  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 difficult tasks 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 recurrent policy to navigate
using only Doppler radar altimeter returns, thus integrating guidance and
navigation.
