# The Emergence of Complex Bodyguard Behavior Through Multi-Agent Reinforcement Learning

## TL;DR

This paper demonstrates how multi-agent reinforcement learning, specifically MADDPG, can be used to develop complex, collaborative robot bodyguard behaviors in crowded environments, ensuring protection of a VIP.

## Contribution

It adapts MADDPG algorithms to enable multi-robot collaboration for physical protection in complex social scenarios.

## Key findings

- MADDPG successfully learns protective behaviors
- Robots coordinate effectively in crowded spaces
- Enhanced understanding of multi-agent interactions in security tasks

## Abstract

In this paper we are considering a scenario where a team of robot bodyguards are providing physical protection to a VIP in a crowded public space. We show that the problem involves a complex mesh of interactions between the VIP and the robots, between the robots themselves and the robots and the bystanders respectively. We show how recently proposed multi-agent policy gradient reinforcement learning algorithms such as MADDPG can be successfully adapted to learn collaborative robot behaviors that provide protection to the VIP.