Deep Electronic Warfare Learning (DEWL)

Near-peer adversaries with advanced Integrated Air Defense Systems (IADS) have created anti-access area denial (A2AD) zones for US Air Forces. A2AD scenarios continue to push conventional strategic platforms to further stand-off ranges, decreasing their mission effectiveness. Toyon proposes the research and development of Artificial Intelligence (AI) to optimize swarming of Electronic Warfare (EW)-enabled munitions.  The hypothesized swarm will employ cooperative maneuvers and non-kinetic effects to counter IADS in A2AD zones. To achieve this, the AI must optimize both the joint maneuvers of the swarm and the parameters of onboard EW payloads. Our approach combines Deep Reinforcement Learning (DRL) with multi-agent training.  The combination allows each agent to learn complicated tasks (through deep reinforcement learning) and to master them (through training against an adversarial AI) from the experience gained while competing in a high-fidelity simulated battlefield. In Phase I, a pulse-level EW simulator was developed and integrated with Toyon’s Modular Autonomy Incubator (MAUI) framework to train a proof-of-concept AI to automatically jam an enemy radar (1-v-1). In Phase II, we will develop a preliminary digital twin of a real EW payload being developed by Toyon (a parallel effort) and expand our focus to harder scenarios combining the digital twin, enhanced EW simulator, and realistic threat models.  Agent models will be designed for both red-side and blue-side AI agents and large-scale multi-agent training will be performed on a high-performance computing cluster to optimize their underlying policy networks. Our objective is the development and demonstration of novel, robust, and deployable autonomy to support Phase III production of Swarming Electronic Attack Munitions (SEAM).