  We present a novel guidance law that uses observations consisting solely of
seeker line of sight angle measurements and their rate of change. The policy is
optimized using reinforcement meta-learning and demonstrated in a simulated
terminal phase of a mid-course exo-atmospheric interception. Importantly, the
guidance law does not require range estimation, making it particularly suitable
for passive seekers. The optimized policy maps stabilized seeker line of sight
angles and their rate of change directly to commanded thrust for the missile's
divert thrusters. The use of reinforcement meta-learning allows the optimized
policy to adapt to target acceleration, and we demonstrate that the policy
performs as well as augmented zero-effort miss guidance with perfect target
acceleration knowledge. The optimized policy is computationally efficient and
requires minimal memory, and should be compatible with today's flight
processors.
