Learning Robot Swarm Tactics over Complex Adversarial Environments

TL;DR

This paper introduces a neural network-based framework for learning tactical swarm robot policies in complex, adversarial environments, enabling efficient mission execution with up to 60 robots.

Contribution

It presents a systematic method combining map abstraction, encoding, and neuroevolution to learn complete swarm tactics, advancing beyond primitive-level learning.

Findings

Successful mission completion with up to 60 robots

Close match in training and testing performance statistics

Framework demonstrates potential generalizability

Abstract

To accomplish complex swarm robotic missions in the real world, one needs to plan and execute a combination of single robot behaviors, group primitives such as task allocation, path planning, and formation control, and mission-specific objectives such as target search and group coverage. Most such missions are designed manually by teams of robotics experts. Recent work in automated approaches to learning swarm behavior has been limited to individual primitives with sparse work on learning complete missions. This paper presents a systematic approach to learn tactical mission-specific policies that compose primitives in a swarm to accomplish the mission efficiently using neural networks with special input and output encoding. To learn swarm tactics in an adversarial environment, we employ a combination of 1) map-to-graph abstraction, 2) input/output encoding via Pareto filtering of points of interest and clustering of robots, and 3) learning via neuroevolution and policy gradient approaches. We illustrate this combination as critical to providing tractable learning, especially given the computational cost of simulating swarm missions of this scale and complexity. Successful mission completion outcomes are demonstrated with up to 60 robots. In addition, a close match in the performance statistics in training and testing scenarios shows the potential generalizability of the proposed framework.