I'm surprised Pearl doesn't mention reinforcement learning once in this essay. This domain is where Pearl-style causal reasoning frameworks most closely meets d

I'm surprised Pearl doesn't mention reinforcement learning once in this essay. This domain is where Pearl-style causal reasoning frameworks most closely meets data-driven statistical machine learning methods. Methods such have Dyna (link below) can be seen as reinforcement learning with some degree of "reasoning" about future consequences of actions. I suspect that there is more work to be done in embedding causal methods into planning, though it's pretty clear that a lot of progress is being made in domains such as Atari. Dyna: https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node29.html https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelb...