mlsanityData to 7 Oct 2026

On Online Learning in Kernelized Markov Decision Processes

arXiv 1911.01871 ↗First posted 4 Nov 2019cs.LG stat.MLcited by 32

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Abstract (arXiv)

We develop algorithms with low regret for learning episodic Markov decision processes based on kernel approximation techniques. The algorithms are based on both the Upper Confidence Bound (UCB) as well as Posterior or Thompson Sampling (PSRL) philosophies, and work in the general setting of continuous state and action spaces when the true unknown transition dynamics are assumed to have smoothness induced by an appropriate Reproducing Kernel Hilbert Space (RKHS).

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 7; 2023: 6; 2024: 6; 2025: 3; 2026: 2.

32 papers.

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  1. 2021Representation Learning for Online and Offline RL in Low-rank MDPscited by 128
  2. 2021Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learningcited by 38
  3. 2021Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coveragecited by 26
  4. 2022Model-based Causal Bayesian Optimizationcited by 26
  5. 2021Differentially Private Regret Minimization in Episodic Markov Decision Processescited by 23
  6. 2023Efficient Exploration in Continuous-time Model-based Reinforcement Learningcited by 21
  7. 2023Kernelized Reinforcement Learning with Order Optimal Regret Boundscited by 20
  8. 2023Distributionally Robust Model-based Reinforcement Learning with Large State Spacescited by 17
  9. 2021Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learningcited by 14
  10. 2022Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planningcited by 12
  11. 2021Learning Good State and Action Representations via Tensor Decompositioncited by 7
  12. 2021Adaptive Control of Differentially Private Linear Quadratic Systemscited by 7
20 more
  1. 2022Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learningcited by 7
  2. 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systemscited by 7
  3. 2023Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learningcited by 7
  4. 2022Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learningcited by 5
  5. 2022Provably Efficient Kernelized Q-Learningcited by 4
  6. 2023STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learningcited by 4
  7. 2024Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPscited by 4
  8. 2023Safe Model-Based Multi-Agent Mean-Field Reinforcement Learningcited by 3
  9. 2024Causal Bayesian Optimization via Exogenous Distribution Learningcited by 3
  10. 2024Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Samplingcited by 3
  11. 2025No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processescited by 3
  12. 2021Model Selection for Generic Reinforcement Learningcited by 1
  13. 2022Model Selection in Reinforcement Learning with General Function Approximationscited by 1
  14. 2024Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learningcited by 1
  15. 2024Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regretcited by 1
  16. 2025Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learningcited by 1
  17. 2024Provably Adaptive Average Reward Reinforcement Learning for Metric Spacescited by 0
  18. 2025Reinforcement Learning Using known Invariancescited by 0
  19. 2026Learning Kernel-Based MDPs from Episodic Preferential Feedbackcited by 0
  20. 2026Information-Based Exploration via Random Features for Reinforcement Learningcited by 0