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Relevant bibliographies by topics / Off-Policy learning

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Academic literature on the topic 'Off-Policy learning'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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Journal articles on the topic "Off-Policy learning"

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Meng, Wenjia, Qian Zheng, Gang Pan, and Yilong Yin. "Off-Policy Proximal Policy Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9162–70. http://dx.doi.org/10.1609/aaai.v37i8.26099.

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Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved great success in sequential decision-making problems. However, PPO faces the issue of sample inefficiency, which is due to the PPO cannot make use of off-policy data. In this paper, we propose an Off-Policy Proximal Policy Optimization method (Off-Policy PPO) that improves the sample efficiency of PPO by utilizing off-policy data. Specifically, we first propose a clipped surrogate objective function that can utilize off-policy data and avoid excessively large policy updates. Next, we theoretica

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Schmitt, Simon, John Shawe-Taylor, and Hado van Hasselt. "Chaining Value Functions for Off-Policy Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8187–95. http://dx.doi.org/10.1609/aaai.v36i8.20792.

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To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its experience. This is important to learn counterfactuals, or because the experience was generated out of its own control. However, off-policy learning is non-trivial, and standard reinforcement-learning algorithms can be unstable and divergent. In this paper we discuss a novel family of off-policy prediction algorithms which are convergent by construction. The idea is to first learn on-policy about the data-generating b

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Yang, Shangdong, Dingyuanhao Sun, and Xingguo Chen. "Off-Policy Temporal Difference Learning with Bellman Residuals." Mathematics 12, no. 22 (2024): 3603. http://dx.doi.org/10.3390/math12223603.

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In reinforcement learning, off-policy temporal difference learning methods have gained significant attention due to their flexibility in utilizing existing data. However, traditional off-policy temporal difference methods often suffer from poor convergence and stability when handling complex problems. To address these issues, this paper proposes an off-policy temporal difference algorithm with Bellman residuals (TDBR). By incorporating Bellman residuals, the proposed algorithm effectively improves the convergence and stability of the off-policy learning process. This paper first introduces the

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Cief, Matej, Branislav Kveton, and Michal Kompan. "Cross-Validated Off-Policy Evaluation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16073–81. https://doi.org/10.1609/aaai.v39i15.33765.

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We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases.

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Xu, Da, Yuting Ye, Chuanwei Ruan, and Bo Yang. "Towards Robust Off-Policy Learning for Runtime Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10101–9. http://dx.doi.org/10.1609/aaai.v36i9.21249.

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Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment. However, during the real-time serving, we observe varieties of interventions and constraints that cause inconsistency between the online and offline setting, which we summarize and term as runtime uncertainty. Such uncertainty cannot be learned from the logged data due to its abnormality and rareness nature. To assert a certain level of robustness, we perturb the off-policy estimators along an adversarial direction in view of the runtime uncertainty. It allows the resulting estimators

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Peters, James F., and Christopher Henry. "Approximation spaces in off-policy Monte Carlo learning." Engineering Applications of Artificial Intelligence 20, no. 5 (2007): 667–75. http://dx.doi.org/10.1016/j.engappai.2006.11.005.

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Yu, Jiayu, Jingyao Li, Shuai Lü, and Shuai Han. "Mixed experience sampling for off-policy reinforcement learning." Expert Systems with Applications 251 (October 2024): 124017. http://dx.doi.org/10.1016/j.eswa.2024.124017.

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Cetin, Edoardo, and Oya Celiktutan. "Learning Pessimism for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6971–79. http://dx.doi.org/10.1609/aaai.v37i6.25852.

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Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected target returns. In this work, we propose Generalized Pessimism Learning (GPL), a strategy employing a novel learnable penalty to enact such pessimism. In particular, we propose to learn this penalty alongside the critic with dual TD-learning, a new procedure to estimate and minimize the magnitude of the target returns bias with trivial computational cost. GPL enables us to accurately counteract overestimation bias t

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Sun, Mingfei, Sam Devlin, Katja Hofmann, and Shimon Whiteson. "Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8378–85. http://dx.doi.org/10.1609/aaai.v36i8.20813.

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Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation to be off-policy regardless of the fact that these off-policy extensions could either change the original objective or involve complicated optimization. We revisit the foundation of adversarial imitation and propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. Our formulation capitalizes on two key insights: (1) the similarity between the Bellman equation and t

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Yang, Shangdong, Shuaiqiang Zhang, and Xingguo Chen. "Online Attentive Kernel-Based Off-Policy Temporal Difference Learning." Applied Sciences 14, no. 23 (2024): 11114. http://dx.doi.org/10.3390/app142311114.

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Temporal difference (TD) learning is a powerful framework for value function approximation in reinforcement learning. However, standard TD methods often struggle with feature representation and off-policy learning challenges. In this paper, we propose a novel framework, online attentive kernel-based off-policy TD learning, and in combination with well-known algorithms, introduce OAKGTD2, OAKTDC, and OAKETD. This framework uses two-timescale optimization. In the slow-timescale, a sparse representation of state features is learned using an online attentive kernel-based method. In the fast-timesc

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Dissertations / Theses on the topic "Off-Policy learning"

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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.

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Tosatto, Samuele [Verfasser], Jan [Akademischer Betreuer] Peters, and 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.

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Sakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.

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Cette thèse s'intéresse au problème de l'apprentissage à partir d'interactions en utilisant le cadre du bandit contextuel hors ligne. En particulier, nous nous intéressons à deux sujets connexes : (1) l'apprentissage de politiques hors ligne avec des certificats de performance, et (2) l'apprentissage rapide et efficace de politiques, pour le problème de recommandation à grande échelle. Pour (1), nous tirons d'abord parti des résultats du cadre d'optimisation distributionnellement robuste pour construire des bornes asymptotiques, sensibles à la variance, qui permettent l'évaluation des performa

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Tosatto, Samuele. "Off-Policy Reinforcement Learning for Robotics." Phd thesis, 2021. https://tuprints.ulb.tu-darmstadt.de/17536/1/thesis.pdf.

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Nowadays, industrial processes are vastly automated by means of robotic manipulators. In some cases, robots occupy a large fraction of the production line, performing a rich range of tasks. In contrast to their tireless ability to repeatedly perform the same tasks with millimetric precision, current robotics exhibits low adaptability to new scenarios. This lack of adaptability in many cases hinders a closer human-robot interaction; furthermore, when one needs to apply some change to the production line, the robots need to be reconfigured by highly-qualified figures. Machine learning and, more

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Delp, Michael. "Experiments in off-policy reinforcement learning with the GQ(lambda) algorithm." Master's thesis, 2011. http://hdl.handle.net/10048/1762.

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Off-policy reinforcement learning is useful in many contexts. Maei, Sutton, Szepesvari, and others, have recently introduced a new class of algorithms, the most advanced of which is GQ(lambda), for off-policy reinforcement learning. These algorithms are the first stable methods for general off-policy learning whose computational complexity scales linearly with the number of parameters, thereby making them potentially applicable to large applications involving function approximation. Despite these promising theoretical properties, these algorithms have received no significant empirical test of

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Diddigi, 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.

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Reinforcement Learning (RL) algorithms are a popular class of algorithms for training an agent to learn desired behavior through interaction with an environment whose dynamics is unknown to the agent. RL algorithms combined with neural network architectures have enjoyed much success in various disciplines like games, medicine, energy management, economics and supply chain management. In our thesis, we study interesting extensions of standard single-agent RL settings, like off-policy and multi-agent settings. We discuss the motivations and importance of these settings and propose convergen

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Books on the topic "Off-Policy learning"

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Kabay, Sarah. Access, Quality, and the Global Learning Crisis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192896865.001.0001.

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Around the world, 250 million children cannot read, write, or perform basic mathematics. They represent almost 40 percent of all primary school-aged children. This situation has come to be called the “Global Learning Crisis,” and it is one of the most critical challenges facing the world today. Work to address this situation depends on how it is understood. Typically, the Global Learning Crisis and efforts to improve primary education are defined in relation to two terms: access and quality. This book is focused on the connection between them. In a mixed-methods case study, this book provides

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Startz, Richard. Profit of Education. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798216001799.

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This important book translates evidence and examines policy, proposing a plan to save America's schools by rewarding teachers with professional-level salaries distributed wisely. Profit of Education makes it clear that rethinking the teaching profession is the key to repairing America's broken-down education system and securing our nation's future. Accomplishing that, author Dick Startz says, requires lifting teacher pay to professional levels and rewarding teachers for student success, with the goal of improving student learning by the equivalent of one extra year of schooling. Profit of Educ

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Book chapters on the topic "Off-Policy learning"

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Li, Jinna, Frank L. Lewis, and Jialu Fan. "Off-Policy Game Reinforcement Learning." In Reinforcement Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9_7.

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Zhang, Li, Xin Li, Mingzhong Wang, and Andong Tian. "Off-Policy Differentiable Logic Reinforcement Learning." In Machine Learning and Knowledge Discovery in Databases. Research Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_38.

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Cief, Matej, Jacek Golebiowski, Philipp Schmidt, Ziawasch Abedjan, and Artur Bekasov. "Learning Action Embeddings for Off-Policy Evaluation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56027-9_7.

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Klein, Edouard, Matthieu Geist, and Olivier Pietquin. "Batch, Off-Policy and Model-Free Apprenticeship Learning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29946-9_28.

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Rak, Alexandra, Alexey Skrynnik, and Aleksandr I. Panov. "Flexible Data Augmentation in Off-Policy Reinforcement Learning." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87986-0_20.

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Rak, Alexandra, Alexey Skrynnik, and Aleksandr I. Panov. "Flexible Data Augmentation in Off-Policy Reinforcement Learning." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87986-0_20.

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Steckelmacher, Denis, Hélène Plisnier, Diederik M. Roijers, and Ann Nowé. "Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46133-1_2.

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Roettger, Frederic. "Reviewing On-Policy/Off-Policy Critic Learning in the Context of Temporal Differences and Residual Learning." In Reinforcement Learning Algorithms: Analysis and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-41188-6_2.

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Zhang, Qichao, Dongbin Zhao, and Sibo Zhang. "Off-Policy Reinforcement Learning for Partially Unknown Nonzero-Sum Games." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_84.

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Mohammed, Abdul Sami, and Emmanuel Okafor. "Off-Policy Inspired Imitation Learning for Generation of Adversarial Malware." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-96231-8_19.

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Conference papers on the topic "Off-Policy learning"

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Zhang, Jie, Yirong Yao, Wei He, Yiqun Niu, and Chongjun Wang. "Regret Optimization Experience Replay in Off-Policy Reinforcement Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889888.

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Wang, Mi, and Huai-Ning Wu. "Data-Driven Inverse Cooperative Game Control via Off-Policy Q-Learning." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662319.

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Lee, Donghwan. "Analysis of Off-Policy Multi-Step TD-Learning with Linear Function Approximation." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886545.

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Jha, Mayank Shekhar, Bahare Kiumarsi, and Didier Theilliol. "Safe Reinforcement Learning Based on Off-Policy Approach for Nonlinear Discrete-Time Systems." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644965.

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Shen, MinYin, and Fei Liu. "H∞ Tracking Control of Tow Time Scale Linear System Based on Off-Policy Reinforcement Learning." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606893.

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Mullachery, Athira, and Shaikshavali Chitraganti. "Off-policy Reinforcement Learning for a Robust Optimal Control Problem with Real Parametric Uncertainty." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886480.

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Morihira, Naoki, Pranav Deo, Manoj Bhadu, et al. "Touch-Based Manipulation with Multi-Fingered Robot using Off-policy RL and Temporal Contrastive Learning." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610239.

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Tekumatla, Shiva Kumar, Varun Gampa, and Siavash Farzan. "Learning-Based Design of Off-Policy Gaussian Controllers: Integrating Model Predictive Control and Gaussian Process Regression." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644559.

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White, Adam, Joseph Modayil, and 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.

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He, Li, Long Xia, Wei Zeng, Zhi-Ming Ma, Yihong Zhao, and Dawei Yin. "Off-policy Learning for Multiple Loggers." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2019. http://dx.doi.org/10.1145/3292500.3330864.

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Reports on the topic "Off-Policy learning"

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Cai, Manlin, Donna Lero, and Sylvia Fuller. Policy Brief: Care/Work Policies for Managing Routine and Unpredictable Caregiving. The Vanier Institute of the Family, 2025. https://doi.org/10.61959/esnt2797e.

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ABSTRACT Employed parents of young children must manage a variety of routine day-to-day caregiving responsibilities. These can be predictable, such as school or child care pickups, or unpredictable, such as caring for a sick child or coping with child care disruptions. Care/work policies, including flexible work arrangements, family responsibility leave, and sick leave, can play a critical role in enabling parents and other caregivers to meet these demands. This policy brief reviews the current regulations for key employment rights in relation to routine caregiving responsibilities under the C

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Lunn, Pete, Marek Bohacek, Jason Somerville, Áine Ní Choisdealbha, and Féidhlim McGowan. PRICE Lab: An Investigation of Consumers’ Capabilities with Complex Products. ESRI, 2016. https://doi.org/10.26504/bkmnext306.

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Executive Summary This report describes a series of experiments carried out by PRICE Lab, a research programme at the Economic and Social Research Institute (ESRI) jointly funded by the Central Bank of Ireland, the Commission for Energy Regulation, the Competition and Consumer Protection Commission and the Commission for Communications Regulation. The experiments were conducted with samples of Irish consumers aged 18-70 years and were designed to answer the following general research question: At what point do products become too complex for consumers to choose accurately between the good ones

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Private sector and food security. Commercial Agriculture for Smallholders and Agribusiness (CASA), 2023. http://dx.doi.org/10.1079/20240191178.

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The global community is facing escalating acute food insecurity crises, predominantly in Sub- Saharan Africa, due to climate change, the Russia-Ukraine conflict, and COVID-19 shocks. Related impacts on donor government budgets, domestic conflicts and limited fiscal capacity in countries already experiencing acute food insecurity, often on top of high chronic food insecurity levels, further exacerbate the issue. This policy brief examines the potential of private sector financing to alleviate acute food insecurity, through providing a targeted review of key mechanisms for mobilizing private sec

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