Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative Model

TL;DR

This paper introduces a novel hierarchical reinforcement learning model that employs a deep generative model and off-policy correction to optimally synchronize policies at different levels, improving learning efficiency in complex tasks.

Contribution

It proposes a new HRL framework using a flow-based deep generative model for optimal level synchronization via off-policy correction, enhancing policy training coherence.

Findings

The model effectively synchronizes hierarchical policies during training.

Experimental results demonstrate improved performance over existing methods.

The approach generalizes inverse models for HRL with optimal synchronization.

Abstract

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.