Gotowa bibliografia na temat „Reinforcement Learning”
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Artykuły w czasopismach na temat "Reinforcement Learning"
Deora, Merin, i Sumit Mathur. "Reinforcement Learning". IJARCCE 6, nr 4 (30.04.2017): 178–81. http://dx.doi.org/10.17148/ijarcce.2017.6433.
Pełny tekst źródłaBarto, Andrew G. "Reinforcement Learning". IFAC Proceedings Volumes 31, nr 29 (październik 1998): 5. http://dx.doi.org/10.1016/s1474-6670(17)38315-5.
Pełny tekst źródłaWoergoetter, Florentin, i Bernd Porr. "Reinforcement learning". Scholarpedia 3, nr 3 (2008): 1448. http://dx.doi.org/10.4249/scholarpedia.1448.
Pełny tekst źródłaMoore, Brett L., Anthony G. Doufas i Larry D. Pyeatt. "Reinforcement Learning". Anesthesia & Analgesia 112, nr 2 (luty 2011): 360–67. http://dx.doi.org/10.1213/ane.0b013e31820334a7.
Pełny tekst źródłaLiaq, Mudassar, i Yungcheol Byun. "Autonomous UAV Navigation Using Reinforcement Learning". International Journal of Machine Learning and Computing 9, nr 6 (grudzień 2019): 756–61. http://dx.doi.org/10.18178/ijmlc.2019.9.6.869.
Pełny tekst źródłaAlrammal, Muath, i Munir Naveed. "Monte-Carlo Based Reinforcement Learning (MCRL)". International Journal of Machine Learning and Computing 10, nr 2 (luty 2020): 227–32. http://dx.doi.org/10.18178/ijmlc.2020.10.2.924.
Pełny tekst źródłaNurmuhammet, Abdullayev. "DEEP REINFORCEMENT LEARNING ON STOCK DATA". Alatoo Academic Studies 23, nr 2 (30.06.2023): 505–18. http://dx.doi.org/10.17015/aas.2023.232.49.
Pełny tekst źródłaLikas, Aristidis. "A Reinforcement Learning Approach to Online Clustering". Neural Computation 11, nr 8 (1.11.1999): 1915–32. http://dx.doi.org/10.1162/089976699300016025.
Pełny tekst źródłaMardhatillah, Elsy. "Teacher’s Reinforcement in English Classroom in MTSS Darul Makmur Sungai Cubadak". Indonesian Research Journal On Education 3, nr 1 (2.01.2022): 825–32. http://dx.doi.org/10.31004/irje.v3i1.202.
Pełny tekst źródłaFan, ZiSheng. "An exploration of reinforcement learning and deep reinforcement learning". Applied and Computational Engineering 73, nr 1 (5.07.2024): 154–59. http://dx.doi.org/10.54254/2755-2721/73/20240386.
Pełny tekst źródłaRozprawy doktorskie na temat "Reinforcement Learning"
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.
Pełny tekst źródłaDenna studie är en kvalitativ jämförelse mellan två olika inlärningsangreppssätt, “Reinforcement Learning” (RL) och “Inverse Reinforcement Learning” (IRL), om använder "Gridworld", en "Markov Decision-Process". Fokus ligger på den senare algoritmen, IRL, eftersom den anses relativt ny och få studier har i nuläget gjorts kring den. I studien är RL mer fördelaktig än IRL, som skapar en korrekt lösning i alla olika scenarier som presenteras i studien. Beteendet hos IRL-algoritmen kan dock förbättras vilket också visas och analyseras i denna studie.
Seymour, B. J. "Aversive reinforcement learning". Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/800107/.
Pełny tekst źródłaAkrour, Riad. "Robust Preference Learning-based Reinforcement Learning". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112236/document.
Pełny tekst źródłaThe thesis contributions resolves around sequential decision taking and more precisely Reinforcement Learning (RL). Taking its root in Machine Learning in the same way as supervised and unsupervised learning, RL quickly grow in popularity within the last two decades due to a handful of achievements on both the theoretical and applicative front. RL supposes that the learning agent and its environment follow a stochastic Markovian decision process over a state and action space. The process is said of decision as the agent is asked to choose at each time step an action to take. It is said stochastic as the effect of selecting a given action in a given state does not systematically yield the same state but rather defines a distribution over the state space. It is said to be Markovian as this distribution only depends on the current state-action pair. Consequently to the choice of an action, the agent receives a reward. The RL goal is then to solve the underlying optimization problem of finding the behaviour that maximizes the sum of rewards all along the interaction of the agent with its environment. From an applicative point of view, a large spectrum of problems can be cast onto an RL one, from Backgammon (TD-Gammon, was one of Machine Learning first success giving rise to a world class player of advanced level) to decision problems in the industrial and medical world. However, the optimization problem solved by RL depends on the prevous definition of a reward function that requires a certain level of domain expertise and also knowledge of the internal quirks of RL algorithms. As such, the first contribution of the thesis was to propose a learning framework that lightens the requirements made to the user. The latter does not need anymore to know the exact solution of the problem but to only be able to choose between two behaviours exhibited by the agent, the one that matches more closely the solution. Learning is interactive between the agent and the user and resolves around the three main following points: i) The agent demonstrates a behaviour ii) The user compares it w.r.t. to the current best one iii) The agent uses this feedback to update its preference model of the user and uses it to find the next behaviour to demonstrate. To reduce the number of required interactions before finding the optimal behaviour, the second contribution of the thesis was to define a theoretically sound criterion making the trade-off between the sometimes contradicting desires of complying with the user's preferences and demonstrating sufficiently different behaviours. The last contribution was to ensure the robustness of the algorithm w.r.t. the feedback errors that the user might make. Which happens more often than not in practice, especially at the initial phase of the interaction, when all the behaviours are far from the expected solution
Tabell, Johnsson Marco, i 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.
Pełny tekst źródłaYang, 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.
Pełny tekst źródłaCortesi, Daniele. "Reinforcement Learning in Rogue". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16138/.
Pełny tekst źródłaGirgin, Sertan. "Abstraction In Reinforcement Learning". Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608257/index.pdf.
Pełny tekst źródłaSuay, Halit Bener. "Reinforcement Learning from Demonstration". Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/173.
Pełny tekst źródłaGao, Yang. "Argumentation accelerated reinforcement learning". Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/26603.
Pełny tekst źródłaAlexander, John W. "Transfer in reinforcement learning". Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908.
Pełny tekst źródłaKsiążki na temat "Reinforcement Learning"
S, Sutton Richard, red. Reinforcement learning. Boston: Kluwer Academic Publishers, 1992.
Znajdź pełny tekst źródłaSutton, Richard S. Reinforcement Learning. Boston, MA: Springer US, 1992.
Znajdź pełny tekst źródłaWiering, Marco, i Martijn van Otterlo, red. Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3.
Pełny tekst źródłaSutton, Richard S., red. Reinforcement Learning. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5.
Pełny tekst źródłaLorenz, Uwe. Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2.
Pełny tekst źródłaNandy, Abhishek, i Manisha Biswas. Reinforcement Learning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3285-9.
Pełny tekst źródłaLi, Jinna, Frank L. Lewis i Jialu Fan. Reinforcement Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9.
Pełny tekst źródłaLorenz, Uwe. Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68311-8.
Pełny tekst źródłaMerrick, Kathryn, i Mary Lou Maher. Motivated Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89187-1.
Pełny tekst źródłaDong, Hao, Zihan Ding i Shanghang Zhang, red. Deep Reinforcement Learning. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0.
Pełny tekst źródłaCzęści książek na temat "Reinforcement Learning"
Sutton, Richard S. "Introduction: The Challenge of Reinforcement Learning". W Reinforcement Learning, 1–3. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_1.
Pełny tekst źródłaWilliams, Ronald J. "Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning". W Reinforcement Learning, 5–32. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_2.
Pełny tekst źródłaTesauro, Gerald. "Practical Issues in Temporal Difference Learning". W Reinforcement Learning, 33–53. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_3.
Pełny tekst źródłaWatkins, Christopher J. C. H., i Peter Dayan. "Technical Note". W Reinforcement Learning, 55–68. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_4.
Pełny tekst źródłaLin, Long-Ji. "Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching". W Reinforcement Learning, 69–97. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_5.
Pełny tekst źródłaSingh, Satinder Pal. "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks". W Reinforcement Learning, 99–115. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_6.
Pełny tekst źródłaDayan, Peter. "The Convergence of TD(λ) for General λ". W Reinforcement Learning, 117–38. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_7.
Pełny tekst źródłaMillán, José R., i Carme Torras. "A Reinforcement Connectionist Approach to Robot Path Finding in Non-Maze-Like Environments". W Reinforcement Learning, 139–71. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5_8.
Pełny tekst źródłaLorenz, Uwe. "Bestärkendes Lernen als Teilgebiet des Maschinellen Lernens". W Reinforcement Learning, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2_1.
Pełny tekst źródłaLorenz, Uwe. "Grundbegriffe des Bestärkenden Lernens". W Reinforcement Learning, 13–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2_2.
Pełny tekst źródłaStreszczenia konferencji na temat "Reinforcement Learning"
Yang, Kun, Chengshuai Shi i Cong Shen. "Teaching Reinforcement Learning Agents via Reinforcement Learning". W 2023 57th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2023. http://dx.doi.org/10.1109/ciss56502.2023.10089695.
Pełny tekst źródłaDoshi, Finale, Joelle Pineau i Nicholas Roy. "Reinforcement learning with limited reinforcement". W the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390189.
Pełny tekst źródłaLi, Zhiyi. "Reinforcement Learning". W SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3287324.3293703.
Pełny tekst źródłaShen, Shitian, i Min Chi. "Reinforcement Learning". W UMAP '16: User Modeling, Adaptation and Personalization Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2930238.2930247.
Pełny tekst źródłaKuroe, Yasuaki, i Kenya Takeuchi. "Sophisticated Swarm Reinforcement Learning by Incorporating Inverse Reinforcement Learning". W 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023. http://dx.doi.org/10.1109/smc53992.2023.10394525.
Pełny tekst źródłaLyu, Le, Yang Shen i Sicheng Zhang. "The Advance of Reinforcement Learning and Deep Reinforcement Learning". W 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2022. http://dx.doi.org/10.1109/eebda53927.2022.9744760.
Pełny tekst źródłaEpshteyn, Arkady, Adam Vogel i Gerald DeJong. "Active reinforcement learning". W the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390194.
Pełny tekst źródłaEpshteyn, Arkady, i Gerald DeJong. "Qualitative reinforcement learning". W the 23rd international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143883.
Pełny tekst źródłaVargas, Danilo Vasconcellos. "Evolutionary reinforcement learning". W GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3207865.
Pełny tekst źródłaLangford, John. "Contextual reinforcement learning". W 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8257902.
Pełny tekst źródłaRaporty organizacyjne na temat "Reinforcement Learning"
Singh, Satinder, Andrew G. Barto i Nuttapong Chentanez. Intrinsically Motivated Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2005. http://dx.doi.org/10.21236/ada440280.
Pełny tekst źródłaGhavamzadeh, Mohammad, i Sridhar Mahadevan. Hierarchical Multiagent Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2004. http://dx.doi.org/10.21236/ada440418.
Pełny tekst źródłaHarmon, Mance E., i Stephanie S. Harmon. Reinforcement Learning: A Tutorial. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1997. http://dx.doi.org/10.21236/ada323194.
Pełny tekst źródłaTadepalli, Prasad, i Alan Fern. Partial Planning Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2012. http://dx.doi.org/10.21236/ada574717.
Pełny tekst źródłaGhavamzadeh, Mohammad, i Sridhar Mahadevan. Hierarchical Average Reward Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 2003. http://dx.doi.org/10.21236/ada445728.
Pełny tekst źródłaJohnson, Daniel W. Drive-Reinforcement Learning System Applications. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1992. http://dx.doi.org/10.21236/ada264514.
Pełny tekst źródłaCleland, Andrew. Bounding Box Improvement With Reinforcement Learning. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.6322.
Pełny tekst źródłaLi, Jiajie. Learning Financial Investment Strategies using Reinforcement Learning and 'Chan theory'. Ames (Iowa): Iowa State University, sierpień 2022. http://dx.doi.org/10.31274/cc-20240624-946.
Pełny tekst źródłaBaird, III, Klopf Leemon C. i A. H. Reinforcement Learning With High-Dimensional, Continuous Actions. Fort Belvoir, VA: Defense Technical Information Center, listopad 1993. http://dx.doi.org/10.21236/ada280844.
Pełny tekst źródłaObert, James, i Angie Shia. Optimizing Dynamic Timing Analysis with Reinforcement Learning. Office of Scientific and Technical Information (OSTI), listopad 2019. http://dx.doi.org/10.2172/1573933.
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