Relevant bibliographies by topics / Reinforcement Learning

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

Academic literature on the topic 'Reinforcement Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Reinforcement Learning"

1

Singh, Pranjal, Prasann Sharma, Yash Gupta, and Sampada Massey. "Reinforcement Learning for Portfolio Management." International Journal of Research Publication and Reviews 6, no. 4 (2025): 10374–77. https://doi.org/10.55248/gengpi.6.0425.1599.

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Deora, Merin, and Sumit Mathur. "Reinforcement Learning." IJARCCE 6, no. 4 (2017): 178–81. http://dx.doi.org/10.17148/ijarcce.2017.6433.

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Barto, Andrew G. "Reinforcement Learning." IFAC Proceedings Volumes 31, no. 29 (1998): 5. http://dx.doi.org/10.1016/s1474-6670(17)38315-5.

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Woergoetter, Florentin, and Bernd Porr. "Reinforcement learning." Scholarpedia 3, no. 3 (2008): 1448. http://dx.doi.org/10.4249/scholarpedia.1448.

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Moore, Brett L., Anthony G. Doufas, and Larry D. Pyeatt. "Reinforcement Learning." Anesthesia & Analgesia 112, no. 2 (2011): 360–67. http://dx.doi.org/10.1213/ane.0b013e31820334a7.

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Likas, Aristidis. "A Reinforcement Learning Approach to Online Clustering." Neural Computation 11, no. 8 (1999): 1915–32. http://dx.doi.org/10.1162/089976699300016025.

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A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ tha
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Mardhatillah, Elsy. "Teacher’s Reinforcement in English Classroom in MTSS Darul Makmur Sungai Cubadak." Indonesian Research Journal On Education 3, no. 1 (2022): 825–32. http://dx.doi.org/10.31004/irje.v3i1.202.

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This research was due to some problems found in MTsS Darul Makmur. First, some students were not motivated in learning. Second, sometime the teacher still uses Indonesian in giving reinforcements. Third, some Students did not care about the teacher's reinforcement. This study aimed to find out the types of reinforcement used by the teacher. Then, to find out the types of reinforcement often and rarely to be usedby the teacher. Then, to find out the reasons the teacher used certain reinforcements. Last, to find out how the teacher understands the reinforcement. This research used a qualitative
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Liaq, Mudassar, and Yungcheol Byun. "Autonomous UAV Navigation Using Reinforcement Learning." International Journal of Machine Learning and Computing 9, no. 6 (2019): 756–61. http://dx.doi.org/10.18178/ijmlc.2019.9.6.869.

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Alrammal, Muath, and Munir Naveed. "Monte-Carlo Based Reinforcement Learning (MCRL)." International Journal of Machine Learning and Computing 10, no. 2 (2020): 227–32. http://dx.doi.org/10.18178/ijmlc.2020.10.2.924.

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Nurmuhammet, Abdullayev. "DEEP REINFORCEMENT LEARNING ON STOCK DATA." Alatoo Academic Studies 23, no. 2 (2023): 505–18. http://dx.doi.org/10.17015/aas.2023.232.49.

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This study proposes using Deep Reinforcement Learning (DRL) for stock trading decisions and prediction. DRL is a machine learning technique that enables agents to learn optimal strategies by interacting with their environment. The proposed model surpasses traditional models and can make informed trading decisions in real-time. The study highlights the feasibility of applying DRL in financial markets and its advantages in strategic decision- making. The model's ability to learn from market dynamics makes it a promising approach for stock market forecasting. Overall, this paper provides valuable
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Dissertations / Theses on the topic "Reinforcement Learning"

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Izquierdo, Ayala Pablo. "Learning comparison: Reinforcement Learning vs Inverse Reinforcement Learning : How well does inverse reinforcement learning perform in simple markov decision processes in comparison to reinforcement learning?" Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259371.

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This research project elaborates a qualitative comparison between two different learning approaches, Reinforcement Learning (RL) and Inverse Reinforcement Learning (IRL) over the Gridworld Markov Decision Process. The interest focus will be set on the second learning paradigm, IRL, as it is considered to be relatively new and little work has been developed in this field of study. As observed, RL outperforms IRL, obtaining a correct solution in all the different scenarios studied. However, the behaviour of the IRL algorithms can be improved and this will be shown and analyzed as part of the sco
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Seymour, B. J. "Aversive reinforcement learning." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/800107/.

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We hypothesise that human aversive learning can be described algorithmically by Reinforcement Learning models. Our first experiment uses a second-order conditioning design to study sequential outcome prediction. We show that aversive prediction errors are expressed robustly in the ventral striatum, supporting the validity of temporal difference algorithms (as in reward learning), and suggesting a putative critical area for appetitive-aversive interactions. With this in mind, the second experiment explores the nature of pain relief, which as expounded in theories of motivational opponency, is r
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Akrour, Riad. "Robust Preference Learning-based Reinforcement Learning." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112236/document.

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Les contributions de la thèse sont centrées sur la prise de décisions séquentielles et plus spécialement sur l'Apprentissage par Renforcement (AR). Prenant sa source de l'apprentissage statistique au même titre que l'apprentissage supervisé et non-supervisé, l'AR a gagné en popularité ces deux dernières décennies en raisons de percées aussi bien applicatives que théoriques. L'AR suppose que l'agent (apprenant) ainsi que son environnement suivent un processus de décision stochastique Markovien sur un espace d'états et d'actions. Le processus est dit de décision parce que l'agent est appelé à ch
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Tabell, Johnsson Marco, and Ala Jafar. "Efficiency Comparison Between Curriculum Reinforcement Learning & Reinforcement Learning Using ML-Agents." Thesis, Blekinge Tekniska Högskola, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20218.

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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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Cortesi, Daniele. "Reinforcement Learning in Rogue." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16138/.

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In this work we use Reinforcement Learning to play the famous Rogue, a dungeon-crawler videogame father of the rogue-like genre. By employing different algorithms we substantially improve on the results obtained in previous work, addressing and solving the problems that were arisen. We then devise and perform new experiments to test the limits of our own solution and encounter additional and unexpected issues in the process. In one of the investigated scenario we clearly see that our approach is not yet enough to even perform better than a random agent and propose ideas for future works.
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Girgin, Sertan. "Abstraction In Reinforcement Learning." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608257/index.pdf.

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Reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment. Generally, the problem to be solved contains subtasks that repeat at different regions of the state space. Without any guidance an agent has to learn the solutions of all subtask instances independently, which degrades the learning performance. In this thesis, we propose two approaches to build connections between different regions of the search space leading to better utilization of gained experience and accelerate learning is proposed. In the fir
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Suay, Halit Bener. "Reinforcement Learning from Demonstration." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/173.

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Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly because they are expected to learn a task from scratch merely through an agent's own experience. In this thesis, we show that learning from scratch is a limiting factor for the learning performance, and that when prior knowledge is available RL agents can learn a task faster. We evaluate relevant previous work and our own algorithms in various experiments. Our first contribution is the first implementation and evaluation of an existing interactive RL algorithm in a real-world domain with a human
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Gao, Yang. "Argumentation accelerated reinforcement learning." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/26603.

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Reinforcement Learning (RL) is a popular statistical Artificial Intelligence (AI) technique for building autonomous agents, but it suffers from the curse of dimensionality: the computational requirement for obtaining the optimal policies grows exponentially with the size of the state space. Integrating heuristics into RL has proven to be an effective approach to combat this curse, but deriving high-quality heuristics from people's (typically conflicting) domain knowledge is challenging, yet it received little research attention. Argumentation theory is a logic-based AI technique well-known for
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Alexander, John W. "Transfer in reinforcement learning." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908.

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The problem of developing skill repertoires autonomously in robotics and artificial intelligence is becoming ever more pressing. Currently, the issues of how to apply prior knowledge to new situations and which knowledge to apply have not been sufficiently studied. We present a transfer setting where a reinforcement learning agent faces multiple problem solving tasks drawn from an unknown generative process, where each task has similar dynamics. The task dynamics are changed by varying in the transition function between states. The tasks are presented sequentially with the latest task presente
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Books on the topic "Reinforcement Learning"

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

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Wiering, Marco, and Martijn van Otterlo, eds. Reinforcement Learning. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3.

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Sutton, Richard S., ed. Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5.

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Lorenz, Uwe. Reinforcement Learning. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2.

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Nandy, Abhishek, and Manisha Biswas. Reinforcement Learning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3285-9.

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S, Sutton Richard, ed. Reinforcement learning. Kluwer Academic Publishers, 1992.

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Lorenz, Uwe. Reinforcement Learning. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68311-8.

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Li, Jinna, Frank L. Lewis, and Jialu Fan. Reinforcement Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9.

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Xiao, Zhiqing. Reinforcement Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-4933-3.

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Merrick, Kathryn, and Mary Lou Maher. Motivated Reinforcement Learning. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89187-1.

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Book chapters on the topic "Reinforcement Learning"

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Sutton, Richard S. "Introduction: The Challenge of Reinforcement Learning." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_1.

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Williams, Ronald J. "Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_2.

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Tesauro, Gerald. "Practical Issues in Temporal Difference Learning." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_3.

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Watkins, Christopher J. C. H., and Peter Dayan. "Technical Note." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_4.

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Lin, Long-Ji. "Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_5.

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Singh, Satinder Pal. "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_6.

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Dayan, Peter. "The Convergence of TD(λ) for General λ." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_7.

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Millán, José R., and Carme Torras. "A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments." In Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_8.

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Lorenz, Uwe. "Bestärkendes Lernen als Teilgebiet des Maschinellen Lernens." In Reinforcement Learning. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2_1.

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Lorenz, Uwe. "Grundbegriffe des Bestärkenden Lernens." In Reinforcement Learning. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2_2.

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Conference papers on the topic "Reinforcement Learning"

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Kruse, Georg, Rodrigo Coelho, Andreas Rosskopf, Robert Wille, and Jeanette-Miriam Lorenz. "Benchmarking Quantum Reinforcement Learning." In Workshop on Quantum Artificial Intelligence and Optimization 2025. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013393200003890.

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Toonen, Kelvin, and Thiago Simão. "Making Reinforcement Learning Safer via Curriculum Learning." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013388100003890.

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Yang, Kun, Chengshuai Shi, and Cong Shen. "Teaching Reinforcement Learning Agents via Reinforcement Learning." In 2023 57th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2023. http://dx.doi.org/10.1109/ciss56502.2023.10089695.

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Doshi, Finale, Joelle Pineau, and Nicholas Roy. "Reinforcement learning with limited reinforcement." In the 25th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390189.

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Li, Zhiyi. "Reinforcement Learning." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. ACM, 2019. http://dx.doi.org/10.1145/3287324.3293703.

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Shen, Shitian, and Min Chi. "Reinforcement Learning." In UMAP '16: User Modeling, Adaptation and Personalization Conference. ACM, 2016. http://dx.doi.org/10.1145/2930238.2930247.

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Kuroe, Yasuaki, and Kenya Takeuchi. "Sophisticated Swarm Reinforcement Learning by Incorporating Inverse Reinforcement Learning." In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023. http://dx.doi.org/10.1109/smc53992.2023.10394525.

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Lyu, Le, Yang Shen, and Sicheng Zhang. "The Advance of Reinforcement Learning and Deep Reinforcement Learning." In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2022. http://dx.doi.org/10.1109/eebda53927.2022.9744760.

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Epshteyn, Arkady, Adam Vogel, and Gerald DeJong. "Active reinforcement learning." In the 25th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390194.

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Epshteyn, Arkady, and Gerald DeJong. "Qualitative reinforcement learning." In the 23rd international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143883.

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Reports on the topic "Reinforcement Learning"

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Singh, Satinder, Andrew G. Barto, and Nuttapong Chentanez. Intrinsically Motivated Reinforcement Learning. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440280.

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Ghavamzadeh, Mohammad, and Sridhar Mahadevan. Hierarchical Multiagent Reinforcement Learning. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada440418.

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Harmon, Mance E., and Stephanie S. Harmon. Reinforcement Learning: A Tutorial. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada323194.

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Tadepalli, Prasad, and Alan Fern. Partial Planning Reinforcement Learning. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada574717.

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Ghavamzadeh, Mohammad, and Sridhar Mahadevan. Hierarchical Average Reward Reinforcement Learning. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada445728.

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Johnson, Daniel W. Drive-Reinforcement Learning System Applications. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada264514.

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Cleland, Andrew. Bounding Box Improvement With Reinforcement Learning. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6322.

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Li, Jiajie. Learning Financial Investment Strategies using Reinforcement Learning and 'Chan theory'. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-946.

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Baird, III, Klopf Leemon C., and A. H. Reinforcement Learning With High-Dimensional, Continuous Actions. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada280844.

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Obert, James, and Angie Shia. Optimizing Dynamic Timing Analysis with Reinforcement Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1573933.

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