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Found 5 papers, 1 papers with code
Price of Safety in Linear Best Arm Identification
We introduce the safe best-arm identification framework with linear feedback, where the agent is subject to some stage-wise safety constraint that linearly depends on an unknown parameter vector.
UCB Momentum Q-learning: Correcting the bias without forgetting
We propose UCBMQ, Upper Confidence Bound Momentum Q-learning, a new algorithm for reinforcement learning in tabular and possibly stage-dependent, episodic Markov decision process.
Stochastic Bandits with Vector Losses: Minimizing $\ell^\infty$-Norm of Relative Losses
We study two goals: (a) finding the arm with the minimum $\ell^\infty$-norm of relative losses with a given confidence level (which refers to fixed-confidence best-arm identification); (b) minimizing the $\ell^\infty$-norm of cumulative relative losses (which refers to regret minimization).
Gamification of Pure Exploration for Linear Bandits
We investigate an active pure-exploration setting, that includes best-arm identification, in the context of linear stochastic bandits.
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS).
