  We use reinforcement meta learning to optimize a line of sight curvature
policy that increases the effectiveness of a guidance system against
maneuvering targets. The policy is implemented as a recurrent neural network
that maps navigation system outputs to a Euler 321 attitude representation. The
attitude representation is then used to construct a direction cosine matrix
that biases the observed line of sight vector. The line of sight rotation rate
derived from the biased line of sight is then mapped to a commanded
acceleration by the guidance system. By varying the bias as a function of
navigation system outputs, the policy enhances accuracy against highly
maneuvering targets. Importantly, our method does not require an estimate of
target acceleration. In our experiments, we demonstrate that when our method is
combined with proportional navigation, the system significantly outperforms
augmented proportional navigation with perfect knowledge of target
acceleration, achieving improved accuracy with less control effort against a
wide range of target maneuvers.
