tra i più difficili da trattare.
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Cleland, Benjamin George. "Reinforcement Learning for Racecar Control." The University of Waikato, 2006. http://hdl.handle.net/10289/2507.

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This thesis investigates the use of reinforcement learning to learn to drive a racecar in the simulated environment of the Robot Automobile Racing Simulator. Real-life race driving is known to be difficult for humans, and expert human drivers use complex sequences of actions. There are a large number of variables, some of which change stochastically and all of which may affect the outcome. This makes driving a promising domain for testing and developing Machine Learning techniques that have the potential to be robust enough to work in the real world. Therefore the principles of the algorithm
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Lundin, Lowe. "Artificial Intelligence for Data Center Power Consumption Optimisation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447627.

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The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3"
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Kostias, Aristotelis, and Georgios Tagkoulis. "Development of an Artificial Intelligent Software Agent using Artificial Intelligence and Machine Learning Techniques to play Backgammon Variants." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251923.

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Artificial Intelligence has seen enormous progress in many disciplines in the recent years. Particularly, digitalized versions of board games require artificial intelligence application due to their complex decision-making environment. Game developers aim to create board game software agents which are intelligent, adaptive and responsive. However, the process of designing and developing such a software agent is far from straight forward due the nature and diversity of each game. The thesis examines and presents a detailed procedure of constructing a software agent for backgammon variants, usin
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Elvira, Boman. "Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change : Using Artificial Intelligence to Adapt Communities to Climate Change." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-373502.

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The consequences of climate change are already noticeable in small island developing states. Road networks are crucial for a functioning society, and are particularly vulnerable to extreme weather, floods, landslides and other effects of climate change. Road systems in small island developing states are therefore in special need of climate adaptation efforts. Climate adaptation of road systems also has to be cost-efficient since these small island states have limited economical resources. Recent advances in deep reinforcement learning, a subfield of artificial intelligence, has proven that int
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Lundström, Caroline, and Sara Hedberg. "Coordinating transportation services in a hospital environment using Deep Reinforcement Learning." Thesis, Uppsala universitet, Avdelningen för datalogi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355737.

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Artificial Intelligence has in the recent years become a popular subject, many thanks to the recent progress in the area of Machine Learning and particularly to the achievements made using Deep Learning. When combining Reinforcement Learning and Deep Learning, an agent can learn a successful behavior for a given environment. This has opened the possibility for a new domain of optimization. This thesis evaluates if a Deep Reinforcement Learning agent can learn to aid transportation services in a hospital environment. A Deep Q-learning Networkalgorithm (DQN) is implemented, and the performance i
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Books on the topic "Artificial intelligence. Machine learning. Reinforcement learning"

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Sutton, Richard S. Reinforcement Learning. Springer US, 1992.

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Merrick, Kathryn E. Motivated reinforcement learning: Curious characters for multiuser games. Springer, 2009.

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Lou, Maher Mary, ed. Motivated reinforcement learning: Curious characters for multiuser games. Springer, 2009.

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Rieser, Verena. Reinforcement Learning for Adaptive Dialogue Systems: A Data-driven Methodology for Dialogue Management and Natural Language Generation. Springer-Verlag Berlin Heidelberg, 2011.

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Joshi, Ameet V. Machine Learning and Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-26622-6.

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Bogaerts, Bart, Gianluca Bontempi, Pierre Geurts, et al., eds. Artificial Intelligence and Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65154-1.

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Baratchi, Mitra, Lu Cao, Walter A. Kosters, Jefrey Lijffijt, Jan N. van Rijn, and Frank W. Takes, eds. Artificial Intelligence and Machine Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76640-5.

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Wiering, Marco. Reinforcement Learning: State-of-the-Art. Springer Berlin Heidelberg, 2012.

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BOOKS, Editors of TIME-LIFE. Artificial intelligence. Edited by Time-Life Books. Time-Life Books, 1991.

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Ramanna, Sheela. Emerging Paradigms in Machine Learning. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Artificial intelligence. Machine learning. Reinforcement learning"

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Joshi, Ameet V. "Dynamic Programming and Reinforcement Learning." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_9.

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Rahimi Gorji, Saeed, Ole-Christoffer Granmo, and Marco Wiering. "Explainable Reinforcement Learning with the Tsetlin Machine." In Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79457-6_15.

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Arel, Itamar. "Deep Reinforcement Learning as Foundation for Artificial General Intelligence." In Atlantis Thinking Machines. Atlantis Press, 2012. http://dx.doi.org/10.2991/978-94-91216-62-6_6.

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Ponce, Hiram, and Ricardo Padilla. "A Hierarchical Reinforcement Learning Based Artificial Intelligence for Non-Player Characters in Video Games." In Nature-Inspired Computation and Machine Learning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13650-9_16.

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Raj, Ritwik, and Anjana Mishra. "Machine Learning for Big Data Analytics, Interactive and Reinforcement." In Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches. CRC Press, 2020. http://dx.doi.org/10.1201/9780367854737-13.

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Aggarwal, Charu C. "Reinforcement Learning." In Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72357-6_10.

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Pérez Castaño, Arnaldo. "Reinforcement Learning." In Practical Artificial Intelligence. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3357-3_17.

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Michalewicz, Zbigniew. "Machine Learning." In Artificial Intelligence. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02830-8_13.

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Taulli, Tom. "Machine Learning." In Artificial Intelligence Basics. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5028-0_3.

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Sigaud, Olivier, and Frédérick Garcia. "Reinforcement Learning." In Markov Decision Processes in Artificial Intelligence. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557426.ch2.

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Conference papers on the topic "Artificial intelligence. Machine learning. Reinforcement learning"

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Bai, Aijun, and Stuart Russell. "Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/196.

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In the context of hierarchical reinforcement learning, the idea of hierarchies of abstract machines (HAMs) is to write a partial policy as a set of hierarchical finite state machines with unspecified choice states, and use reinforcement learning to learn an optimal completion of this partial policy. Given a HAM with potentially deep hierarchical structure, there often exist many internal transitions where a machine calls another machine with the environment state unchanged. In this paper, we propose a new hierarchical reinforcement learning algorithm that discovers such internal transitions au
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Morcos, Amir, Aaron West, and Brian Maguire. "Multi-agent reinforcement learning for convex optimization." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2585624.

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de Heer, Paolo, Nico de Reus, Lucia Tealdi, and Philip Kerbusch. "Intelligence augmentation for urban warfare operation planning using deep reinforcement learning." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, edited by Tien Pham. SPIE, 2019. http://dx.doi.org/10.1117/12.2520051.

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Salaymeh, Areej, Loren Schwiebert, and Stephen Remias. "Multi-Agent Reinforcement Learning for Optimizing Traffic Signal Timing." In 8th International Conference on Artificial Intelligence and Applications (AIAP 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110102.

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Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial In
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Cam, Hasan. "Cyber resilience using autonomous agents and reinforcement learning." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, edited by Tien Pham, Latasha Solomon, and Katie Rainey. SPIE, 2020. http://dx.doi.org/10.1117/12.2559319.

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Zaroukian, Erin G., Anjon Basak, Piyush K. Sharma, Rolando Fernandez, and Derrik E. Asher. "Emergent reinforcement learning behaviors through novel testing conditions." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2585627.

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Sultana, Madeena, Adrian Taylor, and Li Li. "Autonomous network cyber offence strategy through deep reinforcement learning." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2585173.

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Zhang, Daniel, and Colleen P. Bailey. "Obstacle avoidance and navigation utilizing reinforcement learning with reward shaping." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, edited by Tien Pham, Latasha Solomon, and Katie Rainey. SPIE, 2020. http://dx.doi.org/10.1117/12.2558212.

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Serrano, Sergio A. "Inter-Task Similarity for Lifelong Reinforcement Learning in Heterogeneous Tasks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/689.

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Reinforcement learning (RL) is a learning paradigm in which an agent interacts with the environment it inhabits to learn in a trial-and-error way. By letting the agent acquire knowledge from its own experience, RL has been successfully applied to complex domains such as robotics. However, for non-trivial problems, training an RL agent can take very long periods of time. Lifelong machine learning (LML) is a learning setting in which the agent learns to solve tasks sequentially, by leveraging knowledge accumulated from previously solved tasks to learn better/faster in a new one. Most LML works h
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Sharma, Piyush K., Erin G. Zaroukian, Rolando Fernandez, Anjon Basak, and Derrik E. Asher. "Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2585808.

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Reports on the topic "Artificial intelligence. Machine learning. Reinforcement learning"

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Byrd, Lexie, Curtis Smith, Ross Kunz, et al. Big Data, Machine Learning, Artificial Intelligence [PowerPoint]. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1617329.

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Randerson, James, Efi Georgiou, Padhraic Smyth, et al. Machine learning and artificial intelligence for wildfire prediction. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769739.

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Vecherin, Sergey, Jacob Desmond, Taylor Hodgdon, et al. Artificial intelligence and machine learning for autonomous military vehicles. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/37943.

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Milgrom, Paul, and Steven Tadelis. How Artificial Intelligence and Machine Learning Can Impact Market Design. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24282.

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Baker, Nathan, Frank Alexander, Timo Bremer, et al. Brochure on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1484362.

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Aboaba, A., Y. Martinez, S. Mohaghegh, M. Shahnam, C. Guenther, and Y. Liu. Smart Proxy Modeling Application of Artificial Intelligence & Machine Learning in Computational Fluid Dynamics. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1642460.

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Baker, Nathan, Frank Alexander, Timo Bremer, et al. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1478744.

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Ratner, Daniel, Bobby Sumpter, Frank Alexander, et al. BES Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1630823.

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Ali, Alee. From the Starship Enterprise to Los Alamos National Laboratory Artificial Intelligence and Machine Learning in the NSRC. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1760557.

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None, None. Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1734848.

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