On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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10 results for “LSVI-UCB++”

Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximationstat.ML

Improved gap-dependent bounds for reinforcement learning with linear approximations.

problem Achieving nearly minimax-optimal performance with linear function approximation.

method Developed and analyzed the LSVI-UCB++ algorithm and its concurrent variant.

result First gap-dependent regret bound for nearly minimax-optimal algorithm LSVI-UCB++.

reinforcement learning linear function approximation gap-dependent bounds LSVI-UCB++

2026-02-23abs ↗pdf ↗

Towards Optimal Differentially Private Regret Bounds in Linear MDPscs.LG

Improved privacy in RL with near-optimal regret bounds.

problem Privacy-preserving reinforcement learning in personalized decision-making systems.

method Differentially private algorithm based on LSVI-UCB++ with privacy-preserving techniques.

result Achieved a near-optimal regret bound of O(d * sqrt(H^3 * K) + H^(15/4) * d^(7/6) * K^(1/2) / ε).

differential privacy reinforcement learning linear MDPs regret minimization personalized decision-making

2025-04-12abs ↗pdf ↗

Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraintscs.LG

Efficient RL algorithms for linear function approximation with limited adaptivity constraints.

problem Limited adaptivity in reinforcement learning with linear function approximation.

method Proposed two efficient online RL algorithms for episodic linear Markov decision processes under batch learning and rare policy switch models.

result Achieved efficient regret bounds for both batch learning and rare policy switch models, with substantial reduction in adaptivity.

reinforcement learning linear function approximation adaptivity constraints batch learning rare policy switch

2021-01-06abs ↗pdf ↗

Nonstationary Reinforcement Learning with Linear Function Approximationcs.LG

New RL algorithm tackles nonstationary MDPs with linear approximations and varying rewards.

problem Nonstationary reinforcement learning with evolving reward and state transition functions.

method Developed a new algorithm LSVI-UCB-Restart with periodic restart, and parameter-free Ada-LSVI-UCB-Restart for unknown variation budgets.

result First minimax dynamic regret lower bound for nonstationary linear MDPs and linear MDPs lower bound.

reinforcement learning nonstationary linear function approximation dynamic regret optimistic algorithms

2020-10-08abs ↗pdf ↗

Provably Efficient Model-Free Constrained RL with Linear Function Approximationcs.LG

New RL algorithm achieves sublinear regret and constraint violation without simulators.

problem Maximizing reward under utility constraints in large-scale systems.

method Model-free, simulator-free algorithm using LSVI-UCB with primal-dual optimization and soft-max policy.

result Achieves

ilde{\mathcal{O}}(\sqrt{d^3H^3T})

regret and

ilde{\mathcal{O}}(\sqrt{d^3H^3T})

constraint violation bounds.

reinforcement learning constrained optimization model-free linear function approximation sublinear regret

2022-06-23abs ↗pdf ↗

Logarithmic Regret for Reinforcement Learning with Linear Function Approximationcs.LG

Logarithmic regret achieved in RL with linear function approximation.

problem Achieving logarithmic regret in reinforcement learning with linear function approximation.

method LSVI-UCB for linear MDP assumption, UCRL-VTR for linear mixture MDP assumption.

result Logarithmic regret bounds established for RL with linear function approximation.

reinforcement learning logarithmic regret linear function approximation linear MDP UCRL-VTR

2020-11-23abs ↗pdf ↗

A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Designcs.LG

A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.

problem Optimizing reserve prices in multi-phase second-price auctions with noisy and potentially untruthful bidders.

method Combines RL techniques with buffer periods, a novel algorithm, and LSVI-UCB extension.

result Achieves optimal revenue regret under known and unknown noise conditions.

Reinforcement Learning Second Price Auctions Multi-Phase Auctions Revenue Optimization Markov Decision Process

2022-10-19abs ↗pdf ↗

Reinforcement Learning with Action-Triggered Observationscs.LG

New framework for reinforcement learning with sporadic state observations.

problem Partial observability in reinforcement learning.

method Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs).

result Optimistic algorithm achieving regret bound for episodic learning.

Reinforcement Learning Partial Observability Sporadic Observations Markov Decision Processes Optimistic Algorithms

2025-10-02abs ↗pdf ↗

Demystifying Linear MDPs and Novel Dynamics Aggregation Frameworkstat.ML

We prove a lower bound for feature dimension in linear MDPs and propose a novel dynamics aggregation framework.

problem The limitation of feature dimension in linear MDPs and the need for efficient hierarchical reinforcement learning.

method We propose a novel dynamics aggregation framework based on structural dynamics and design a provably efficient hierarchical reinforcement learning algorithm.

result Our algorithm achieves a regret of

ilde{O} ( d_ψ^{3/2} H^{3/2}\sqrt{ N T} )

and meets the condition

d_ψ^3 N \ll d^{3}

in most real-world environments.

linear MDPs feature dimension dynamics aggregation hierarchical reinforcement learning provably efficient

2024-10-31abs ↗pdf ↗

Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Boundscs.LG

New algorithm tackles heavy-tailed rewards in RL with instance-dependent regret bounds.

problem Efficient algorithms for RL with heavy-tailed rewards in large state-action spaces.

method Design of \textsc{Heavy-OFUL} for heavy-tailed linear bandits and \textsc{Heavy-LSVI-UCB} for RL with linear function approximation.

result First instance-dependent regret bounds for heavy-tailed rewards in RL with linear function approximation.

Reinforcement Learning Heavy-tailed Rewards Function Approximation Minimax Optimal Instance-Dependent Regret

2023-06-12abs ↗pdf ↗