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Exploration in Structured Reinforcement Learning
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Exploration in Structured Reinforcement Learning (2018)Exploration in Structured Reinforcement L…Minimal Exploration in Structured Stochastic Bandits (2017)Minimal Exploration in Struct…On Lower Bounds for Regret in Reinforcement Learning (2016)On Lower Bounds for Regret in…Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms (2014)Unimodal Bandits: Regret Lowe…On the Complexity of Best Arm Identification in Multi-Armed Bandit Models (2014)On the Complexity of Best Arm…Model-based Reinforcement Learning and the Eluder Dimension (2014)Model-based Reinforcement Lea…Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms (2014)Lipschitz Bandits: Regret Low…Near-optimal Regret Bounds for Reinforcement Learning (2010)Near-optimal Regret Bounds fo…Optimistic linear programming gives logarithmic regret for irreducible MDPs (2007)Optimistic linear programming…Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning (2007)Logarithmic Online Regret Bou…Some aspects of the sequential design of experiments (1952)Finite-time Analysis of the Multiarmed Bandit Problem (2002)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Non-stochastic Best Arm Identification and Hyperparameter Optimization (2015)Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm (2017)A unified framework for stochastic optimization (2018)The Nonstochastic Multiarmed Bandit Problem (2002)Regret Bounds for the Adaptive Control of Linear Quadratic Systems (2011)Confronting Deep Uncertainties in Risk Analysis (2012)A Tutorial on Thompson Sampling (2018)Using confidence bounds for exploitation-exploration trade-offs (2003)Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits (2014)Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits (2015)Online Reinforcement Learning in Stochastic Games (2017)Improved Algorithms for Linear Stochastic Bandits (2011)Thompson Sampling for Learning Parameterized Markov Decision Processes (2014)On Thompson Sampling and Asymptotic Optimality (2017)Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems (2018)Prediction, Learning, and Games (2006)Lipschitz Bandits: Regret Lower Bound and Optimal Algorithms (2014)Non-Asymptotic Pure Exploration by Solving Games (2019)Sliding-Window Thompson Sampling for Non-Stationary Settings (2020)Input perturbations for adaptive control and learning (2020)Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism (2020)Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon (2020)Pure Exploration with Multiple Correct Answers (2019)Multi-Armed Bandits With Correlated Arms (2021)Multiple-Play Bandits in the Position-Based Model (2016)Stochastic Online Shortest Path Routing: The Value of Feedback (2017)Structural causal bandits: where to intervene? (2018)
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References

  • 2010Near-optimal Regret Bounds for Reinforcement Learning718 cited
  • 2014On the Complexity of Best Arm Identification in Multi-Armed Bandit Models704 cited
  • 2007Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning170 cited
  • 2017Minimal Exploration in Structured Stochastic Bandits113 cited
  • 2014Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms97 cited
  • 2014Model-based Reinforcement Learning and the Eluder Dimension75 cited
  • 2014Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms62 cited
  • 2016On Lower Bounds for Regret in Reinforcement Learning46 cited
  • 2007Optimistic linear programming gives logarithmic regret for irreducible MDPs40 cited

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