  Search and Rescue (SAR) missions in remote environments often employ
autonomous multi-robot systems that learn, plan, and execute a combination of
local single-robot control actions, group primitives, and global
mission-oriented coordination and collaboration. Often, SAR coordination
strategies are manually designed by human experts who can remotely control the
multi-robot system and enable semi-autonomous operations. However, in remote
environments where connectivity is limited and human intervention is often not
possible, decentralized collaboration strategies are needed for
fully-autonomous operations. Nevertheless, decentralized coordination may be
ineffective in adversarial environments due to sensor noise, actuation faults,
or manipulation of inter-agent communication data. In this paper, we propose an
algorithmic approach based on adversarial multi-agent reinforcement learning
(MARL) that allows robots to efficiently coordinate their strategies in the
presence of adversarial inter-agent communications. In our setup, the objective
of the multi-robot team is to discover targets strategically in an
obstacle-strewn geographical area by minimizing the average time needed to find
the targets. It is assumed that the robots have no prior knowledge of the
target locations, and they can interact with only a subset of neighboring
robots at any time. Based on the centralized training with decentralized
execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning
framework to learn dynamic team-coordination modalities and discover emergent
team behavior under complex cooperative-competitive scenarios. The
effectiveness of our approach is demonstrated on a collection of prototype
grid-world environments with different specifications of benign and adversarial
agents, target locations, and agent rewards.

Robotics;Machine Learning;Multiagent Systems;Systems and Control;Systems and Control;Optimization and Control