Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning
This addresses the challenge of slow learning in high-dimensional RL domains, offering a practical solution for improving learning speeds, though it appears incremental as it builds on existing hierarchical methods.
The paper tackles the problem of slow learning speeds in reinforcement learning by proposing a novel method, ARM-HSTRL, which autonomously decomposes tasks using association rule mining to extract hierarchical structures, resulting in significant efficiency and performance improvements in RL and multi-task RL.
Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining. We introduce a novel method called ARM-HSTRL (Association Rule Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning). It extracts temporal and structural relationships of sub-goals in RL, and multi-task RL. In particular,it finds sub-goals and relationship among them. It is shown the significant efficiency and performance of the proposed method in two main topics of RL.