Referring to deep learning as just being good for "perception" really strains credulity. Chess has historically been a prototypical exercise in reasoning, but since variables weren't involved in it's mastery, I guess it's perception now?https://twitter.com/GaryMarcus/status/1068897657530138629 …
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That is a very liberal definition of variable binding. That would encompass all planning procedures (e.g. s_{t+n} is bound to the state predicted in n steps). If wading into model-based RL is all you wanted, then every DRL researcher is already on board. Mission accomplished?
it is partly an implementational question; some functions can be computed in different ways, some w recourse to variables others not. too long for twitter, see chapter 2&3 of algebraic mind
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a challenge for you if you wanted to pursue an ant-symbol position would be to capture the power of MCTS using a neural net that didn’t just map onto code like this: https://jeffbradberry.com/posts/2015/09/intro-to-monte-carlo-tree-search/ …
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Perhaps part of the confusion is that Alpha-Go exploits two related things: 1) a perfect forward model and 2) MCTS on that model. 1) is an active area of research where you could compare symbolic vs NN. 2) is a planning procedure, which are fully embraced by DRL.
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