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The use of unmanned aerial vehicles (UAVs) for intelligence, surveillance, and reconnaissance (ISR) is one of the most well-known applications of the technology. UAVs performing ISR can carry out missions that would be too dangerous or otherwise too complicated for manned systems to complete. Today, explicit multi-agent information theoretic optimizations present challenges, requiring approximations and computational shortcuts to determine the expected information gain of an action. The approximation enables distributed optimization at the cost of efficiency in terms of under-utilization of certain sensors during tracking. While some techniques introduce multi-step planning to address this issue, these come at the cost of an additional computational burden. By using information theoretic metrics as reward functions in a Multi-Agent Reinforcement Learning (MARL) framework, we train RL agents to select actions which maximize expected information gain without having to estimate this quantity at runtime. This approach has potential to out-perform existing techniques, which rely on truncated estimates of expected information due to computational limitations. Our current prototype focuses on developing a cooperative learning strategy by modeling UAVs tracking down adversarial ground targets in an area of interest. Missions are expected to be highly dynamic where suboptimal conditions are likely to occur. To replicate this in our research, RL agents exist in a partial-knowledge environment where they learn to leverage various sensors and information available to complete the mission together. Our next step is to add a policy to optimize the number of UAVs needed to scan an area of interest while still tracking targets.
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