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We apply a reinforcement meta-learning framework to optimize an integratedand adaptive guidance and flight control system for an air-to-air missile. Thesystem is implemented as a policy that maps navigation system outputs directlyto commanded rates of change for the missile's control surface deflections. Thesystem induces intercept trajectories against a maneuvering target that satisfycontrol constraints on fin deflection angles, and path constraints on lookangle and load. We test the optimized system in a six degrees-of-freedomsimulator that includes a non-linear radome model and a strapdown seeker model,and demonstrate that the system adapts to both a large flight envelope andoff-nominal flight conditions including perturbation of aerodynamic coefficientparameters and center of pressure locations, and flexible body dynamics.Moreover, we find that the system is robust to the parasitic attitude loopinduced by radome refraction and imperfect seeker stabilization. We compare oursystem's performance to a longitudinal model of proportional navigation coupledwith a three loop autopilot, and find that our system outperforms thisbenchmark by a large margin. Additional experiments investigate the impact ofremoving the recurrent layer from the policy and value function networks,performance with an infrared seeker, and flexible body dynamics.

Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning