Learning Multi-Level Hierarchies with Hindsight

4 Dec 2017Andrew LevyGeorge KonidarisRobert PlattKate Saenko

Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of policies in parallel so these simpler subproblems can be solved simultaneously... (read more)

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