A DDPG agent learns the gains of a fixed three-loop missile autopilot; with a shaped reference command in the reward, it matches or outperforms classical gain scheduling in simulation.
Connections Between Adaptive Control and Optimization in Machine Learning
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abstract
This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concepts in stability, performance, and learning, common to both fields are then discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis are provided. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.
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cs.AI 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design
A DDPG agent learns the gains of a fixed three-loop missile autopilot; with a shaped reference command in the reward, it matches or outperforms classical gain scheduling in simulation.