Risk-sensitive Distributional Reinforcement Learning for Flight Control

Recent aerospace systems increasingly demand model-free controller synthesis, and autonomous operations require adaptability to uncertainties in partially observable environments. This paper applies distributional reinforcement learning to synthesize risk-sensitive, robust model-free policies for aerospace control. We investigate the use of distributional soft actor-critic (DSAC) agents for flight control and compare their learning characteristics and tracking performance with the soft actor-critic (SAC) algorithm. The results show that (1) the addition of distributional critics significantly improves learning consistency, (2) risk-averse agents increase flight safety by avoiding uncertainties in the environment.