Inter-Level Cooperation in Hierarchical Reinforcement Learning. (arXiv:1912.02368v1 [cs.AI])

This article presents a novel algorithm for promoting cooperation between
internal actors in a goal-conditioned hierarchical reinforcement learning (HRL)
policy. Current techniques for HRL policy optimization treat the higher and
lower level policies as separate entities which are trained to maximize
different objective functions, rendering the HRL problem formulation more
similar to a general sum game than a single-agent task. Within this setting, we
hypothesize that improved cooperation between the internal agents of a
hierarchy can simplify the credit assignment problem from the perspective of
the high-level policies, thereby leading to significant improvements to
training in situations where intricate sets of action primitives must be
performed to yield improvements in performance. In order to promote cooperation
within this setting, we propose the inclusion of a connected gradient term to
the gradient computations of the higher level policies. Our method is
demonstrated to achieve superior results to existing techniques in a set of
difficult long time horizon tasks.

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