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

תקציר מקורי באנגליתarXiv:2107.08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures. 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 m