Basically given some predicted environment state, going forward for say 100 time steps, we need to find an optimal cost course of action. Although the environment state has been predicted, for the purposes of this task the agent can consider it deterministic. The agent has one variable of internal state and can take actions to increase or decrease this value based on interactions with the environment. We can then calculate the new cost over the given time horizon by simulating the actions chosen at each step, but this simulation is fundamentally sequential and wouldn't allow backpropagation of gradients.

>you can go with sampling approaches

What exactly do you mean by this? something like REINFORCE?

> I guess it is if you're using a MILP approach.

Not sure I follow here, but I'm not using a MILP (as in mixed integer linear program). At the moment I'm using a linear programming approximation and heuristics, which doesn't generalize well.

> some combination of MCTS with value function learning

I think this could work, however without looking into it I'm not sure that it would work at inference time in my resource-constrained setting