# Emergence of Distributed Coordination in the Kolkata Paise Restaurant Problem with Finite Information

## TL;DR

This paper investigates how distributed agents can efficiently coordinate resource allocation in large-scale systems without central control, using adaptive strategies and reinforcement learning to improve resource utilization.

## Contribution

It introduces novel adaptive strategies combining finite information and reinforcement learning to enhance resource utilization in decentralized settings.

## Key findings

- Finite information and reinforcement learning significantly improve resource utilization.
- Adaptive strategies outperform traditional methods in large-scale coordination.
- The approach reduces congestion and unutilized resources effectively.

## Abstract

In this paper, we study a large-scale distributed coordination problem and propose efficient adaptive strategies to solve the problem. The basic problem is to allocate finite number of resources to individual agents such that there is as little congestion as possible and the fraction of unutilized resources is reduced as far as possible. In the absence of a central planner and global information, agents can employ adaptive strategies that uses only finite knowledge about the competitors. In this paper, we show that a combination of finite information sets and reinforcement learning can increase the utilization rate of resources substantially.