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...

Evaluation of physical damage associated with action selection strategies in reinforcement learning

Reinforcement learning techniques enable robots to deal with their own dynamics and with unknown environments without using explicit models or preprogrammed behaviors. However, reinforcement learning relies on intrinsically risky exploration, which is often damaging for physical systems. In the case of the bipedal walking robot Leo, which is...