$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank | Paper Citation Record

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This paper cites Schapire.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Schapire

Reference 1

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This paper cites Eﬃcient optimal learning for contextual bandits.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Eﬃcient optimal learning for contextual bandits

Reference 2

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This paper cites Dynamic programming and optimal control , volume 1.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Dynamic programming and optimal control , volume 1

Reference 3

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Observation 60f2e34c-8475-4f9c-b361-b942afa71e2e · outbound

This paper cites Approximate Dynamic Programming: Solving the curses of dim ensionality, volume.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Approximate Dynamic Programming: Solving the curses of dim ensionality, volume

Reference 4

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Observation 9aa0314a-1dfb-46f4-9f31-76c614c0cf63 · outbound

This paper cites Human-level control through deep reinforcement learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Human-level control through deep reinforcement learning

Reference 5

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Observation a3b5f717-4263-45b2-b6bf-bd38c4a7d0b2 · outbound

This paper cites Unifying count-based exploration and intrinsic motivation.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Unifying count-based exploration and intrinsic motivation

Reference 6

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This paper cites Mastering the game of go with deep neural networks and tree search.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Mastering the game of go with deep neural networks and tree search

Reference 7

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Observation 60df2f6d-336c-4dba-845a-2f26467e27b2 · outbound

This paper cites Ma stering the game of go without human knowledge.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Ma stering the game of go without human knowledge

Reference 8

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Observation ab1668e3-a944-41da-9d4f-f4008e991b56 · outbound

This paper cites Deep reinforcement learning for robotic manipulation with asynchronous oﬀ-policy updates.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Deep reinforcement learning for robotic manipulation with asynchronous oﬀ-policy updates

Reference 9

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Observation c6d76ad9-7b13-42ce-85ed-7ef61dd5d296 · outbound

This paper cites Is Q-learning provably eﬃcient? In Proceedings of Advances in Neural Information Processing S ystems (NeurIPS) , pages 4863–4873,.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Is Q-learning provably eﬃcient? In Proceedings of Advances in Neural Information Processing S ystems (NeurIPS) , pages 4863–4873,

Reference 10

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Observation 7541a362-e33a-4c63-b5bc-e4617e947fd7 · outbound

This paper cites Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

Reference 11

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Observation c6ff1699-9e4d-4039-a20c-1bfea857f4f2 · outbound

This paper cites Min imax regret bounds for reinforcement learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Min imax regret bounds for reinforcement learning

Reference 12

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Observation 6457e25c-b60e-4e10-aab5-a021349f3c44 · outbound

This paper cites Pac model-free reinforcement learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Pac model-free reinforcement learning

Reference 13

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Observation 53dad104-a1ff-48bf-b438-5895be4eede5 · outbound

This paper cites Speedy q-learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Speedy q-learning

Reference 14

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Observation 3710c4c3-634f-44ba-8bd7-1d9bd8e7c6f3 · outbound

This paper cites Learning rates for q-lear ning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Learning rates for q-lear ning

Reference 15

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Observation 012aa6c1-db3b-4f91-b3da-ced392c3c3af · outbound

This paper cites Variance re duced value iteration and faster al- gorithms for solving markov decision processes.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Variance re duced value iteration and faster al- gorithms for solving markov decision processes

Reference 16

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Observation f99b3821-9f17-4986-b7ab-706eb62a1154 · outbound

This paper cites An analysis of bid-price con trols for network revenue manage- ment.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank An analysis of bid-price con trols for network revenue manage- ment

Reference 17

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This paper cites Dynamic bid prices in revenue management.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Dynamic bid prices in revenue management

Reference 18

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Observation b31dc2dc-f577-4717-b60e-64d439a354da · outbound

This paper cites Asynchronous metho ds for deep reinforcement learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Asynchronous metho ds for deep reinforcement learning

Reference 19

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This paper cites The malmo platform for arti- ﬁcial intelligence experimentation.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank The malmo platform for arti- ﬁcial intelligence experimentation

Reference 20

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This paper cites Gambling in a rigged casino: The adversarial multi-armed bandit problem.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Gambling in a rigged casino: The adversarial multi-armed bandit problem

Reference 21

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Observation 0aa6861f-a9e4-4934-9c9d-2c2a5a5c2caa · outbound

This paper cites Learning from delayed rewards.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Learning from delayed rewards

Reference 22

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Observation a9b4beea-ee82-4b7a-9ff5-064f5027c09f · outbound

This paper cites Q-learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Q-learning

Reference 23

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Observation 543ac82f-4627-4459-b740-67a81400fc24 · outbound

This paper cites Asynchronous stochastic approximation and q -learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Asynchronous stochastic approximation and q -learning

Reference 24

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Observation fa802055-7200-4088-b23e-ee942157aadc · outbound

This paper cites Regu- larized policy iteration with nonparametric function spaces.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Regu- larized policy iteration with nonparametric function spaces

Reference 25

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Observation be1358dd-9d80-4b2a-aa15-aa4a969a9253 · outbound

This paper cites Finite-sample analysis of least- squares policy iteration.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Finite-sample analysis of least- squares policy iteration

Reference 26

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Observation 1bdd4641-48d3-470b-b6f8-d777a05c04ac · outbound

This paper cites Learning near-optimal policies with bellman-residual minimization based ﬁtted policy iteration and a single sample path.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Learning near-optimal policies with bellman-residual minimization based ﬁtted policy iteration and a single sample path

Reference 27

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This paper cites Finite-time bounds for ﬁt ted value iteration.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Finite-time bounds for ﬁt ted value iteration

Reference 28

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Observation 056c1862-9c91-4e10-a7de-62d4e0d4e10f · outbound

This paper cites Information-theoretic consideratio ns in batch reinforcement learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Information-theoretic consideratio ns in batch reinforcement learning

Reference 29

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Observation 0002a454-f72e-466d-ac60-e94f78eff01d · outbound

This paper cites Stable function approximation in dynamic pro gramming.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Stable function approximation in dynamic pro gramming

Reference 30

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Observation 70db911d-5bc8-49d7-9b49-3b148c7fdae3 · outbound

This paper cites Provably Efficient Reinforcement Learning with Linear Function Approximation.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Provably Efficient Reinforcement Learning with Linear Function Approximation

Reference 31

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Observation 6d6e05e9-b7c9-4ff2-abcb-f222d4938260 · outbound

This paper cites On oracle-eﬃcient pac rl with rich observations.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank On oracle-eﬃcient pac rl with rich observations

Reference 32

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Observation c6df8ebc-569a-4987-95c3-27c4c4c98a16 · outbound

This paper cites Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, M iroslav Dudik, and John Langford.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, M iroslav Dudik, and John Langford

Reference 33

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Observation 96047cdd-2dd3-4e8d-b827-f1a3f13fc13c · outbound

This paper cites Eﬃcient reinforcement learnin g in deterministic systems with value function generalization.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Eﬃcient reinforcement learnin g in deterministic systems with value function generalization

Reference 34

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Observation 4ea3b8f1-d38c-4c25-8cb4-a165513d1543 · outbound

This paper cites N ear-optimal time and sample complexities for solving discounted markov decision process with a ge nerative model.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank N ear-optimal time and sample complexities for solving discounted markov decision process with a ge nerative model

Reference 35

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Observation 7efce2da-627c-4d07-a012-eb34ddf29243 · outbound

This paper cites Glo bal convergence of policy gradient methods for linearized control problems.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Glo bal convergence of policy gradient methods for linearized control problems

Reference 36

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Observation 48344bfe-79b9-435f-a0a5-561279b0772f · outbound

This paper cites Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning

Reference 37

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Observation b7573e80-b58a-4011-874b-6b5b620b634d · outbound

This paper cites Taming the monster: A fast and simple algorithm for contextual bandits.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Taming the monster: A fast and simple algorithm for contextual bandits

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.112407Z

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Observation d86242a7-485e-42aa-905c-954229a22d59 · outbound

This paper cites Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound

Reference 39

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Observation 51440e06-2d73-446b-9d59-e3b16f2cbdea · outbound

This paper cites On learning sets and functions.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank On learning sets and functions

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.101367Z

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Observation d12b1613-a872-4dc4-b587-ae450c1e7b5c · outbound

This paper cites Decision theoretic generalizations of the pac mo del for neural net and other learning applications.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Decision theoretic generalizations of the pac mo del for neural net and other learning applications

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.090701Z

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Observation 05a60069-8a0e-4796-9b03-781f3fdfc24a · outbound

This paper cites Rates of convergence in the central limit the orem for empirical processes.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Rates of convergence in the central limit the orem for empirical processes

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.080068Z

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Observation c2f41ed6-565d-470f-a3d2-221fc359dfa0 · outbound

This paper cites On general minimax theorems.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank On general minimax theorems

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.067730Z

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Observation 4eb87988-5959-4ab2-b515-148935a61a47 · outbound

This paper cites On minimum volume ellipsoids containing part of a give n ellipsoid.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank On minimum volume ellipsoids containing part of a give n ellipsoid

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.054233Z

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Observation c7aa974c-3be4-41e0-9f41-d2f8ae213fef · outbound

This paper cites Sphere packing numbers for subsets of the bo olean n-cube with bounded vapnik- chervonenkis dimension.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Sphere packing numbers for subsets of the bo olean n-cube with bounded vapnik- chervonenkis dimension

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.040507Z

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Observation 2c84bc67-97d4-4741-84f9-54cdb046dbd1 · outbound

This paper cites Springer Science & Business Media, 2013.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Springer Science & Business Media, 2013

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.027802Z

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Observation aced5f6a-38ae-4bb1-adfe-07eddab1dd2a · outbound

This paper cites Convergence of stochastic processes.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Convergence of stochastic processes

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:54:14.015789Z

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Observation a1d8be10-b6d3-4b4e-9381-4490a608b6b0 · outbound

This paper cites an unresolved cited work.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Unresolved cited work

Reference 703

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