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[1] Peter L. Bartlett,et al. Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions , 2013, NIPS.
[2] Chelsea Finn,et al. Deep Reinforcement Learning amidst Lifelong Non-Stationarity , 2020, ArXiv.
[3] Sindhu Padakandla. A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments , 2020, ArXiv.
[4] Robert L. Smith,et al. A Linear Programming Approach to Nonstationary Infinite-Horizon Markov Decision Processes , 2013, Oper. Res..
[5] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[6] Ufuk Topcu,et al. Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation , 2019, J. Mach. Learn. Res..
[7] Sherief Abdallah,et al. Addressing Environment Non-Stationarity by Repeating Q-learning Updates , 2016, J. Mach. Learn. Res..
[8] M. de Rijke,et al. When People Change their Mind: Off-Policy Evaluation in Non-stationary Recommendation Environments , 2019, WSDM.
[9] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[10] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[11] Peter Auer,et al. A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions , 2018, ArXiv.
[12] Pieter Abbeel,et al. Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments , 2017, ICLR.
[13] M. de Rijke,et al. Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model , 2019, IJCAI.
[14] Karthik Sridharan,et al. Online Learning with Predictable Sequences , 2012, COLT.
[15] Gabriel Dulac-Arnold,et al. Challenges of Real-World Reinforcement Learning , 2019, ArXiv.
[16] Victor R. Lesser,et al. Multi-Agent Learning with Policy Prediction , 2010, AAAI.
[17] Philip S. Thomas,et al. Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing , 2017, AAAI.
[18] Sergey Levine,et al. Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL , 2018, ICLR.
[19] Mehryar Mohri,et al. Optimistic Bandit Convex Optimization , 2016, NIPS.
[20] Michael H. Bowling,et al. Convergence and No-Regret in Multiagent Learning , 2004, NIPS.
[21] Vincent Conitzer,et al. AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents , 2003, Machine Learning.
[22] Mark B. Ring. Continual learning in reinforcement environments , 1995, GMD-Bericht.
[23] Henry Zhu,et al. Soft Actor-Critic Algorithms and Applications , 2018, ArXiv.
[24] Pieter Abbeel,et al. Adaptive Online Planning for Continual Lifelong Learning , 2019, ArXiv.
[25] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[26] Nan Jiang,et al. Doubly Robust Off-policy Value Evaluation for Reinforcement Learning , 2015, ICML.
[27] Masashi Sugiyama,et al. Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition , 2012, Neurocomputing.
[28] Shie Mannor,et al. Online learning in Markov decision processes with arbitrarily changing rewards and transitions , 2009, 2009 International Conference on Game Theory for Networks.
[29] Naoyuki Kubota,et al. Reinforcement Learning in non-stationary environments: An intrinsically motivated stress based memory retrieval performance (SBMRP) model , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[30] Sergey Levine,et al. Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning , 2018, ICLR.
[31] Yishay Mansour,et al. Nash Convergence of Gradient Dynamics in General-Sum Games , 2000, UAI.
[32] Guannan Qu,et al. Markov Decision Processes with Time-varying Transition Probabilities and Rewards , 2019 .
[33] Philip S. Thomas,et al. Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation , 2017, NIPS.
[34] Martha White,et al. Unifying Task Specification in Reinforcement Learning , 2016, ICML.
[35] Robert L. Smith,et al. Solution and Forecast Horizons for Infinite-Horizon Nonhomogeneous Markov Decision Processes , 2007, Math. Oper. Res..
[36] M. M. Hassan Mahmud,et al. Learning in non-stationary MDPs as transfer learning , 2013, AAMAS.
[37] Mehryar Mohri,et al. Accelerating Online Convex Optimization via Adaptive Prediction , 2016, AISTATS.
[38] David Simchi-Levi,et al. Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism , 2019 .
[39] Nolan Wagener,et al. An Online Learning Approach to Model Predictive Control , 2019, Robotics: Science and Systems.
[40] Jack Cuzick,et al. A strong law for weighted sums of i.i.d. random variables , 1995 .
[41] Robert L. Smith,et al. Solving Nonstationary Infinite Horizon Dynamic Optimization Problems , 2000 .
[42] Philip S. Thomas,et al. Safe Reinforcement Learning , 2015 .
[43] C. Cobelli,et al. The UVA/PADOVA Type 1 Diabetes Simulator , 2014, Journal of diabetes science and technology.
[44] Éva Tardos,et al. Learning in Games: Robustness of Fast Convergence , 2016, NIPS.
[45] Doina Precup,et al. Eligibility Traces for Off-Policy Policy Evaluation , 2000, ICML.
[46] Meysam Bastani,et al. Model-Free Intelligent Diabetes Management Using Machine Learning , 2014 .
[47] Jun-Kun Wang,et al. Optimistic Adaptive Acceleration for Optimization , 2019, ArXiv.
[48] Dit-Yan Yeung,et al. An Environment Model for Nonstationary Reinforcement Learning , 1999, NIPS.
[49] Peter Stone,et al. Bayesian Models of Nonstationary Markov Decision Processes , 2005 .
[50] Peter L. Bartlett,et al. Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning , 2001, J. Mach. Learn. Res..
[51] Philip S. Thomas,et al. Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning , 2016, ICML.
[52] Erwan Lecarpentier,et al. Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning , 2019, NeurIPS.
[53] Sergey Levine,et al. Online Meta-Learning , 2019, ICML.
[54] Martha White,et al. Meta-descent for Online, Continual Prediction , 2019, AAAI.
[55] Robert L. Smith,et al. A New Optimality Criterion for Nonhomogeneous Markov Decision Processes , 1987, Oper. Res..
[56] Omar Besbes,et al. Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards , 2014, NIPS.
[57] Shai Shalev-Shwartz,et al. Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..
[58] W. Greene,et al. 计量经济分析 = Econometric analysis , 2009 .
[59] Zhizhen Zhao,et al. Be Aware of Non-Stationarity: Nearly Optimal Algorithms for Piecewise-Stationary Cascading Bandits , 2019, ArXiv.
[60] Shie Mannor,et al. Rotting Bandits , 2017, NIPS.
[61] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[62] Yishay Mansour,et al. Experts in a Markov Decision Process , 2004, NIPS.
[63] Shimon Whiteson,et al. Learning with Opponent-Learning Awareness , 2017, AAMAS.
[64] Aurélien Garivier,et al. On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems , 2008, 0805.3415.
[65] Sebastian Thrun,et al. Lifelong Learning Algorithms , 1998, Learning to Learn.
[66] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[67] Eduardo W. Basso,et al. Reinforcement Learning in Non-Stationary Continuous Time and Space Scenarios , 2009 .
[68] Patrick M. Pilarski,et al. TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent , 2018, ArXiv.
[69] David Simchi-Levi,et al. Reinforcement Learning under Drift , 2019, ArXiv.
[70] Jonathan L. Shapiro,et al. Opponent Modelling by Sequence Prediction and Lookahead in Two-Player Games , 2013, ICAISC.
[71] Richard S. Sutton,et al. Weighted importance sampling for off-policy learning with linear function approximation , 2014, NIPS.
[72] Alessandro Lazaric,et al. Rotting bandits are no harder than stochastic ones , 2018, AISTATS.
[73] Corso Elvezia. A General Method for Incremental Self-improvement and Multi-agent Learning in Unrestricted Environments , 1996 .
[74] Larry D. Pyeatt,et al. Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers , 2014, J. Mach. Learn. Res..
[75] Archis Ghate,et al. Policy iteration for robust nonstationary Markov decision processes , 2016, Optim. Lett..