The Assistive Multi-Armed Bandit
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for human-robot interaction.
Code (1)
Tasks
Multi-Armed BanditsSimilar Papers 제목 키워드 기반
Batched Multi-armed Bandits Problem
In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for the two-armed stochastic bandits has be…
Multi-Armed BanditsBuilding Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens
In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits,…
Active LearningBinary ClassificationGeneral ClassificationMulti-Armed BanditsOSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms …
Multi-Armed BanditsCommunication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we …
Multi-agent Reinforcement LearningMulti-Armed Banditsreinforcement-learningReinforcement Learning (RL)Indexability of Finite State Restless Multi-Armed Bandit and Rollout Policy
We consider finite state restless multi-armed bandit problem. The decision maker can act on M bandits out of N bandits in each time step. The play of arm (active arm) yields state dependent rewards based on action and wh…