Adaptive guidance and integrated navigation with reinforcement meta-learning

Brian Gaudet, Richard Linares, Roberto Furfaro

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)180-190
Number of pages11
JournalActa Astronautica
StatePublished - Apr 2020


  • Guidance
  • Landing guidance
  • Meta learning
  • Reinforcement learning

ASJC Scopus subject areas

  • Aerospace Engineering


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