  This paper proposes a novel adaptive guidance system developed using
reinforcement meta-learning with a recurrent policy and value function
approximator. The use of recurrent network layers allows the deployed policy to
adapt real time to environmental forces acting on the agent. We compare the
performance of the DR/DV guidance law, an RL agent with a non-recurrent policy,
and an RL agent with a recurrent policy in four challenging environments with
unknown but highly variable dynamics. These tasks include a safe Mars landing
with random engine failure and a landing on an asteroid with unknown
environmental dynamics. We also demonstrate the ability of a RL meta-learning
optimized policy to implement a guidance law using observations consisting of
only Doppler radar altimeter readings in a Mars landing environment, and LIDAR
altimeter readings in an asteroid landing environment, thus integrating
guidance and navigation.
