Adaptive guidance and integrated navigation with reinforcement meta-learning
Name
1904.09865.pdf
Description
Submitted version
Size
1.35 MB
Format
Unknown
Checksum (MD5)
0c206694dd331f92b2fcbcd506bbc5d6
Author(s)
Gaudet, Brian
Linares, Richard
Furfaro, Roberto
Date Issued
April 2020
Journal
Acta Astronautica
Publisher
Elsevier BV
Version
Original manuscript
Abstract
© 2020 IAA 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 in 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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.actaastro.2020.01.007
