Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
This addresses the communication efficiency problem for multi-agent systems in bandit learning, representing a foundational contribution rather than an incremental one.
The paper tackled the problem of balancing parallelism and communication overhead in multi-agent multi-armed bandits for regret minimization, presenting the first set of tradeoffs between communication rounds and regret.
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 present the first set of tradeoffs between the number of rounds of communication among the agents and the regret of the collaborative learning process.