@proceedings {1556010,
	title = {Beyond {\textquotedblleft}To Act or Not to Act{\textquotedblright}: Fast Lagrangian Approaches to General Multi-Action Restless Bandits},
	journal = {IJCAI 2021 Workshop on AI for Social Good},
	year = {2021},
	abstract = {We present a new algorithm and theoretical results for solving Multi-action Multi-armed Restless Bandits, an important but insufficiently studied generalization of traditional Multi-armed Restless Bandits (MARBs). Multi-action MARBs are capable of handling critical problem complexities often present in AI4SG domains like anti-poaching and healthcare, but that traditional MARBs fail to capture. Limited previous work on Multi-action MARBs has only been specialized to sub-problems. Here we derive BLam, an algorithm for general Multi-action MARBs using Lagrangian relaxation techniques and convexity to quickly converge to good policies via bound optimization. We also provide experimental results comparing BLam to baselines on a simulated distributions motivated by a real-world community health intervention task, achieving up to five-times speedups over more general methods without sacrificing performance.},
	author = {Jackson A. Killian and Perrault, Andrew and Tambe, Milind}
}
