MENTOR: Guiding Hierarchical Reinforcement Learning with Human Feedback and Dynamic Distance Constraint

arXiv:2402.14244v1 Announce Type: cross
Abstract: Hierarchical reinforcement learning (HRL) provides a promising solution for complex tasks with sparse rewards of intelligent agents, which uses a hierarchical framework that divides tasks into subgoals and completes them sequentially. However, current methods struggle to find suitable subgoals for ensuring a stable learning process. Without additional guidance, it is impractical to rely solely on exploration or heuristics methods to determine subgoals in a large goal space. To address the issue, We propose a general …

abstract agents arxiv cs.ai cs.hc cs.lg current dynamic feedback framework hierarchical human human feedback intelligent mentor reinforcement reinforcement learning solution struggle tasks them type