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.
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.