[xiphias2]

You're right with using MCTS, but ,,Their accomplishments on Atari lean a lot more on efficient computing than biological plausibility'' is a strange thing to write.

Efficient computing is of course needed for AGI, I think that was never a question. The question is what algorithms to use on the computers, and also what computing architectures should be created for those algorithms.

Those Atari simulations were the first ones putting together reinforcement learning and deep convolutional networks AFAIK, and yes, tree search was needed (which human brains are consciously doing, but extremely bad at compared to computers).

Just looking at what works is not enough. There was a strong reason why DeepMind didn't start with modelling language or logical reasoning, like many other people, and the background was based in biology (animal behaviour).

[jimfleming]

My point is that DQN is pretty far removed from the biological equivalent. It's impressive and useful but the main reason it succeeded was not because of some deep insight from neuroscience but because it scaled well (or at least better than alternatives at the time).

EDIT: Richard Sutton (largely credited as the grandfather of RL) has written about this recently: http://incompleteideas.net/IncIdeas/BitterLesson.html