Auswahl der wissenschaftlichen Literatur zum Thema „Off-Policy learning“
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Zeitschriftenartikel zum Thema "Off-Policy learning"
Meng, Wenjia, Qian Zheng, Gang Pan und Yilong Yin. „Off-Policy Proximal Policy Optimization“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 8 (26.06.2023): 9162–70. http://dx.doi.org/10.1609/aaai.v37i8.26099.
Der volle Inhalt der QuelleSchmitt, Simon, John Shawe-Taylor und Hado van Hasselt. „Chaining Value Functions for Off-Policy Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 8 (28.06.2022): 8187–95. http://dx.doi.org/10.1609/aaai.v36i8.20792.
Der volle Inhalt der QuelleXu, Da, Yuting Ye, Chuanwei Ruan und Bo Yang. „Towards Robust Off-Policy Learning for Runtime Uncertainty“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 9 (28.06.2022): 10101–9. http://dx.doi.org/10.1609/aaai.v36i9.21249.
Der volle Inhalt der QuellePeters, James F., und Christopher Henry. „Approximation spaces in off-policy Monte Carlo learning“. Engineering Applications of Artificial Intelligence 20, Nr. 5 (August 2007): 667–75. http://dx.doi.org/10.1016/j.engappai.2006.11.005.
Der volle Inhalt der QuelleYu, Jiayu, Jingyao Li, Shuai Lü und Shuai Han. „Mixed experience sampling for off-policy reinforcement learning“. Expert Systems with Applications 251 (Oktober 2024): 124017. http://dx.doi.org/10.1016/j.eswa.2024.124017.
Der volle Inhalt der QuelleCetin, Edoardo, und Oya Celiktutan. „Learning Pessimism for Reinforcement Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 6 (26.06.2023): 6971–79. http://dx.doi.org/10.1609/aaai.v37i6.25852.
Der volle Inhalt der QuelleKong, Seung-Hyun, I. Made Aswin Nahrendra und Dong-Hee Paek. „Enhanced Off-Policy Reinforcement Learning With Focused Experience Replay“. IEEE Access 9 (2021): 93152–64. http://dx.doi.org/10.1109/access.2021.3085142.
Der volle Inhalt der QuelleLi, Lihong. „A perspective on off-policy evaluation in reinforcement learning“. Frontiers of Computer Science 13, Nr. 5 (17.06.2019): 911–12. http://dx.doi.org/10.1007/s11704-019-9901-7.
Der volle Inhalt der QuelleLuo, Biao, Huai-Ning Wu und Tingwen Huang. „Off-Policy Reinforcement Learning for $ H_\infty $ Control Design“. IEEE Transactions on Cybernetics 45, Nr. 1 (Januar 2015): 65–76. http://dx.doi.org/10.1109/tcyb.2014.2319577.
Der volle Inhalt der QuelleSun, Mingfei, Sam Devlin, Katja Hofmann und Shimon Whiteson. „Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 8 (28.06.2022): 8378–85. http://dx.doi.org/10.1609/aaai.v36i8.20813.
Der volle Inhalt der QuelleDissertationen zum Thema "Off-Policy learning"
Hauser, Kristen. „Hyperparameter Tuning for Reinforcement Learning with Bandits and Off-Policy Sampling“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1613034993418088.
Der volle Inhalt der QuelleTosatto, Samuele [Verfasser], Jan [Akademischer Betreuer] Peters und Martha [Akademischer Betreuer] White. „Off-Policy Reinforcement Learning for Robotics / Samuele Tosatto ; Jan Peters, Martha White“. Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1227582293/34.
Der volle Inhalt der QuelleSakhi, Otmane. „Offline Contextual Bandit : Theory and Large Scale Applications“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.
Der volle Inhalt der QuelleThis thesis presents contributions to the problem of learning from logged interactions using the offline contextual bandit framework. We are interested in two related topics: (1) offline policy learning with performance certificates, and (2) fast and efficient policy learning applied to large scale, real world recommendation. For (1), we first leverage results from the distributionally robust optimisation framework to construct asymptotic, variance-sensitive bounds to evaluate policies' performances. These bounds lead to new, more practical learning objectives thanks to their composite nature and straightforward calibration. We then analyse the problem from the PAC-Bayesian perspective, and provide tighter, non-asymptotic bounds on the performance of policies. Our results motivate new strategies, that offer performance certificates before deploying the policies online. The newly derived strategies rely on composite learning objectives that do not require additional tuning. For (2), we first propose a hierarchical Bayesian model, that combines different signals, to efficiently estimate the quality of recommendation. We provide proper computational tools to scale the inference to real world problems, and demonstrate empirically the benefits of the approach in multiple scenarios. We then address the question of accelerating common policy optimisation approaches, particularly focusing on recommendation problems with catalogues of millions of items. We derive optimisation routines, based on new gradient approximations, computed in logarithmic time with respect to the catalogue size. Our approach improves on common, linear time gradient computations, yielding fast optimisation with no loss on the quality of the learned policies
Tosatto, Samuele. „Off-Policy Reinforcement Learning for Robotics“. Phd thesis, 2021. https://tuprints.ulb.tu-darmstadt.de/17536/1/thesis.pdf.
Der volle Inhalt der QuelleDelp, Michael. „Experiments in off-policy reinforcement learning with the GQ(lambda) algorithm“. Master's thesis, 2011. http://hdl.handle.net/10048/1762.
Der volle Inhalt der QuelleDiddigi, Raghuram Bharadwaj. „Reinforcement Learning Algorithms for Off-Policy, Multi-Agent Learning and Applications to Smart Grids“. Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5673.
Der volle Inhalt der QuelleBücher zum Thema "Off-Policy learning"
Kabay, Sarah. Access, Quality, and the Global Learning Crisis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192896865.001.0001.
Der volle Inhalt der QuelleStartz, Richard. Profit of Education. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798216001799.
Der volle Inhalt der QuelleBuchteile zum Thema "Off-Policy learning"
Li, Jinna, Frank L. Lewis und Jialu Fan. „Off-Policy Game Reinforcement Learning“. In Reinforcement Learning, 185–232. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9_7.
Der volle Inhalt der QuelleZhang, Li, Xin Li, Mingzhong Wang und Andong Tian. „Off-Policy Differentiable Logic Reinforcement Learning“. In Machine Learning and Knowledge Discovery in Databases. Research Track, 617–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_38.
Der volle Inhalt der QuelleCief, Matej, Jacek Golebiowski, Philipp Schmidt, Ziawasch Abedjan und Artur Bekasov. „Learning Action Embeddings for Off-Policy Evaluation“. In Lecture Notes in Computer Science, 108–22. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56027-9_7.
Der volle Inhalt der QuelleKlein, Edouard, Matthieu Geist und Olivier Pietquin. „Batch, Off-Policy and Model-Free Apprenticeship Learning“. In Lecture Notes in Computer Science, 285–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29946-9_28.
Der volle Inhalt der QuelleRak, Alexandra, Alexey Skrynnik und Aleksandr I. Panov. „Flexible Data Augmentation in Off-Policy Reinforcement Learning“. In Artificial Intelligence and Soft Computing, 224–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87986-0_20.
Der volle Inhalt der QuelleRak, Alexandra, Alexey Skrynnik und Aleksandr I. Panov. „Flexible Data Augmentation in Off-Policy Reinforcement Learning“. In Artificial Intelligence and Soft Computing, 224–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87986-0_20.
Der volle Inhalt der QuelleSteckelmacher, Denis, Hélène Plisnier, Diederik M. Roijers und Ann Nowé. „Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics“. In Machine Learning and Knowledge Discovery in Databases, 19–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46133-1_2.
Der volle Inhalt der QuelleRoettger, Frederic. „Reviewing On-Policy/Off-Policy Critic Learning in the Context of Temporal Differences and Residual Learning“. In Reinforcement Learning Algorithms: Analysis and Applications, 15–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-41188-6_2.
Der volle Inhalt der QuelleZhang, Qichao, Dongbin Zhao und Sibo Zhang. „Off-Policy Reinforcement Learning for Partially Unknown Nonzero-Sum Games“. In Neural Information Processing, 822–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_84.
Der volle Inhalt der QuelleWei, Qinglai, Ruizhuo Song, Benkai Li und Xiaofeng Lin. „Off-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems“. In Self-Learning Optimal Control of Nonlinear Systems, 201–14. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4080-1_9.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Off-Policy learning"
He, Li, Long Xia, Wei Zeng, Zhi-Ming Ma, Yihong Zhao und Dawei Yin. „Off-policy Learning for Multiple Loggers“. In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330864.
Der volle Inhalt der QuelleWhite, Adam, Joseph Modayil und Richard S. Sutton. „Scaling life-long off-policy learning“. In 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL). IEEE, 2012. http://dx.doi.org/10.1109/devlrn.2012.6400860.
Der volle Inhalt der QuelleZhang, Yan, und Michael M. Zavlanos. „Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus“. In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029969.
Der volle Inhalt der QuelleZheng, Bowen, und Ran Cheng. „Rethinking Population-assisted Off-policy Reinforcement Learning“. In GECCO '23: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583131.3590512.
Der volle Inhalt der QuelleCheng, Zhihao, Li Shen und Dacheng Tao. „Off-policy Imitation Learning from Visual Inputs“. In 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023. http://dx.doi.org/10.1109/icra48891.2023.10161566.
Der volle Inhalt der QuelleMiao, Dadong, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu und Yunjiang Jiang. „Sequential Search with Off-Policy Reinforcement Learning“. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459637.3481954.
Der volle Inhalt der QuelleJeunen, Olivier, Sean Murphy und Ben Allison. „Off-Policy Learning-to-Bid with AuctionGym“. In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599877.
Der volle Inhalt der QuelleSaito, Yuta, Himan Abdollahpouri, Jesse Anderton, Ben Carterette und Mounia Lalmas. „Long-term Off-Policy Evaluation and Learning“. In WWW '24: The ACM Web Conference 2024. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3589334.3645446.
Der volle Inhalt der QuelleJoseph, Ajin George, und Shalabh Bhatnagar. „Bounds for off-policy prediction in reinforcement learning“. In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966359.
Der volle Inhalt der QuelleMarvi, Zahra, und Bahare Kiumarsi. „Safe Off-policy Reinforcement Learning Using Barrier Functions“. In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147584.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Off-Policy learning"
Private sector and food security. Commercial Agriculture for Smallholders and Agribusiness (CASA), 2023. http://dx.doi.org/10.1079/20240191178.
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