Hierarchical Policy Learning Is Sensitive To Goal Space Design

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Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding improved sample efficiency, the ability to deal with long time horizons and transferability of sub-policies to tasks outside the training distribution.