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Could reinforcement learning work in the following context : Given that the initial state space is very large (10^6) and the actions would only effect a subspace of the state could we randomly select a sample(the subspace we want to adjust) from the state? And thus given the observation(sample) take an action for this sample ? And afterwards update the state in a global according to the actions for that sample. And then in the next step feed it a new random sample from this new state?

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