Deep reinforcement learning for six degree-of-freedom planetary landing

Brian Gaudet, Richard Linares, Roberto Furfaro

Research output: Contribution to journalArticlepeer-review

116 Scopus citations


This work develops a deep reinforcement learning based approach for Six Degree-of-Freedom (DOF) planetary powered descent and landing. Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target specific surface locations and achieve pinpoint accuracy (landing error ellipse <5 m radius). This requires both a navigation system capable of estimating the lander's state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander's estimated state directly to a commanded thrust for each engine, resulting in accurate and almost fuel-optimal trajectories over a realistic deployment ellipse. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system's performance in a 6-DOF simulation environment and demonstrate robustness to noise and system parameter uncertainty.

Original languageEnglish (US)
Pages (from-to)1723-1741
Number of pages19
JournalAdvances in Space Research
Issue number7
StatePublished - Apr 1 2020


  • Artificial intelligence
  • Autonomous maneuvers
  • Integrated guidance and control
  • Mars landing
  • Reinforcement learning

ASJC Scopus subject areas

  • Aerospace Engineering
  • Astronomy and Astrophysics
  • Geophysics
  • Atmospheric Science
  • Space and Planetary Science
  • General Earth and Planetary Sciences


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