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Found 11 papers, 1 papers with code
Near-continuous time Reinforcement Learning for continuous state-action spaces
In addition, we consider a generic reward function and model the state dynamics according to a jump process with an arbitrary transition kernel on $\mathbb{R}^d$.
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Logistic Bandits have recently undergone careful scrutiny by virtue of their combined theoretical and practical relevance.
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity.
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
It was shown by Faury et al. (2020) that the learning-theoretic difficulties of Logistic Bandits can be embodied by a large (sometimes prohibitively) problem-dependent constant $\kappa$, characterizing the magnitude of the reward's non-linearity.
Real-Time Optimisation for Online Learning in Auctions
In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day.
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
We study the exploration-exploitation dilemma in the linear quadratic regulator (LQR) setting.
Improved Optimistic Algorithms for Logistic Bandits
For logistic bandits, the frequentist regret guarantees of existing algorithms are $\tilde{\mathcal{O}}(\kappa \sqrt{T})$, where $\kappa$ is a problem-dependent constant.
Thompson Sampling in Non-Episodic Restless Bandits
Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging.
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Thompson sampling (TS) is an effective approach to trade off exploration and exploration in reinforcement learning.
Thompson Sampling for Linear-Quadratic Control Problems
Despite the empirical and theoretical success in a wide range of problems from multi-armed bandit to linear bandit, we show that when studying the frequentist regret TS in control problems, we need to trade-off the frequency of sampling optimistic parameters and the frequency of switches in the control policy.
Linear Thompson Sampling Revisited
We derive an alternative proof for the regret of Thompson sampling (\ts) in the stochastic linear bandit setting.
