Risk-aware autonomous search and rescue with multiagent reinforcement learning

Autonomous navigation in dynamic high-consequence environments, such as search and rescue (SAR) missions, often relies on multiagent robotic systems that need to learn and adapt to changing conditions. Adversarial risks can introduce further challenges in such a setting where an autonomous agent may exhibit deviations in their learned actions from training to testing. Moreover, the uncertain environment itself may also evolve with additional obstacles that can emerge during testing compared to conditions when algorithmic training of autonomous agents was performed. In this paper, we first focus on mathematically formulating the autonomous SAR problem via a risk-aware multiagent reinforcement learning approach. Thereafter, we design and implement numerical experiments to evaluate our approach under diverse hazard scenarios with a centralized training and decentralized testing paradigm. Finally, we discuss our results and steps for further research.