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Keyword

policy optimization

Description: Policy optimization is the process of finding or improving a decision strategy to maximize rewards or performance, especially in reinforcement learning.
OpenAlex ID: keywords/policy-optimization
Reinforcement Learning: An Introduction
1998 · R.S. Sutton, A.G. Barto · IEEE Transactions on Neural Networks · 27,181
Reinforcement Learning: An Introduction
2000 · Jeffrey D Johnson, Jinghong Li, et al. · Neurocomputing · 8,413
Policy Gradient Methods for Reinforcement Learning with Function Approximation
1999 · Richard S. Sutton, David McAllester, et al. · 5,150
Trust Region Policy Optimization
2015 · Schulman, John, Levine, Sergey, et al. · arXiv (Cornell University) · 4,369
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A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
2010 · Ross, Stephane, Gordon, Geoffrey J., et al. · arXiv (Cornell University) · 1,939
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Foundations of Inventory Management
2000 · Paul Zipkin · Medical Entomology and Zoology · 1,856
A comprehensive survey on safe reinforcement learning
2015 · Javier García, Fernando Fernández · Journal of Machine Learning Research · 1,119
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
2020 · Levine, Sergey, Kumar, Aviral, et al. · arXiv (Cornell University) · 824
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Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
2016 · Finn, Chelsea, Levine, Sergey, et al. · arXiv (Cornell University) · 638
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Reinforcement Learning based Recommender Systems: A Survey
2022 · M. Mehdi Afsar, Trafford Crump, et al. · ACM Computing Surveys · 506
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Batch Reinforcement Learning
2012 · Sascha Lange, Thomas Gabel, et al. · Adaptation, learning, and optimization · 387
Doubly Robust Policy Evaluation and Learning
2011 · Dudik, Miroslav, Langford, John, et al. · arXiv (Cornell University) · 383
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When to Trust Your Model: Model-Based Policy Optimization
2019 · Janner, Michael, Fu, Justin, et al. · arXiv (Cornell University) · 351
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Taming Decentralized POMDPs: Towards Efficient Policy Computation for Multiagent Settings
2003 · Ranjit Nair, R. Nair, et al. · 330
A unified framework for stochastic optimization
2018 · Warren B. Powell · European Journal of Operational Research · 317
Deep Reinforcement Learning
2020 · Springer eBooks · 240
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments
2021 · Sindhu Padakandla · ACM Computing Surveys · 239
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Dynamic power management based on continuous-time Markov decision processes
1999 · Qinru Qiu, Massoud Pedram · Proceedings - ACM IEEE Design Automation Conference · 221
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Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems
2022 · Joren Gijsbrechts, Robert N. Boute, et al. · Manufacturing & Service Operations Management · 203
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Learning Tetris Using the Noisy Cross-Entropy Method
2006 · István Szita, András Lörincz · Neural Computation · 203
Task-based End-to-end Model Learning in Stochastic Optimization
2017 · Donti, Priya L., Amos, Brandon, et al. · arXiv (Cornell University) · 202
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POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
2020 · Kwon, Yeong-Dae, Choo, Jinho, et al. · arXiv (Cornell University) · 186
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Off-Policy Actor-Critic
2012 · Degris, Thomas, White, Martha, et al. · arXiv (Cornell University) · 179
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Introduction to Reinforcement Learning
2020 · Zihan Ding, Yanhua Huang, et al. · Springer eBooks · 178
Doubly Robust Policy Evaluation and Optimization
2014 · Miroslav Dudík, Dumitru Erhan, et al. · 174
Works count: 3,507
Citation count: 74,300
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