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Found 4 papers, 2 papers with code
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
This paper proposes NeurWIN, a neural Whittle index network that seeks to learn the Whittle indices for any restless bandits by leveraging mathematical properties of the Whittle indices.
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
We consider the problem of learning the optimal threshold policy for control problems.
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making
This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making.
Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback
In addition to the exploration-exploitation dilemma in the traditional multi-armed bandit problem, we show that the consideration of multiple stages introduces a third component, education, where an agent needs to choose its actions to facilitate the learning of agents in the next stage.
