  Restless multi-armed bandits (RMAB) have demonstrated success in optimizing
resource allocation for large beneficiary populations in public health
settings. Unfortunately, RMAB models lack flexibility to adapt to evolving
public health policy priorities. Concurrently, Large Language Models (LLMs)
have emerged as adept automated planners across domains of robotic control and
navigation. In this paper, we propose a Decision Language Model (DLM) for
RMABs, enabling dynamic fine-tuning of RMAB policies in public health settings
using human-language commands. We propose using LLMs as automated planners to
(1) interpret human policy preference prompts, (2) propose reward functions as
code for a multi-agent RMAB environment, and (3) iterate on the generated
reward functions using feedback from grounded RMAB simulations. We illustrate
the application of DLM in collaboration with ARMMAN, an India-based non-profit
promoting preventative care for pregnant mothers, that currently relies on RMAB
policies to optimally allocate health worker calls to low-resource populations.
We conduct a technology demonstration in simulation using the Gemini Pro model,
showing DLM can dynamically shape policy outcomes using only human prompts as
input.
