Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model

JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Khadeer Hussain

High-dimensional state and action spaces com- bined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architec- tures. Hierarchical Reinforcement Learning (HRL) demonstrates superior performance compared to atomic RL approaches in these challenging scenarios. HRL can manage the complexity of commands to achieve task objectives through its hierarchical structure. One of the key challenges in HRL is efficiently training each level’s policy with optimal data collection from its experience. Off-policy correction is a critical technique for facilitating sample-efficient off-policy training in HRL, as it addresses the non-stationary issue of higher-level policy training. However, existing methods typically employ indirect probabilistic approaches that fail to accurately capture the current capability of the lower-level policy. This mismatch ultimately constrains the effectiveness of higher-level policy training. In this paper, we propose a novel HRL model that supports direct off-policy correction based on a Flow-based Deep Generative Model (FDGM). This approach leverages the inverse operation of FDGM to achieve goals aligned with the current knowledge of the lower-level policy. Additionally, our model addresses the limitations of FDGM to enable its effective use in HRL. Through comparative experiments on benchmark environments, our model demonstrates superior performance over existing models