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Found 6 papers, 1 papers with code
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
Furthermore, we propose a variant of TD(0) with linear approximators that incorporates a centering sequence, and establish that it exhibits an exponential rate of convergence in expectation.
Finite-Time Analysis of Kernelised Contextual Bandits
For contextual bandits, the related algorithm GP-UCB turns out to be a special case of our algorithm, and our finite-time analysis improves the regret bound of GP-UCB for the agnostic case, both in the terms of the kernel-dependent quantity and the RKHS norm of the reward function.
Thompson Sampling for 1-Dimensional Exponential Family Bandits
Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the Bernoulli case.
Fast gradient descent for drifting least squares regression, with application to bandits
In the case when strong convexity in the regression problem is guaranteed, we provide bounds on the error both in expectation and high probability (the latter is often needed to provide theoretical guarantees for higher level algorithms), despite the drifting least squares solution.
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling
We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm.
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis
The question of the optimality of Thompson Sampling for solving the stochastic multi-armed bandit problem had been open since 1933.
