# Distributed Task Allocation for Self-Interested Agents with Partially Unknown Rewards

arXiv: https://arxiv.org/abs/2311.00222 · HTML: https://mlsanity.com/paper/2311.00222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Choose Your Battles: Distributed Learning Over Multiple Tug of War Games](https://mlsanity.com/paper/2509.20147/) (2025)

## Abstract (arXiv)

This paper provides a novel solution to a task allocation problem, by which a group of agents decides on the assignment of a discrete set of tasks in a distributed manner. In this setting, heterogeneous agents have individual preferences and associated rewards for doing each task; however, these rewards are only known asymptotically. We start by formulating the assignment problem by means of a combinatorial partition game for known rewards, with no constraints on number of tasks per agent. We relax this into a weight game, which together with the former, are shown to contain the optimal task allocation in the corresponding set of Nash Equilibria (NE). We then propose a projected, best-response, ascending gradient dynamics (PBRAG) that converges to a NE in finite time. This forms the basis of a distributed online version that can deal with a converging sequence of rewards by means of an agreement sub-routine. We present simulations that support our results