I’m currently attempting to NOT do reinforcement learning research, and the points made in this article are why, in the negative. In naive RL, we model every p

I’m currently attempting to NOT do reinforcement learning research, and the points made in this article are why, in the negative. In naive RL, we model every process as having unknown dynamics and an unknown optimal policy, and furthermore the dynamics are assumed to be Markov decision processes. An MDP is, as far as I know, the least restrictive (hence most general) setting in which a dynamic process can be considered a “process”; any more general and the only “dynamics” we would be left with would be pure random noise. But for nearly all physical systems of interest, the dynamics can be described perfectly well as differential equations. We might not know the structure of the equations, but simply stating that the dynamics are in fact differential equations, and building a learning algorithm around that assumption, actually provides a significant amount of structure vice MDPs. So I am researching differential equation-based learning techniques, on the assumption that they should be much more data efficient. The arguments put forth here seem to indicate that I should stop, and just wait for computing to catch up to extremely high dimensional MDPs. Harrumph.