  The high-dimensional or sparse reward task of a reinforcement learning (RL)
environment requires a superior potential controller such as hierarchical
reinforcement learning (HRL) rather than an atomic RL because it absorbs the
complexity of commands to achieve the purpose of the task in its hierarchical
structure. One of the HRL issues is how to train each level policy with the
optimal data collection from its experience. That is to say, how to synchronize
adjacent level policies optimally. Our research finds that a HRL model through
the off-policy correction technique of HRL, which trains a higher-level policy
with the goal of reflecting a lower-level policy which is newly trained using
the off-policy method, takes the critical role of synchronizing both level
policies at all times while they are being trained. We propose a novel HRL
model supporting the optimal level synchronization using the off-policy
correction technique with a deep generative model. This uses the advantage of
the inverse operation of a flow-based deep generative model (FDGM) to achieve
the goal corresponding to the current state of the lower-level policy. The
proposed model also considers the freedom of the goal dimension between HRL
policies which makes it the generalized inverse model of the model-free RL in
HRL with the optimal synchronization method. The comparative experiment results
show the performance of our proposed model.
