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Found 6 papers, 1 papers with code
Queue Scheduling with Adversarial Bandit Learning
In this paper, we study scheduling of a queueing system with zero knowledge of instantaneous network conditions.
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
\texttt{Banker-OMD} leads to the first delayed scale-free adversarial MAB algorithm achieving $\widetilde{\mathcal O}(\sqrt{K}L(\sqrt T+\sqrt D))$ regret and the first delayed adversarial linear bandit algorithm achieving $\widetilde{\mathcal O}(\text{poly}(n)(\sqrt{T} + \sqrt{D}))$ regret.
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Training deep reinforcement learning (DRL) models usually requires high computation costs.
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
Specifically, we design an algorithm \texttt{HTINF}, when the heavy-tail parameters $\alpha$ and $\sigma$ are known to the agent, \texttt{HTINF} simultaneously achieves the optimal regret for both stochastic and adversarial environments, without knowing the actual environment type a-priori.
Scale-Free Adversarial Multi-Armed Bandit with Arbitrary Feedback Delays
We consider the Scale-Free Adversarial Multi-Armed Bandit (MAB) problem with unrestricted feedback delays.
Banker Online Mirror Descent
In particular, it leads to the first delayed adversarial linear bandit algorithm achieving $\tilde{O}(\text{poly}(n)(\sqrt{T} + \sqrt{D}))$ regret.
