Evolutionary Reinforcement Learning: A Hybrid Approach for Safety-informed Intelligent Fault-tolerant Flight Control

Abstract

Recent research in artificial intelligence potentially provides solutions to the challenging problem of fault-tolerant and robust flight control. The current work proposes a novel Safety-informed Evolutionary Reinforcement Learning (SERL) algorithm, which combines Deep Reinforcement Learning (DRL) and neuro-evolution to optimize a population of non-linear control policies. Using SERL, the work has trained agents to provide attitude tracking on a high-fidelity non-linear fixed-wing aircraft model. Compared to a state-of-the-art DRL solution, SERL achieves better tracking performance in nine out of ten cases, remaining robust against faults and changes in flight conditions, while providing smoother actions.