> Because non-greedy online planning can be slow, offline planning can be used to generate a policy that maps any state to the best action to take. The resultin

> Because non-greedy online planning can be slow, offline planning can be used to generate a policy that maps any state to the best action to take. The resulting policy can be run very fast at execution time; no matter what state the agent ends up in after executing an action, the next action to execute is available as quickly as π can be queried. The article claims this is insufficient for AGI, but to me it sounds a lot like Kahneman's distinction between fast and slow thinking. Slow thinking is the offline planning, the trained network does the fast thinking. If humans work that way, then a human-level AGI probably can too.