Simplified Risk-Aware POMDP Planning
With the recent advent of risk awareness, decision-making algorithms’ complexity increases, posing a severe difficulty to solve such formulations of the problem online. Our approach is centered on the distribution of the return in the challenging continuous domain under partial observability. In this research proejct we introduce and investigate a simplification framework to ease the computational burden while providing guarantees on the simplification impact. On top of this framework, we present novel stochastic bounds on the return that apply to any reward function. Further, we consider simplification’s impact on decision making with risk averse objectives, which, to the best of our knowledge, has not been investigated thus far.