mlsanityData to 7 Oct 2026

Online Regret Bounds for Undiscounted Continuous Reinforcement Learning

arXiv 1302.2550 ↗First posted 11 Feb 2013cs.LGcited by 17

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Abstract (arXiv)

We derive sublinear regret bounds for undiscounted reinforcement learning in continuous state space. The proposed algorithm combines state aggregation with the use of upper confidence bounds for implementing optimism in the face of uncertainty. Beside the existence of an optimal policy which satisfies the Poisson equation, the only assumptions made are Holder continuity of rewards and transition probabilities.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2013.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 4; 2025: 4.

17 papers.

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  1. 2021A Survey of Exploration Methods in Reinforcement Learningcited by 68
  2. 2021Sublinear Regret for Learning POMDPscited by 19
  3. 2022Learning to Control Linear Systems can be Hardcited by 10
  4. 2024No-Regret Reinforcement Learning in Smooth MDPscited by 6
  5. 2022Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systemscited by 5
  6. 2022Autoregressive Banditscited by 3
  7. 2021Coarse-Grained Smoothness for RL in Metric Spacescited by 2
  8. 2023Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platformscited by 1
  9. 2023Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehiclescited by 1
  10. 2024Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learningcited by 1
  11. 2024Efficient Learning of POMDPs with Known Observation Model in Average-Reward Settingcited by 1
  12. 2025Safe Learning Under Irreversible Dynamics via Asking for Helpcited by 1
5 more
  1. 2025A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Modelcited by 1
  2. 2021Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertaintycited by 0
  3. 2024Provably Adaptive Average Reward Reinforcement Learning for Metric Spacescited by 0
  4. 2025Achieving widetildeO(√T) Regret in Average-Reward POMDPs with Known Observation Modelscited by 0
  5. 2025DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual-Systemscited by 0