Reinforcement Learning control with known dynamic equation

I know there is model-based reinforcement learning. But all the approaches assume an MDP.

If I want to do a feedback control of a system (i. e. control an inverted pendulum) it's quite easy to find the nonlinear differential equation. Can I somehow feed this knowledge into RL-algorithms or are there ways to transform a dynamic system to an MDP?

• Can you please define MDP for better understanding of the question – Peter Jun 28 '19 at 21:43

If you are starting with differential equations, then typically you would convert them into some non-differential form in order to apply them. If you can do this fully analytically - e.g. change something in the form $$a\frac{d^2x}{dt} + b\frac{dx}{dt} +c = 0$$ into some $$x = \alpha e^{\beta t}\text{sin}(\gamma t)$$ - then this would make the most accurate predictions and simulations. Otherwise you can use some method of approximation in order to resolve the differential equation into something that predicts next state from current state.