Relevant bibliographies by topics / Reinforcement value

Contents

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

Academic literature on the topic 'Reinforcement value'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Reinforcement value"

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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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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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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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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Dissertations / Theses on the topic "Reinforcement value"

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Mahadevan, Swaminathan. "Probabilistic linear function approximation for value-based reinforcement learning." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98759.

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Reinforcement learning (RL) is a computational framework for learning sequential decision strategies from the interaction of an agent with an unknown dynamic environment. This thesis focuses on value-based reinforcement learning methods, which rely on computing utility values for different behavior strategies that can be adopted by the agent. Real-world complex problems involve very large discrete or continuous state spaces where the use of approximate methods is required. It has been observed that subtle differences in the approximate methods result in very different theoretical properties an
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James, Michael. "The estimation of reward and value in reinforcement learning." Thesis, University of York, 2003. http://etheses.whiterose.ac.uk/14066/.

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Beus, Ben. "Conditioned Reinforcement and the Value of Praise in Children with Autism." DigitalCommons@USU, 2014. https://digitalcommons.usu.edu/etd/3848.

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Many efforts in teaching children with autism are focused on increasing the value of praise as a reward for work. Increasing the value of praise can help children with autism to work in a natural setting, without requiring constant rewards of food or toys for work. In this study, I analyzed a pairing method—a technique of providing verbal praise while simultaneously providing a food reward—to assess whether it would result in an increased value for praise for participants in the study. First, a baseline phase was conducted in which praise statements were provided as a reward for a certain task
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Wingate, David. "Solving Large MDPs Quickly with Partitioned Value Iteration." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd437.pdf.

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Mac, Dermed Liam Charles. "Value methods for efficiently solving stochastic games of complete and incomplete information." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50270.

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Multi-agent reinforcement learning (MARL) poses the same planning problem as traditional reinforcement learning (RL): What actions over time should an agent take in order to maximize its rewards? MARL tackles a challenging set of problems that can be better understood by modeling them as having a relatively simple environment but with complex dynamics attributed to the presence of other agents who are also attempting to maximize their rewards. A great wealth of research has developed around specific subsets of this problem, most notably when the rewards for each agent are either the same or di
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Rossi, Martina. "Opponent Modelling using Inverse Reinforcement Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22263/.

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Un’area di ricerca particolarmente attiva ultimamente nel campo dell'intelligenza artificiale (IA) riguarda lo studio di agenti autonomi, notevolmente diffusi anche nella vita quotidiana. L'obiettivo principale è sviluppare agenti che interagiscano in modo efficiente con altri agenti o esseri umani. Di conseguenza, queste relazioni potrebbero essere notevolmente semplificate grazie alla capacità di dedurre autonomamente le preferenze di altre entità e di adattare di conseguenza la strategia dell'agente. Pertanto, lo scopo di questa tesi è implementare un agente, in grado di apprendere, che int
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Bredthauer, Jennifer Lyn Johnston James M. "The assessment of preference for qualitatively different reinforcers in persons with developmental and learning disabilities a comparison of value using behavioral economic and standard preference assessment procedures /." Auburn, Ala, 2009. http://hdl.handle.net/10415/1809.

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Russell, Danielle M. "Using Progressive Ratio Schedules to Evaluate Edible, Leisure, and Token Reinforcement." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc271888/.

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The general purpose of the current study was to evaluate the potency of different categories of reinforcers with young children diagnosed with developmental delays. The participants were two boys and one girl who were between the ages of seven and eight. In Phase 1, we evaluated the reinforcing potency of tokens, edible items, and leisure items by using a progressive ratio (PR) schedule. For two participants, we found that tokens resulted in the highest PR break points. For one participant, edibles resulted in the highest break points (tokens were found to have the lowest break points). In Pha
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Masters, David M. "Verifying Value Iteration and Policy Iteration in Coq." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1618999718015199.

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Francisco, Monica T. "Evaluation of absolute and relative reinforcer value using progressive ratio schedules." Scholarly Commons, 2007. https://scholarlycommons.pacific.edu/uop_etds/672.

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We evaluated behavior exhibited by individuals with developmental disabilities using progressive ratio schedules. High- and low-preferred stimuli were determined based on the results of a paired-stimulus preference assessment and were evaluated in subsequent reinforcer and progressive ratio assessments using concurrent- and single-operant schedules of presentation in a modified reversal design. Results showed that for two participants, stimuli determined to be low-preferred via a preference assessment functioned as reinforcers when evaluated independently of high-preferred stimuli and under gr
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Books on the topic "Reinforcement value"

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Morillo, Carolyn R. Contingent creatures: A reward event theory of motivation and value. Littlefield Adams Books, 1995.

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Maurilus, Emmy. The Effect of the Establishment of Reinforcement Value for Math on Rate of Learning for Pre-Kindergarten Students. [publisher not identified], 2018.

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The Effect of delay and intervening events on reinforcement value. Lawrence Erlbaum, 1987.

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Commons, Michael L., James E. Mazur, John A. Nevin, and Howard Rachlin, eds. The Effect of Delay and of Intervening Events on Reinforcement Value. Psychology Press, 2013. http://dx.doi.org/10.4324/9781315825502.

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Mazur, James E. Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior, Volume V. Taylor & Francis Group, 2013.

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Mazur, James E. Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior, Volume V. Taylor & Francis Group, 2013.

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Mazur, James E. Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior, Volume V. Taylor & Francis Group, 2013.

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Mazur, James E. Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior, Volume V. Taylor & Francis Group, 2013.

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(Editor), Michael L. Commons, James E. Mazur (Editor), John A. Nevin (Editor), and Howard Rachlin (Editor), eds. The Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior, Volume V (Quantitative Analyses of Behavior). Lawrence Erlbaum, 1986.

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Deep Reinforcement Learning Hands-On: Apply Modern RL Methods, with Deep Q-Networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More. de Gruyter GmbH, Walter, 2018.

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Book chapters on the topic "Reinforcement value"

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Yan, Xu. "Reinforcement Value." In The ECPH Encyclopedia of Psychology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6000-2_92-1.

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Dart, Evan H., and Timothy Thompson. "Reinforcement Value." In Encyclopedia of Personality and Individual Differences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_998.

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Dart, Evan H., and Timothy Thompson. "Reinforcement Value." In Encyclopedia of Personality and Individual Differences. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28099-8_998-1.

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Yan, Xu. "Reinforcement Value." In The ECPH Encyclopedia of Psychology. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7874-4_92.

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Szepesvári, Csaba. "Value Prediction Problems." In Algorithms for Reinforcement Learning. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01551-9_2.

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Zhao, Shiyu. "Value Function Methods." In Mathematical Foundations of Reinforcement Learning. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-3944-8_8.

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Liu, Zhen “Leo.” "Value-Based Reinforcement Learning." In Artificial Intelligence for Engineers. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75953-6_14.

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Hu, Michael. "Linear Value Function Approximation." In The Art of Reinforcement Learning. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9606-6_6.

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Hu, Michael. "Nonlinear Value Function Approximation." In The Art of Reinforcement Learning. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9606-6_7.

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Zhao, Shiyu. "Value Iteration and Policy Iteration." In Mathematical Foundations of Reinforcement Learning. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-3944-8_4.

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Conference papers on the topic "Reinforcement value"

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Ruan, Zhendong, Teng Yan, Yaobang Cai, Yu Han, Leyi Zheng, and Yang Zhang. "Q-Value Regularized Decision ConvFormer for Offline Reinforcement Learning." In 2024 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2024. https://doi.org/10.1109/robio64047.2024.10907371.

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Yao, Dunqi, Chuxiong Sun, Kai Li, et al. "CMIX: Causal Value Decomposition for Cooperative Multi-Agent Reinforcement Learning." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831507.

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Chen, Baicheng, Tianhan Gao, and Qingwei Mi. "A Reinforcement Learning Sampling Optimization Method Based on Training Value." In 2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI). IEEE, 2024. http://dx.doi.org/10.1109/icetci62771.2024.10704169.

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Ni, Xinyi, and Lifeng Lai. "Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk." In 2024 IEEE Information Theory Workshop (ITW). IEEE, 2024. https://doi.org/10.1109/itw61385.2024.10806953.

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Han, Yiyi, Lulu Guo, and Hong Chen. "Value Conditional State Entropy Reinforcement Learning for Autonomous Driving Decision Making." In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN). IEEE, 2024. https://doi.org/10.1109/indin58382.2024.10774316.

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Liu, Xiaosong, Quan Liu, and Lan Wu. "Improved Techniques for Offline Reinforcement Learning: Advantage Value Estimation and Layernorm." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890751.

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Lee, Dongsu, Chanin Eom, and Minhae Kwon. "AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610308.

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Nafi, Nasik Muhammad, Raja Farrukh Ali, and William Hsu. "Analyzing the Sensitivity to Policy-Value Decoupling in Deep Reinforcement Learning Generalization." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651435.

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Xu, Du, Lu Jia, Yewei Wang, Qian Liu, and Yong Zhang. "Qasync: A Dual Network Architecture for Value Based Multi-Agent Reinforcement Learning." In 2024 3rd International Conference on Automation, Robotics and Computer Engineering (ICARCE). IEEE, 2024. https://doi.org/10.1109/icarce63054.2024.00099.

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Rozada, Sergio, Santiago Paternain, Juan Andrés Bazerque, and Antonio G. Marques. "A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942931.

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Reports on the topic "Reinforcement value"

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Baluk, Nadia, Natalia Basij, Larysa Buk, and Olha Vovchanska. VR/AR-TECHNOLOGIES – NEW CONTENT OF THE NEW MEDIA. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.49.11074.

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The article analyzes the peculiarities of the media content shaping and transformation in the convergent dimension of cross-media, taking into account the possibilities of augmented reality. With the help of the principles of objectivity, complexity and reliability in scientific research, a number of general scientific and special methods are used: method of analysis, synthesis, generalization, method of monitoring, observation, problem-thematic, typological and discursive methods. According to the form of information presentation, such types of media content as visual, audio, verbal and combi
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Hall, Chris. Development of an Infantry ‘Disrupt Force’ and Technological Experimentation in an Operational Unit. Giraffe Publishing, 2025. https://doi.org/10.61451/2675146.

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In 2021–2022, the 1st Battalion, Royal Australian Regiment (1 RAR), experimented to develop a ‘disrupt force’ consisting of light infantry augmented with uncrewed aerial systems and ground vehicles. This group operated ahead of other forces to report on the opposing force, but also ambushed enemy reinforcement and withdrawal routes. The disrupt force actively attacked and degraded opposing forces and their plans, isolating objectives and setting the conditions for friendly combat teams to conduct decisive manoeuvre. The aims of this experiment were twofold: to provide feedback on individual pl
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Christoforidou, Eirini, Antonio Bobet, Tommy Nantung, and Philippe L. Bourdeau. Use of Geosynthetics on Subgrade and on Low and Variable Fill Foundations. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317437.

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There are significant problems during construction to establish an adequate foundation for fills and/or subgrade for pavements when the natural ground has low-bearing soils. Geosynthetics such as geogrids, geotextiles, and/or geocells could provide a less time-consuming, costly alternative for establishing an adequate foundation for the fill and/or subgrade. There is extensive evidence in the literature and on DOTs practices about the suitability of using geotextiles in pavements as separators. Previous studies have also shown that the use of geogrids in flexible pavements as a reinforcing mec
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Pisani, William, Dane Wedgeworth, Michael Roth, John Newman, and Manoj Shukla. Exploration of two polymer nanocomposite structure-property relationships facilitated by molecular dynamics simulation and multiscale modeling. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46713.

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Polyamide 6 (PA6) is a semi-crystalline thermoplastic used in many engineering applications due to good strength, stiffness, mechanical damping, wear/abrasion resistance, and excellent performance-to-cost ratio. In this report, two structure-property relationships were explored. First, carbon nanotubes (CNT) and graphene (G) were used as reinforcement molecules in simulated and experimentally prepared PA6 matrices to improve the overall mechanical properties. Molecular dynamics (MD) simulations with INTERFACE and reactive INTERFACE force fields (IFF and IFF-R) were used to predict bulk and You
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WIND RESISTANCE PERFORMANCE AND REINFORCEMENT MEASURES OF STANDING SEAM METAL ROOFS BASED ON WIND TUNNEL TESTS. The Hong Kong Institute of Steel Construction, 2025. https://doi.org/10.18057/ijasc.2025.21.1.1.

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As an important building envelope structure, metal roofs have been widely used in various large-scale buildings because of their unique advantages such as beautiful shape, lightweight nature, flexibility, and exceptional strength. However, there are still some gaps in the existing research on the wind resistance performance of metal roof systems, which poses a serious threat to life and property. To address this gap, this paper conducted wind tunnel tests of long-span standing seam metal roofs. The basic wind pressure of 0.95 kN/m2, which is once every 100 years. The wind-induced response of t
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PROGRESSIVE COLLAPSE RESISTANCE OF STEEL FRAMED BUILDINGS UNDER EXTREME EVENTS. The Hong Kong Institute of Steel Construction, 2021. http://dx.doi.org/10.18057/ijasc.2021.17.3.10.

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This paper presents experimental and theoretical investigations on progressive collapse behavior of steel framed structures subjected to an extreme load such as fire, blast and impact. A new capacity-based index is proposed to quantify robustness of structures. An energy-based theoretical model is also proposed to quantify the effect of concrete slabs on collapse resistance of structures. The experimental results show that the dynamic amplification factors of frames subject to impact or blast are much less than the conventional value of 2.0. The collapse process of frames in fire can be either
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