Autonomous reinforcement learning method of receiver scan schedule control

Abstract

A method of detecting electromagnetic signal sources of interest includes applying reinforcement learning to automatically and continuously update a receiver scan schedule wherein an agent is reinforced according to comparisons between expected and actual degrees of success after each schedule update, actual degrees of success being estimated by applying to signal data a plurality of value scales applicable to a plurality of reward classes. An exponential scale can be applied across the plurality of reward classes. A companion system can provide data analysis to the agent. The agent can include an actor module that determines schedule updates and a critic module that determines the degrees of scanning success and awards the reinforcements. Embodiments implement a plurality of agents according to asynchronous multiple-worker actor/critic reinforcement learning. The method can be initially applied to training data comprising synthetic and/or previously measured signal data for which the signal sources are fully characterized.