Adaptive guidance and integrated navigation with reinforcement meta-learning

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 p...

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Vydáno v:	Acta astronautica Ročník 169; s. 180 - 190
Hlavní autoři:	Gaudet, Brian, Linares, Richard, Furfaro, Roberto
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elmsford Elsevier Ltd 01.04.2020 Elsevier BV
Témata:	Adaptive systems Asteroids Doppler radar Engine failure Guidance Guidance (motion) Landing guidance Learning Lidar Mars Mars environment Mars landing Meta learning Navigation Radio altimeters Reinforcement learning Guidance Reinforcement learning Landing guidance Meta learning
ISSN:	0094-5765, 1879-2030
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Abstract	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. •Adaptive guidance using meta-reinforcement learning.•Learns to handle engine failures, variations in mass and environmental forces.•Approach learns closed-loop controller.
AbstractList	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. 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. •Adaptive guidance using meta-reinforcement learning.•Learns to handle engine failures, variations in mass and environmental forces.•Approach learns closed-loop controller.
Author	Linares, Richard Furfaro, Roberto Gaudet, Brian
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Cites_doi	10.2514/1.G003341 10.1016/j.asr.2018.09.016 10.2514/1.G000921
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SubjectTerms	Adaptive systems Asteroids Doppler radar Engine failure Guidance Guidance (motion) Landing guidance Learning Lidar Mars Mars environment Mars landing Meta learning Navigation Radio altimeters Reinforcement learning
Title	Adaptive guidance and integrated navigation with reinforcement meta-learning
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