Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits

Key points

• The asynchronous variant, in which players are active only at a fraction of timesteps, better reflects real-world decentralized networks, where devices are not permanently active.1
• Existing work on asynchronous MMAB has focused on homogeneous players, who share the same activation probability, a setting in which the optimal centralized allocation can be easily characterized.1
• Furthermore, when players are processed in decreasing order of their activation probabilities, the approximation guarantee improves to $\frac{1}{\sqrt{e}}$.1
• Overall, our results establish the greedy algorithm as an efficient approximation to the optimal allocation, providing a practical oracle baseline for the design of online algorithms for heterogeneous asynchronous multi-player bandits.1

Sources (1)

• [1]Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 03:07 PM

  Motivated by applications to cognitive radio networks, multi-player multi-armed bandits (MMAB) have attracted considerable attention in recent years.

  The asynchronous variant, in which players are active only at a fraction of timesteps, better reflects real-world decentralized networks, where devices are not permanently active.

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