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Adversarial Attacks on Cooperative Multi-agent Bandits
Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game.
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Prior algorithms in both paradigms achieve the optimal group regret.
On-Demand Communication for Asynchronous Multi-Agent Bandits
We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times.
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
When the "per-load" reward follows a Gaussian distribution, we prove a sample complexity lower bound of learning the capacity from load-dependent rewards and also a regret lower bound of this new MP-MAB problem.
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications
The reward from a shareable arm is equal to the "per-load" reward multiplied by the minimum between the number of players pulling the arm and the arm's maximal shareable resources.
