Hierarchical Reinforcement Learning with Adversarially Guided Subgoals. (arXiv:2201.09635v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2201.09635

Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks
by performing decision-making and control at successively higher levels of
temporal abstraction. However, off-policy HRL often suffers from the problem of
non-stationary high-level policy since the low-level policy is constantly
changing. In this paper, we propose a novel HRL approach for mitigating the
non-stationarity by adversarially enforcing the high-level policy to generate
subgoals compatible with the current instantiation of the low-level policy. In
practice, the adversarial learning is implemented by training …

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