Reinforcement Learning for Angle-Only Intercept Guidance of Maneuvering Targets

Abstract

We present a novel guidance law that uses observations consisting solely of seeker line of sight angle measurements and their rate of change. The policy is optimized using reinforcement meta-learning and demonstrated in a simulated terminal phase of a mid-course exo-atmospheric interception. Importantly, the guidance law does not require range estimation, making it particularly suitable for passive seekers. The optimized policy maps stabilized seeker line of sight angles and their rate of change directly to commanded thrust for the missile's divert thrusters. The use of reinforcement meta-learning allows the optimized policy to adapt to target acceleration, and we demonstrate that the policy performs as well as augmented zero-effort miss guidance with perfect target acceleration knowledge. The optimized policy is computationally efficient and requires minimal memory, and should be compatible with today's flight processors.

Also in 2020 AIAA Scitech Guidance Navigation and Control Conference

References in corpus (6)

• Gated Feedback Recurrent Neural Networks
• Learning to reinforcement learn
• EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
• Meta Learning Shared Hierarchies
• Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Powered Descent and Landing
• Adaptive Guidance with Reinforcement Meta-Learning

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