Risk-sensitive Distributional Reinforcement Learning for Flight Control
Peter Seres (Student TU Delft)
Cheng Liu (TU Delft - Control & Simulation)
E. van Kampen (TU Delft - Control & Simulation)
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Abstract
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.
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