Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation
Ziyun Zhou,
Jingwei Shang,
Yimang Li
Abstract:Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for sub-goals, have been introduced to address these limitations by facilitating the agent’s understanding and reasoning with respect to the task hierarchy. In this paper, we… Show more
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