The Impact of Dredging Access Channel on Harbor Oscillation

Dong, Yujin; Ma, Xiaozhou; Zheng, Zhenjun; Dong, Guohai

doi:10.1007/978-981-15-0291-0_173

Cited by 1 publication

(1 citation statement)

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“…Most of the literature focused on the analysis of the Bayesian regret of TS for general settings such as linear bandits or reinforcement learning (see e.g., [Osband and Van Roy, 2015]). More recently, [Russo and Van Roy, 2016, Dong and Van Roy, 2018, Dong et al, 2019 provided an information-theoretic analysis of TS, where the key tool in their approach is the information ratio which quantifies the trade-off between exploration and exploitation. Additionally, [Gopalan and Mannor, 2015] provides regret guarantees for TS in the finite and infinite MDP setting.…”

Section: Related Workmentioning

confidence: 99%

Safe Linear Thompson Sampling with Side Information

Moradipari¹,

Amani²,

Alizadeh³

et al. 2019

Preprint

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The design and performance analysis of bandit algorithms in the presence of stage-wise safety or reliability constraints has recently garnered significant interest. In this work, we consider the linear stochastic bandit problem under additional linear safety constraints that need to be satisfied at each round. We provide a new safe algorithm based on linear Thompson Sampling (TS) for this problem and show a frequentist regret of order O(d 3/2 log 1/2 d • T 1/2 log 3/2 T ), which remarkably matches the results provided by [Abeille et al., 2017] for the standard linear TS algorithm in the absence of safety constraints. We compare the performance of our algorithm with a UCB-based safe algorithm and highlight how the inherently randomized nature of TS leads to a superior performance in expanding the set of safe actions the algorithm has access to at each round.

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Section: Related Workmentioning

confidence: 99%