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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.

168,742 papers · 148 categories

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3 results for “UCRL-VTR”

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processescs.LG

New RL algorithm for linear MDPs with nearly optimal regret.

problem Optimizing reinforcement learning for linear mixture Markov decision processes.

method Proposed a new Bernstein-type concentration inequality for self-normalized martingales and a computationally efficient algorithm UCRL-VTR+.

result UCRL-VTR+ achieves nearly minimax optimal regret of

ilde O(dH\sqrt{T})

reinforcement learning Markov decision processes linear function approximation minimax optimality Bernstein inequality

2020-12-15abs ↗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 ↗

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regretquant-ph

Quantum RL algorithm achieves logarithmic regret for exploration.

problem Designing efficient quantum RL algorithms for exploration.

method UCRL-style quantum algorithm with lazy updating and quantum estimation.

result Proves

\mathcal{O}(\mathrm{poly}(S, A, H, \log T))

worst-case regret.

quantum reinforcement learning exploration-exploitation regret minimization lazy updating quantum estimation

2023-02-21abs ↗pdf ↗