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We use Reinforcement Meta-Learning to optimize an adaptive integratedguidance, navigation, and control system suitable for exoatmosphericinterception of a maneuvering target. The system maps observations consistingof strapdown seeker angles and rate gyro measurements directly to thrusteron-off commands. Using a high fidelity six degree-of-freedom simulator, wedemonstrate that the optimized policy can adapt to parasitic effects includingseeker angle measurement lag, thruster control lag, the parasitic attitude loopresulting from scale factor errors and Gaussian noise on angle and rotationalvelocity measurements, and a time varying center of mass caused by fuelconsumption and slosh. Importantly, the optimized policy gives good performanceover a wide range of challenging target maneuvers. Unlike previous work thatenhances range observability by inducing line of sight oscillations, our systemis optimized to use only measurements available from the seeker and rate gyros.Through extensive Monte Carlo simulation of randomized exoatmosphericinterception scenarios, we demonstrate that the optimized policy givesperformance close to that of augmented proportional navigation with perfectknowledge of the full engagement state. The optimized system is computationallyefficient and requires minimal memory, and should be compatible with today'sflight processors.

Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop