Submitted by **EmbarrassedFuel** t3_10w5f9u
in **MachineLearning**

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**UnusualClimberBear**
t1_j7lvpz8 wrote

Looks like an optimal control problem rather than an RL one. RL is there for situations with no good model available. If stochasticity is present, but you still have a good model once the uncertainty is known, then Markov predictive control is a good way to go.

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**UnusualClimberBear**
t1_j7opc2r wrote

Also if your world is deterministic but you cannot build a good model of it, it may be that you are close to the situation of games such as Go, and Monte Carlo Tree search algorithms are an option to consider (variants of UCT with or without function approximation)

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**EmbarrassedFuel**
OP
t1_j7p40eo wrote

oh also the model needs to run at inference time in a relatively short period of time on cheap hardware :)

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**EmbarrassedFuel**
OP
t1_j7p3xc1 wrote

I haven't been able to find anything about optimal control with all of:

- non-linear dynamics/model
- non-linear constraints
- both discrete and continuously parameterized actions in the output space

but in general, discovery of papers/techniques in control theory seems to be much harder for some reason

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**UnusualClimberBear**
t1_j7pdue6 wrote

This is because the information is in the books.

(free online) http://www.cds.caltech.edu/~murray/amwiki/index.php/Main_Page

https://www.amazon.com/Modern-Control-Systems-12th-Edition/dp/0136024580

Yet nonlinear breaks everything there. The usual approach is to linearize at well-chosen positions and compute the control using the closest linearization.

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