Let's imagine we have a blackbox function
f(X) -> y which we don't know.
X is a vector of 10 continuous variables, which we want to optimize to reduce
y output (a continuous reward with unknown min and max). Also,
f(X) maps a different optimum
y depending on some other continuous parameters
Z, which are observable. Each day we can test a new
X combination, and at the end of it we know the result.
I Could find out three posible solutions to this problem:
- The contextual multi-armed bandit problem solvable with Bayesian Optimization
- Variants of continuous reinforcement-learning/Q-learning
- Actor-critic methods
In the case when some of Z values are influenced by X (let's say only 2 non-related binary values), and other are external (3 real valued), which RL algorithm would fit better this problem, and how would I add these independent Z values to take them into account?
If all Z values are extenral, is multiarmed bandits the best approach?