A Reinforcement Learning machine would have to be re-trained for changes such as that, but not from scratch. Depending on the size of the change, it would take

A Reinforcement Learning machine would have to be re-trained for changes such as that, but not from scratch. Depending on the size of the change, it would take much less time to adapt to such a change. Several notes: -The article notes that a lot of the hard parts of human video-game playing have been done 'for' the AI by hardcoding. It doesn't actually have to look at the screen to parse information--it has direct access to game variables that player would have to access through menus. More relevant to your question, a lot of the strategic decisions like item purchasing, ward placement, and probably character placement were just picked by the programmers or set to be ignored. The whole thing is less impressive than the headline sounds. I think it was just learning--where to run on the map and when to attack things? -RL is based on deep learning, and there are fundamental issues with deep learning's ability to adapt to genuinely new scenarios. None of these system can presently adapt to something genuinely unprecendeted in a time frame you would consider 'safe'. They need at least several opportunities to observe how the world works after the changes and the consequences of their actions. To try to make this concrete--they can't reason about what implications a flood/power outage/landslide has for their traffic management. They can only learn from trial and error <-(the important part) how traffic behaves during a disaster.