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jimmymvp t1_j7oybk9 wrote

Ok, first off, I'm very curious what's the actual problem that you're solving. Can you describe it a bit more in detail or give a link?

If you have a perfect model that's cheap to compute, you can go with sampling approaches, I don't know how your constraints look like though. If your state/action space is too big, you might want to reduce it somehow by learning an embedding.

Is the model differentiable? I guess it is if you're using a MILP approach.

I guess some combination of MCTS with value function learning is plausible if your search space is big, such as it's done with alpha zero etc. I find the hybrid aspect of it very interesting though. It sounds like if you want to do amortized search, you need to combine MCTS and search in continuous space (sampling). Should be simple enough with a perfect model. Probably some ideas from mu zero would come in handy.

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EmbarrassedFuel OP t1_j7p519o wrote

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

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