Adaptive Guidance with Reinforcement Meta-Learning

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 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.

arXiv admin note: substantial text overlap with arXiv:1810.08719

References in corpus (2)

• Gated Feedback Recurrent Neural Networks
• Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Powered Descent and Landing

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• Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control
• Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop