Planning in Hierarchical Reinforcement Learning - Guarantees for Using Local Policies

ALT, pp. 906-934, 2020.

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Abstract:

We consider a settings of hierarchical reinforcement learning, in which the reward is a sum of components. For each component we are given a policy that maximizes it and our goal is to assemble a policy from the individual policies that maximizes the sum of the components. We provide theoretical guarantees for assembling such policies i...More

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