Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents

Understand

Stochastic multi-agent multi-armed bandits typically assume that the rewards from each arm follow a fixed distribution, regardless of which agent pulls the arm.

• However, in many real-world settings, rewards can depend on the sensitivity of each agent to their environment.
• In medical screening, disease detection rates can vary by test type; in preference matching, rewards can depend on user preferences; and in environmental sensing, observation quality can vary across sensors.
• Since past work does not specify how to allocate agents of heterogeneous but known sensitivity of these types in a stochastic bandit setting, we introduce a UCB-style algorithm, Min-Width, which aggregates information from diverse agents.