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Journal articles on the topic 'Reinforcement value'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Liu, Shiyi. "Research of Multi-agent Deep Reinforcement Learning based on Value Factorization." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 848–54. http://dx.doi.org/10.54097/hset.v39i.6655.

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One of the numerous multi-agents’ deep reinforcements learning methods and a hotspot for research in the field is multi-agent deep reinforcement learning based on value factorization. In order to effectively address the issues of environmental instability and the exponential expansion of action space in multi-agent systems, it uses some constraints to break down the joint action value function of the multi-agent system into a specific combination of individual action value functions. Firstly, in this paper, the reason for the factorization of value function is explained. The fundamentals of mu

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Li, Beining, Yimeng Lu, Yunhao Mo, and Weiqi Yu. "Playing Flappy Bird with Two Different Value Learning Algorithms." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 622–26. http://dx.doi.org/10.54097/hset.v39i.6608.

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In this paper, reinforcement learning will be applied to the game flappy bird with two methods DQN and Q-learning. Then, we compare the performance through the visualization of data. Furthermore, more results from other games are summarized to analysis the corresponding advantages and disadvantages. Finally, we discuss and compare these two reinforcements learning methods.

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3

Preston, Ray A., and Edmund Fantino. "CONDITIONED REINFORCEMENT VALUE AND CHOICE." Journal of the Experimental Analysis of Behavior 55, no. 2 (1991): 155–75. http://dx.doi.org/10.1901/jeab.1991.55-155.

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4

Williams, Ben A. "Behavioral contrast and reinforcement value." Animal Learning & Behavior 19, no. 4 (1991): 337–44. http://dx.doi.org/10.3758/bf03197894.

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Szepesvári, Csaba, and Michael L. Littman. "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms." Neural Computation 11, no. 8 (1999): 2017–60. http://dx.doi.org/10.1162/089976699300016070.

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Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning

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Pan, Yaozong, Jian Zhang, Chunhui Yuan, and Haitao Yang. "Supervised Reinforcement Learning via Value Function." Symmetry 11, no. 4 (2019): 590. http://dx.doi.org/10.3390/sym11040590.

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Using expert samples to improve the performance of reinforcement learning (RL) algorithms has become one of the focuses of research nowadays. However, in different application scenarios, it is hard to guarantee both the quantity and quality of expert samples, which prohibits the practical application and performance of such algorithms. In this paper, a novel RL decision optimization method is proposed. The proposed method is capable of reducing the dependence on expert samples via incorporating the decision-making evaluation mechanism. By introducing supervised learning (SL), our method optimi

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Grace, Randolph C., and Hernán I. Savastano. "Temporal context and conditioned reinforcement value." Journal of Experimental Psychology: General 129, no. 4 (2000): 427–43. http://dx.doi.org/10.1037/0096-3445.129.4.427.

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8

Buriticá, Jonathan, and Cristiano V. dos Santos. "Reinforcement value and fixed-interval performance." Journal of the Experimental Analysis of Behavior 108, no. 2 (2017): 151–70. http://dx.doi.org/10.1002/jeab.279.

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Wang, Sixuan, Cailong Ma, Wenhu Wang, et al. "Prediction of Failure Modes and Minimum Characteristic Value of Transverse Reinforcement of RC Beams Based on Interpretable Machine Learning." Buildings 13, no. 2 (2023): 469. http://dx.doi.org/10.3390/buildings13020469.

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Shear failure of reinforced concrete (RC) beams is a form of brittle failure and has always been a concern. This study adopted the interpretable machine-learning technique to predict failure modes and identify the boundary value between different failure modes to avoid diagonal splitting failure. An experimental database consisting of 295 RC beams with or without transverse reinforcements was established. Two features were constructed to reflect the design characteristics of RC beams, namely, the shear–span ratio and the characteristic value of transverse reinforcement. The characteristic valu

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Mavlonov, Ravshanbek, Sobirjon Razzakov, and Sohiba Numanova. "Stress-strain state of combined steel-FRP reinforced concrete beams." E3S Web of Conferences 452 (2023): 06022. http://dx.doi.org/10.1051/e3sconf/202345206022.

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Steel reinforcements in reinforced concrete structures are susceptible to corrosion under different exposure conditions. This can lead to some disadvantages, including concrete deterioration, reduced long-term service life, increased cost of the structure due to re-strengthening measures, and reduced overall durability of the structure. In order to solve these problems, the issue of comprehensive use of Fiber reinforced polymer (FRP) reinforcements as an alternative to steel bars is urgent. FRP reinforcements have specific advantages including corrosion resistance, high tensile strength, densi

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Alzahri, Syahril, Adiguna, Bimo Brata Adhitya, Yulindasari Sutejo, and Reffanda Kurniawan Rustam. "Kajian Stabilitas Lereng dengan Perkuatan Geotekstil dan Dinding Penahan Tanah Kantilever di Ruas Jalan Padang-Lb. Selasih Sumatera Barat." Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil 9, no. 1 (2020): 15–24. http://dx.doi.org/10.35139/cantilever.v9i1.18.

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A typical relatively steep slope makes the Lb. Selasih – Bts. Kota Padang KM.29+650 experienced a landslide in 2017. So, it is necessary to strengthen the slope to overcome the landslide. Alternative slope reinforcement used is reinforcement using cantilever retaining walls or geotextiles. Slope stability analysis before and after were analyzed using the Slope/W program. The output produced by Slope/W program is the value of the safety factor. The safety factor value for the state of the original slope is 1.100. It shows that the slope in the original condition is unstable and vulnerable to la

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McDevitt, Margaret A., and Ben A. Williams. "DUAL EFFECTS ON CHOICE OF CONDITIONED REINFORCEMENT FREQUENCY AND CONDITIONED REINFORCEMENT VALUE." Journal of the Experimental Analysis of Behavior 93, no. 2 (2010): 147–55. http://dx.doi.org/10.1901/jeab.2010.93-147.

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13

Tamashima, Daisuke, Seiichi Koakutsu, Takashi Okamoto, and Hironori Hirata. "Profit Sharing Using a Dynamic Reinforcement Function Considering Expectation Value of Reinforcement." IEEJ Transactions on Electronics, Information and Systems 129, no. 7 (2009): 1339–47. http://dx.doi.org/10.1541/ieejeiss.129.1339.

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14

Alessandri, Jérôme, Carlos R. X. Cançado, and Josele Abreu-Rodrigues. "Effects of reinforcement value on instruction following under schedules of negative reinforcement." Behavioural Processes 145 (December 2017): 27–30. http://dx.doi.org/10.1016/j.beproc.2017.10.003.

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15

Ptasczynski, Lena Esther, Isa Steinecker, Philipp Sterzer, and Matthias Guggenmos. "The value of confidence: Confidence prediction errors drive value-based learning in the absence of external feedback." PLOS Computational Biology 18, no. 10 (2022): e1010580. http://dx.doi.org/10.1371/journal.pcbi.1010580.

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Reinforcement learning algorithms have a long-standing success story in explaining the dynamics of instrumental conditioning in humans and other species. While normative reinforcement learning models are critically dependent on external feedback, recent findings in the field of perceptual learning point to a crucial role of internally-generated reinforcement signals based on subjective confidence, when external feedback is not available. Here, we investigated the existence of such confidence-based learning signals in a key domain of reinforcement-based learning: instrumental conditioning. We c

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Okokpujie, Imhade Princess, Lagouge Kwanda Tartibu, and Rajneesh K. Singh. "Development of an Aluminium-based Composite Reinforced with Various Concentrations of 600 Microns of Alumina Particulates for Engineering Applications." Journal of Advanced Research in Micro and Nano Engineering 31, no. 1 (2025): 1–22. https://doi.org/10.37934/armne.31.1.122.

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A popular and cost-effective method for creating and processing metal matrix composite materials is the stir casting process, which produces casting alloy-based composite components made of aluminium. This study entails the development of an aluminium matrix composite reinforced with alumina particles, with the Matrix being the Al6061 alloy and the reinforcement Al2O3 particles, with a particle size of 600 microns. The originality of this study is on the formation of the mixing ratio of the 600 microns in the composite development. The fabrication method was the stir casting technique to enabl

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Olusunmade, Olusola Femi, Abba Emmanuel Bulus, and Terwase Kelvin Kashin. "EFFECT OF IMPERATA CYLINDRICA REINFORCEMENT FORM ON THE TENSILE AND IMPACT PROPERTIES OF ITS COMPOSITES WITH RECYCLED LOW DENSITY POLYETHYLENE." Acta Polytechnica 58, no. 5 (2018): 292. http://dx.doi.org/10.14311/ap.2018.58.0292.

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Composites of recycled low-density polyethylene obtained from waste water-sachets and imperata cylindrica were produced with particulate and long-fibre unidirectional mat reinforcements. Comparison was made of the tensile and impact properties resulting from the use of the different reinforcement forms at 10 wt% ratio in the matrix. The results obtained from the tests carried out revealed that tensile strength, tensile modulus, elongation at break and impact strength of the composite with the long-fibre mat reinforcement were better than those of the one composite with the particulate reinforc

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18

Shahan, Timothy A., and Christopher A. Podlesnik. "CONDITIONED REINFORCEMENT VALUE AND RESISTANCE TO CHANGE." Journal of the Experimental Analysis of Behavior 89, no. 3 (2008): 263–98. http://dx.doi.org/10.1901/jeab.2008-89-263.

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19

Skoruk, L. "VALUE OF REINFORCEMENT IN CONCRETE MONOLITHIC STRUCTURES." Building constructions. Theory and Practice 1, no. 1 (2017): 144–48. http://dx.doi.org/10.32347/2522-4182.1.2017.144-148.

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20

Littman, Michael L. "Value-function reinforcement learning in Markov games." Cognitive Systems Research 2, no. 1 (2001): 55–66. http://dx.doi.org/10.1016/s1389-0417(01)00015-8.

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21

Hu, Yujing, Yang Gao, and Bo An. "Multiagent Reinforcement Learning With Unshared Value Functions." IEEE Transactions on Cybernetics 45, no. 4 (2015): 647–62. http://dx.doi.org/10.1109/tcyb.2014.2332042.

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22

Adediran, Adeolu Adesoji, Francis Odikpo Edoziuno, Olanrewaju Seun Adesina, et al. "Mechanical Characterization and Numerical Optimization of Aluminum Matrix Hybrid Composite." Materials Science Forum 1065 (June 30, 2022): 47–57. http://dx.doi.org/10.4028/p-m21wne.

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Hybridization of aluminium matrix composite is with a view to offset the properties deficient in one composite reinforcement. The present investigation involves a comparative study of AA6063 matrix composites with single reinforcement of Al2O3, SiC, graphene respectively and various hybridized proportions of the same reinforcements. Physical (density and %porosity) and mechanical (tensile strength, fracture toughness, %elongation, elastic modulus, etc.) properties of composites developed via solidification processing technique were evaluated. The porosity of all the composites falls below the

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23

Morimoto, Jun, and Kenji Doya. "Robust Reinforcement Learning." Neural Computation 17, no. 2 (2005): 335–59. http://dx.doi.org/10.1162/0899766053011528.

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This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both off-line learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H∞ control, we consider a differential game in which a “disturbing” agent tries to make the worst possible disturbance while a “control” agent tries to make the best control inp

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Rigley, Eryn, Adriane Chapman, Christine Evers, and Will McNeill. "ME: Modelling Ethical Values for Value Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27608–16. https://doi.org/10.1609/aaai.v39i26.34974.

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Value alignment, at the intersection of moral philosophy and AI safety, is dedicated to ensuring that artificially intelligent (AI) systems align with a certain set of values. One challenge facing value alignment researchers is accurately translating these values into a machine readable format. In the case of reinforcement learning (RL), a popular method within value alignment, this requires designing a reward function which accurately defines the value of all state-action pairs. It is common for programmers to hand-set and manually tune these values. In this paper, we examine the challenges o

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25

Wang, Yantao, Guangqing Yang, Lei Wang, Xujia Li, and Guomu Jiao. "Experimental Study on Reinforcement Properties of Tension-Resistant Reinforced Soil Retaining Wall." Buildings 14, no. 9 (2024): 2951. http://dx.doi.org/10.3390/buildings14092951.

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The tensioned reinforced soil retaining wall, a novel retaining structure, utilizes either anchors or geosynthetic materials as reinforcements that contribute to load-bearing and friction within the structure. This study aims to explore the tension distribution and strain patterns in the reinforcements, and their influence on the reinforced soil retaining walls. To this end, tensile, direct shear, and pullout tests were conducted on GeoStrap@5-50 geotextile strips and TGDG130HDPE geogrids to evaluate the tensile strength and interface strength between the reinforcement and the soil. The charac

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Zang, Xinshi, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, and Zhenhui Li. "MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1153–60. http://dx.doi.org/10.1609/aaai.v34i01.5467.

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Using reinforcement learning for traffic signal control has attracted increasing interests recently. Various value-based reinforcement learning methods have been proposed to deal with this classical transportation problem and achieved better performances compared with traditional transportation methods. However, current reinforcement learning models rely on tremendous training data and computational resources, which may have bad consequences (e.g., traffic jams or accidents) in the real world. In traffic signal control, some algorithms have been proposed to empower quick learning from scratch,

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27

Farhan Heryo Nugroho and Muhammad Zaki. "THE MODEL ANALYSIS OF REINFORCED BAMBOO WOVEN FOR SHALLOW FOUNDATION SUPPORTING BEARING CAPACITY ON SOFT SOIL." International Journal on Livable Space 7, no. 2 (2023): 53–58. http://dx.doi.org/10.25105/livas.v7i2.16806.

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North Jakarta generally has soft soil characteristics. Construction built on soft soil is a big problem due to the low bearing capacity of the soil and the large settlement. Therefore, a reinforcement is needed with the aim of increasing the carrying capacity of the soil. In this study using woven bamboo reinforcement, the use of this reinforcement can be an alternative to increase the bearing capacity of the soil used as the basis for shallow foundations. Variations in depth and number of layers of reinforcement are used to obtain the maximum value of the soil bearing capacity. The depth vari

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Lecarpentier, Erwan, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, and Michael L. Littman. "Lipschitz Lifelong Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8270–78. http://dx.doi.org/10.1609/aaai.v35i9.17006.

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We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes and establish that close MDPs have close optimal value functions. Formally, the optimal value functions are Lipschitz continuous with respect to the tasks space. These theoretical results lead us to a value-transfer method for Lifelong RL, which we use to build a PAC-MDP algorithm with improved convergence rate. Further, we show the method to experience no negative transfer with high probability. We illustrate the ben

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Gowtham, Mr M., Dr G. Rajkumar, and Mr P. Madessh. "An Investigation of Mechanical and Tribological Properties of Heat Treated Al6082 Hybrid Metal Matrix Composites." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1218–22. http://dx.doi.org/10.22214/ijraset.2024.58562.

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Abstract: In recent years, there has been an ever-increasing demand for enhancing mechanical properties of Aluminum Matrix Composites (AMCs), which are finding wide applications in the field of aerospace, automobile, defense etc.,. Among all available aluminium alloys, Al6082 is extensively used owing to its excellent wear resistance and ease of processing. Newer techniques of improving the hardness and wear resistance of Al6082 by dispersing an appropriate mixture of hard ceramic powder and whiskers in the aluminium alloy are gaining popularity. The conventional aluminium based composites pos

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Nikolyukin, A. N., V. P. Yartsev, S. A. Mamontov, I. I. Kolomnikova, A. S. Pechnikov, and N. O. Nikitin. "The Analytical Study of the Value of Adhesion of Cement Gel between Reinforcement and Concrete." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 26, no. 3 (2020): 483–95. http://dx.doi.org/10.17277/vestnik.2020.03.pp.483-495.

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Disruption of the adhesion of reinforcement to concrete causes significant deformation of the structure, which can subsequently lead to the loss of its bearing capacity. There is a need to study the bonding process between concrete and reinforcement under various influences. The results of a numerical experiment on pulling out reinforcement of periodic profile from concrete are presented. A mathematical model to study the processes taking place in the field of embedding reinforcement in concrete has been built. The results of numerical modeling are described.

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Ajay, Khadka, Rijal Sudip, Thapa Ritika, Khanal Siroj, and Kumar Shah Umesh. "Geogrid for Road Construction in Nepal." Journal of Earthquake Science and Soil Dynamics Engineering 4, no. 3 (2022): 1–7. https://doi.org/10.5281/zenodo.5990845.

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New technological and improved methods for road construction to achieve stability, as well as sustainable service, seems really important in the present context. The use of Geogrid in road provides two major functions- base reinforcement and subgrade stabilization. Geogrid improves the ability to obtain compaction in overlaying aggregates. It confines aggregates together and strengthens subgrades material to resist traffic loads to a greater extent acting as a reinforcement. Problems like loose soil, seepage, poor drainage, water table variation directly affect the base materials of the road w

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Whittington, James C. R., and Timothy E. J. Behrens. "Reinforcement learning: Dopamine ramps with fuzzy value estimates." Current Biology 32, no. 5 (2022): R213—R215. http://dx.doi.org/10.1016/j.cub.2022.01.070.

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33

O'Daly, Matthew, and Edmund Fantino. "Delay reduction theory: Choice, value, and conditioned reinforcement." Behavior Analyst Today 4, no. 2 (2003): 141–50. http://dx.doi.org/10.1037/h0100116.

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34

Hutchison, Kent E., Frank L. Collins, John Tassey, and Emily Rosenberg. "Stress, naltrexone, and the reinforcement value of nicotine." Experimental and Clinical Psychopharmacology 4, no. 4 (1996): 431–37. http://dx.doi.org/10.1037/1064-1297.4.4.431.

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35

Wimmer, G. Elliott, Nathaniel D. Daw, and Daphna Shohamy. "Generalization of value in reinforcement learning by humans." European Journal of Neuroscience 35, no. 7 (2012): 1092–104. http://dx.doi.org/10.1111/j.1460-9568.2012.08017.x.

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36

Li, Ruiqun, Ruobing Wang, Tao Tian, Fukai Jia, and Zhong Zheng. "Multi-Agent Reinforcement Learning based on Value Distribution." Journal of Physics: Conference Series 1651 (November 2020): 012017. http://dx.doi.org/10.1088/1742-6596/1651/1/012017.

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Washburn, David A., Jonathan P. Gulledge, and Duane M. Rumbaugh. "The Heuristic and Motivational Value of Video Reinforcement." Learning and Motivation 28, no. 4 (1997): 510–20. http://dx.doi.org/10.1006/lmot.1997.0981.

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38

Zhang, Zihong, and Ruijia Li. "Q-value-based experience replay in reinforcement learning." Knowledge-Based Systems 315 (April 2025): 113296. https://doi.org/10.1016/j.knosys.2025.113296.

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Nandiyanto, Asep Bayu Dani, Risti Ragadhita, Meli Fiandini, Dwi Fitria Al Husaeni, Dwi Novia Al Husaeni, and Farid Fadhillah. "Domestic waste (eggshells and banana peels particles) as sustainable and renewable resources for improving resin-based brakepad performance: Bibliometric literature review, techno-economic analysis, dual-sized reinforcing experiments, to comparison ..." Communications in Science and Technology 7, no. 1 (2022): 50–61. http://dx.doi.org/10.21924/cst.7.1.2022.757.

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The objective of this study is to develop a new environmentally-friendly brake pad made from eggshells (Es) and banana peels (BPs) as reinforcement agents. E and BP particles as dual reinforcement with various compositions were combined. The E/BP mixture was then embedded on a polymer matrix composing a resin/hardener mixture in a 1:1 ratio. As a standard, brake pads using a single reinforcement of E and BP particles were also fabricated. Physical properties (i.e. particle size, surface roughness, morphology, and density), as well as mechanical properties (i.e. hardness, wear rate, and frictio

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Shekar, A. Chandra, Gurusamy Pathinettampadian, R. Suthan, et al. "Optimization on Wear Rate of AA2219/Nanographite/TiB2/Si3N4 Hybrid Composites Using Taguchi Process." Journal of Nanomaterials 2022 (July 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/1814623.

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The various following reinforcements like nanographite, titanium diboride (TiB2), silicon nitride (Si3N4), and aluminum 2219 have all been investigated in this study. Current research suggests that TiB2 and graphite may be a suitable reinforcement for Al2219 alloy. The stir casting process was used to make reinforced composites on unreinforced Al2219. Compared to the unreinforced Al2219, the TiB2 and nanographite-reinforced hybrid composites showed the exceptional wear resistance (30%) at 175°C. Matrix strengthening kinetics is improved at 175°C when TiB2 and nano-Gr reinforcement particles ar

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Serra, Albert, Quim Tarrés, Miquel-Àngel Chamorro, et al. "Modeling the Stiffness of Coupled and Uncoupled Recycled Cotton Fibers Reinforced Polypropylene Composites." Polymers 11, no. 10 (2019): 1725. http://dx.doi.org/10.3390/polym11101725.

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The stiffness of a composite material is mainly affected by the nature of its phases and its contents, the dispersion of the reinforcement, as well as the morphology and mean orientation of such reinforcement. In this paper, recovered dyed cotton fibers from textile industry were used as reinforcement for a polypropylene matrix. The specific dye seems to decrease the hydrophilicity of the fibers and to increase its chemical compatibility with the matrix. The results showed a linear evolution of the Young’s moduli of the composites against the reinforcement contents, although the slope of the r

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Friston, Karl, and Ping Ao. "Free Energy, Value, and Attractors." Computational and Mathematical Methods in Medicine 2012 (2012): 1–27. http://dx.doi.org/10.1155/2012/937860.

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It has been suggested recently that action and perception can be understood as minimising the free energy of sensory samples. This ensures that agents sample the environment to maximise the evidence for their model of the world, such that exchanges with the environment are predictable and adaptive. However, the free energy account does not invoke reward or cost-functions from reinforcement-learning and optimal control theory. We therefore ask whether reward is necessary to explain adaptive behaviour. The free energy formulation uses ideas from statistical physics to explain action in terms of

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Wiewiora, E. "Potential-Based Shaping and Q-Value Initialization are Equivalent." Journal of Artificial Intelligence Research 19 (September 1, 2003): 205–8. http://dx.doi.org/10.1613/jair.1190.

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Shaping has proven to be a powerful but precarious means of improving reinforcement learning performance. Ng, Harada, and Russell (1999) proposed the potential-based shaping algorithm for adding shaping rewards in a way that guarantees the learner will learn optimal behavior. In this note, we prove certain similarities between this shaping algorithm and the initialization step required for several reinforcement learning algorithms. More specifically, we prove that a reinforcement learner with initial Q-values based on the shaping algorithm's potential function make the same updates throughout

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Cai, Qingpeng, Ling Pan, and Pingzhong Tang. "Deterministic Value-Policy Gradients." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3316–23. http://dx.doi.org/10.1609/aaai.v34i04.5732.

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Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of deep reinforcement learning algorithms. Previous works consider deterministic value gradients with the finite horizon, but it is too myopic compared with infinite horizon. We firstly give a theoretical guarantee of the existence of the value gradients in this infinite setting. Ba

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Trapko, Tomasz, and Michał Musiał. "Effect of PBO–FRCM Reinforcement on Stiffness of Eccentrically Compressed Reinforced Concrete Columns." Materials 13, no. 5 (2020): 1221. http://dx.doi.org/10.3390/ma13051221.

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This paper examines the effect of PBO (P-phenylene benzobisoxazole)–FRCM (Fabric Reinforced Cementitious Matrix) reinforcement on the stiffness of eccentrically compressed reinforced concrete columns. Reinforcement with FRCM consists of bonding composite meshes to the concrete substrate by means of mineral mortar. Longitudinal and/or transverse reinforcements made of PBO (P-phenylene benzobisoxazole) mesh were applied to the analyzed column specimens. When assessing the stiffness of the columns, the focus was on the effect of the composite reinforcement itself, the value and eccentricity of th

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Д.М., Кислинська, та Мілорадова Н.Е. "ЦІННОСТІ ТА ЦІННІСНІ ОРІЄНТАЦІЇ В ПСИХОЛОГІЧНИХ ТЕОРІЯХ". Вісник Харківського національного педагогічного університету імені Г.С. Сковороди "Психологія", № 52 (14 січня 2016): 103–12. https://doi.org/10.5281/zenodo.44716.

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Psychological theories explain the value of the position of scientific theories. Be-haviorism considers value as a result of associative learning. Psychoanalysis sees value-normative regulation of individual in-depth structures "ego", "superego" and "id". Hu-manistic psychology sees the value in the "self" personality and connects them with human needs, introduces the term "functional autonomy", meaning thereby converting values; value orientation as regards personal meaning. Domestic scholars introduced the concept of "focus", "

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M.N., Nwigbo, Lasisi U.E., and Ukaru Y.N. "Comparative Study of Tensile Properties of Hybrid AA6061/SIC/Carbonized Coconut Shell Micro and Nano Composites." International Journal of Mechanical and Civil Engineering 5, no. 1 (2022): 10–24. http://dx.doi.org/10.52589/ijmce-yemppwep.

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This study synthesized a hybrid aluminium 6061 matrix composite with particulates of silicon carbide, SiCp and carbonized coconut shell (CCSP as reinforcements), and determined the effect of combining SiCp and CCSp reinforcements of different sizes and weight fractions on the strength properties and microstructure of the developed composite. The hybrid aluminium matrix composites were developed using the stir casting method. Several samples of the composites consisting of AA6061 alloy with 3, 6, 9, 12 and 15% by wt. each of CCSp and SiCp with average particle sizes of 38μm and 42.3nm for SiC,

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McDevitt, Margaret A., and Matthew C. Bell. "Discrete-trial vs. continuous free-operant procedures in assessing whether reinforcement context affects reinforcement value." Behavioural Processes 77, no. 3 (2008): 376–83. http://dx.doi.org/10.1016/j.beproc.2007.10.005.

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Lufthansa, Luthfie, Sumaryanti, Rachmah Laksmi Ambardini, et al. "The effect of positive and negative reinforcement to increase motivation of basic locomotor movements in children with mild intellectual disabilities." Fizjoterapia Polska 24, no. 4 (2024): 194–201. http://dx.doi.org/10.56984/8zg01a8k4p8.

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The provision of good treatment can increase various positive things for the growth and development of children with disabilities, one of which is Positive and Negative Reinforcement. This study aims to determine the influence of positive and negative reinforcement on enhancing motivation for basic locomotor movements in children with disabilities. This study uses a pre-experimental, one-group pretest-posttest design. The research was conducted at Kendungkandang State Special School with a sample of 20 students. The data collection technique in this study uses tests and measurements. The resul

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Manavalan, Mani, and Apoorva Ganapathy. "Reinforcement Learning in Robotics." Engineering International 2, no. 2 (2014): 113–24. http://dx.doi.org/10.18034/ei.v2i2.572.

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Reinforcement learning has been found to offer to robotics the valid tools and techniques for the redesign of valuable and sophisticated designs for robotics. There are multiple challenges related to the prime problems related to the value added in the reinforcement of the new learning. The study has found the linkages between different subjects related to science in particular. We have attempted to make and establish the links that have been found between the two research communities in order to provide a survey-related task in reinforcement learning for behavior in terms of the generation th

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