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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MCTS is a search procedure, whereas you seem to be mistaking it for tree-structured representations. That they both have "tree" in the name seems besides the point.
the procedure operates over variables, with lots of variable binding taking place. that’s what matters. (and as it happens at least in the original Nature paper parts of the tree itself were encoded, if i recall correctly.)
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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?
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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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