Many consider Deep Reinforcement Learning (DeepRL) systems, such as AlhpaZero or MuZero, as the most-likely early version of AGI systems.

However, a common critique to RL systems is that they are applicable only to well-defined problems, such as games.

Hence, a natural question that follow from this is: what "messy" problems could be tackled using DeepRL systems?

Here I intend messy as a pseudo-definition of problems that do not have clearly identifiable inputs and outputs. Examples that come to my mind are generally problems from Economics, Management and Policy Making.