Gary Marcus

It’s question of emphasis, in part, but if I were running your ship I would spend more time exploring principled ways of building hybrids, and more kinds of of hybrids, and more time on on open-ended problems.

Other than Neural Turing Machines, AlphaGo, GraphNets, GQN, SPIRAL, etc? I'm sure you'd run things differently, but this is a far cry from the DRL centric narrative of the Wired article.

Oh, so now AlphaGo is a hybrid? :) but yes I like a lot of that work and have advocated for some of it over time. I totally agree that DRL is not the only emphasis at DM; it’s just the largest (from what I can tell) and my least favorite and most visible, wrapped in one.

It's what you'd consider a hybrid. To RL folks the jump from DQN to MCTS doesn't change fields, so "hybrid" sounds weird.

Its not weird, it’s what (in conjunction) with RL makes it work. You have drunk the KoolAid if you ignore this.

MCTS is literally in RL 101 textbooks. The tree structure of MDPs is inherent in their temporal structure and core to RL. I'm sure it reminds you of Chomsky, parse trees, grammars, and that's fine, but not invoking them isn't "drinking the KoolAid", it's understanding the field.

Assimiling DQN and MCS + DRL as if they are same, without talking about why different design choices were made is misleading.

That's never happened. They are different algorithms in the same field (Seriously, look at RL textbooks). As my previous quote showed, they were quite up front with the different design choices.