RL is useful for action selection and planning. Actually determining the mechanics of the game can be achieved with explicit instruction and definition of an ac

RL is useful for action selection and planning. Actually determining the mechanics of the game can be achieved with explicit instruction and definition of an action set. I suppose whether you find this result intriguing or not depends on if you’re looking to build result-building planning agents over an indeterminate (and sizable!) time horizon, in which case this is a SOTA improvement and moderately cool, or if you’re looking for a god in the machine, which this is not.