Literatura científica selecionada sobre o tema "Off-Policy learning"
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Artigos de revistas sobre o assunto "Off-Policy learning"
Meng, Wenjia, Qian Zheng, Gang Pan e Yilong Yin. "Off-Policy Proximal Policy Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junho de 2023): 9162–70. http://dx.doi.org/10.1609/aaai.v37i8.26099.
Texto completo da fonteSchmitt, Simon, John Shawe-Taylor e Hado van Hasselt. "Chaining Value Functions for Off-Policy Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junho de 2022): 8187–95. http://dx.doi.org/10.1609/aaai.v36i8.20792.
Texto completo da fonteXu, Da, Yuting Ye, Chuanwei Ruan e Bo Yang. "Towards Robust Off-Policy Learning for Runtime Uncertainty". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 9 (28 de junho de 2022): 10101–9. http://dx.doi.org/10.1609/aaai.v36i9.21249.
Texto completo da fontePeters, James F., e Christopher Henry. "Approximation spaces in off-policy Monte Carlo learning". Engineering Applications of Artificial Intelligence 20, n.º 5 (agosto de 2007): 667–75. http://dx.doi.org/10.1016/j.engappai.2006.11.005.
Texto completo da fonteYu, Jiayu, Jingyao Li, Shuai Lü e Shuai Han. "Mixed experience sampling for off-policy reinforcement learning". Expert Systems with Applications 251 (outubro de 2024): 124017. http://dx.doi.org/10.1016/j.eswa.2024.124017.
Texto completo da fonteCetin, Edoardo, e Oya Celiktutan. "Learning Pessimism for Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junho de 2023): 6971–79. http://dx.doi.org/10.1609/aaai.v37i6.25852.
Texto completo da fonteKong, Seung-Hyun, I. Made Aswin Nahrendra e 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.
Texto completo da fonteLi, Lihong. "A perspective on off-policy evaluation in reinforcement learning". Frontiers of Computer Science 13, n.º 5 (17 de junho de 2019): 911–12. http://dx.doi.org/10.1007/s11704-019-9901-7.
Texto completo da fonteLuo, Biao, Huai-Ning Wu e Tingwen Huang. "Off-Policy Reinforcement Learning for $ H_\infty $ Control Design". IEEE Transactions on Cybernetics 45, n.º 1 (janeiro de 2015): 65–76. http://dx.doi.org/10.1109/tcyb.2014.2319577.
Texto completo da fonteSun, Mingfei, Sam Devlin, Katja Hofmann e Shimon Whiteson. "Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junho de 2022): 8378–85. http://dx.doi.org/10.1609/aaai.v36i8.20813.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteTosatto, Samuele [Verfasser], Jan [Akademischer Betreuer] Peters e 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.
Texto completo da fonteSakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.
Texto completo da fonteThis 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.
Texto completo da fonteDelp, Michael. "Experiments in off-policy reinforcement learning with the GQ(lambda) algorithm". Master's thesis, 2011. http://hdl.handle.net/10048/1762.
Texto completo da fonteDiddigi, 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.
Texto completo da fonteLivros sobre o assunto "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.
Texto completo da fonteStartz, Richard. Profit of Education. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798216001799.
Texto completo da fonteCapítulos de livros sobre o assunto "Off-Policy learning"
Li, Jinna, Frank L. Lewis e 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.
Texto completo da fonteZhang, Li, Xin Li, Mingzhong Wang e 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.
Texto completo da fonteCief, Matej, Jacek Golebiowski, Philipp Schmidt, Ziawasch Abedjan e 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.
Texto completo da fonteKlein, Edouard, Matthieu Geist e 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.
Texto completo da fonteRak, Alexandra, Alexey Skrynnik e 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.
Texto completo da fonteRak, Alexandra, Alexey Skrynnik e 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.
Texto completo da fonteSteckelmacher, Denis, Hélène Plisnier, Diederik M. Roijers e 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.
Texto completo da fonteRoettger, 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.
Texto completo da fonteZhang, Qichao, Dongbin Zhao e 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.
Texto completo da fonteWei, Qinglai, Ruizhuo Song, Benkai Li e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Off-Policy learning"
He, Li, Long Xia, Wei Zeng, Zhi-Ming Ma, Yihong Zhao e 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.
Texto completo da fonteWhite, Adam, Joseph Modayil e 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.
Texto completo da fonteZhang, Yan, e 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.
Texto completo da fonteZheng, Bowen, e 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.
Texto completo da fonteCheng, Zhihao, Li Shen e 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.
Texto completo da fonteMiao, Dadong, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu e 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.
Texto completo da fonteJeunen, Olivier, Sean Murphy e 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.
Texto completo da fonteSaito, Yuta, Himan Abdollahpouri, Jesse Anderton, Ben Carterette e 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.
Texto completo da fonteJoseph, Ajin George, e 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.
Texto completo da fonteMarvi, Zahra, e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "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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