Gotowa bibliografia na temat „Off-Policy learning”
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Artykuły w czasopismach na temat "Off-Policy learning"
Meng, Wenjia, Qian Zheng, Gang Pan i 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.
Pełny tekst źródłaSchmitt, Simon, John Shawe-Taylor i 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.
Pełny tekst źródłaXu, Da, Yuting Ye, Chuanwei Ruan i 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.
Pełny tekst źródłaPeters, James F., i Christopher Henry. "Approximation spaces in off-policy Monte Carlo learning". Engineering Applications of Artificial Intelligence 20, nr 5 (sierpień 2007): 667–75. http://dx.doi.org/10.1016/j.engappai.2006.11.005.
Pełny tekst źródłaYu, Jiayu, Jingyao Li, Shuai Lü i Shuai Han. "Mixed experience sampling for off-policy reinforcement learning". Expert Systems with Applications 251 (październik 2024): 124017. http://dx.doi.org/10.1016/j.eswa.2024.124017.
Pełny tekst źródłaCetin, Edoardo, i 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.
Pełny tekst źródłaKong, Seung-Hyun, I. Made Aswin Nahrendra i 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.
Pełny tekst źródłaLi, 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.
Pełny tekst źródłaLuo, Biao, Huai-Ning Wu i Tingwen Huang. "Off-Policy Reinforcement Learning for $ H_\infty $ Control Design". IEEE Transactions on Cybernetics 45, nr 1 (styczeń 2015): 65–76. http://dx.doi.org/10.1109/tcyb.2014.2319577.
Pełny tekst źródłaSun, Mingfei, Sam Devlin, Katja Hofmann i 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.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaTosatto, Samuele [Verfasser], Jan [Akademischer Betreuer] Peters i 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.
Pełny tekst źródłaSakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaDelp, Michael. "Experiments in off-policy reinforcement learning with the GQ(lambda) algorithm". Master's thesis, 2011. http://hdl.handle.net/10048/1762.
Pełny tekst źródłaDiddigi, 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.
Pełny tekst źródłaKsiążki na temat "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.
Pełny tekst źródłaStartz, Richard. Profit of Education. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798216001799.
Pełny tekst źródłaCzęści książek na temat "Off-Policy learning"
Li, Jinna, Frank L. Lewis i Jialu Fan. "Off-Policy Game Reinforcement Learning". W Reinforcement Learning, 185–232. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9_7.
Pełny tekst źródłaZhang, Li, Xin Li, Mingzhong Wang i Andong Tian. "Off-Policy Differentiable Logic Reinforcement Learning". W 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.
Pełny tekst źródłaCief, Matej, Jacek Golebiowski, Philipp Schmidt, Ziawasch Abedjan i Artur Bekasov. "Learning Action Embeddings for Off-Policy Evaluation". W Lecture Notes in Computer Science, 108–22. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56027-9_7.
Pełny tekst źródłaKlein, Edouard, Matthieu Geist i Olivier Pietquin. "Batch, Off-Policy and Model-Free Apprenticeship Learning". W 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.
Pełny tekst źródłaRak, Alexandra, Alexey Skrynnik i Aleksandr I. Panov. "Flexible Data Augmentation in Off-Policy Reinforcement Learning". W Artificial Intelligence and Soft Computing, 224–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87986-0_20.
Pełny tekst źródłaRak, Alexandra, Alexey Skrynnik i Aleksandr I. Panov. "Flexible Data Augmentation in Off-Policy Reinforcement Learning". W Artificial Intelligence and Soft Computing, 224–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87986-0_20.
Pełny tekst źródłaSteckelmacher, Denis, Hélène Plisnier, Diederik M. Roijers i Ann Nowé. "Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics". W 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.
Pełny tekst źródłaRoettger, Frederic. "Reviewing On-Policy/Off-Policy Critic Learning in the Context of Temporal Differences and Residual Learning". W 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.
Pełny tekst źródłaZhang, Qichao, Dongbin Zhao i Sibo Zhang. "Off-Policy Reinforcement Learning for Partially Unknown Nonzero-Sum Games". W Neural Information Processing, 822–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_84.
Pełny tekst źródłaWei, Qinglai, Ruizhuo Song, Benkai Li i Xiaofeng Lin. "Off-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Off-Policy learning"
He, Li, Long Xia, Wei Zeng, Zhi-Ming Ma, Yihong Zhao i Dawei Yin. "Off-policy Learning for Multiple Loggers". W 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.
Pełny tekst źródłaWhite, Adam, Joseph Modayil i Richard S. Sutton. "Scaling life-long off-policy learning". W 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL). IEEE, 2012. http://dx.doi.org/10.1109/devlrn.2012.6400860.
Pełny tekst źródłaZhang, Yan, i Michael M. Zavlanos. "Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus". W 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029969.
Pełny tekst źródłaZheng, Bowen, i Ran Cheng. "Rethinking Population-assisted Off-policy Reinforcement Learning". W GECCO '23: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583131.3590512.
Pełny tekst źródłaCheng, Zhihao, Li Shen i Dacheng Tao. "Off-policy Imitation Learning from Visual Inputs". W 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023. http://dx.doi.org/10.1109/icra48891.2023.10161566.
Pełny tekst źródłaMiao, Dadong, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu i Yunjiang Jiang. "Sequential Search with Off-Policy Reinforcement Learning". W 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.
Pełny tekst źródłaJeunen, Olivier, Sean Murphy i Ben Allison. "Off-Policy Learning-to-Bid with AuctionGym". W 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.
Pełny tekst źródłaSaito, Yuta, Himan Abdollahpouri, Jesse Anderton, Ben Carterette i Mounia Lalmas. "Long-term Off-Policy Evaluation and Learning". W WWW '24: The ACM Web Conference 2024. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3589334.3645446.
Pełny tekst źródłaJoseph, Ajin George, i Shalabh Bhatnagar. "Bounds for off-policy prediction in reinforcement learning". W 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966359.
Pełny tekst źródłaMarvi, Zahra, i Bahare Kiumarsi. "Safe Off-policy Reinforcement Learning Using Barrier Functions". W 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147584.
Pełny tekst źródłaRaporty organizacyjne na temat "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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