Hierarchical Reinforcement Learning Based on Planning Operators

TL;DR

This paper presents a hierarchical reinforcement learning framework that combines symbolic planning operators with low-level policies to improve long-horizon robotic manipulation tasks like stacking, achieving high success rates and reduced training time.

Contribution

The paper introduces a novel integration of planning operators into hierarchical RL using SAC-X, enabling flexible, reusable policies for complex manipulation tasks.

Findings

97.2% success rate in stacking tasks

Training time reduced by 68%

High success rates in individual actions like reach and lift

Abstract

Long-horizon manipulation tasks such as stacking represent a longstanding challenge in the field of robotic manipulation, particularly when using reinforcement learning (RL) methods which often struggle to learn the correct sequence of actions for achieving these complex goals. To learn this sequence, symbolic planning methods offer a good solution based on high-level reasoning, however, planners often fall short in addressing the low-level control specificity needed for precise execution. This paper introduces a novel framework that integrates symbolic planning with hierarchical RL through the cooperation of high-level operators and low-level policies. Our contribution integrates planning operators (e.g. preconditions and effects) as part of the hierarchical RL algorithm based on the Scheduled Auxiliary Control (SAC-X) method. We developed a dual-purpose high-level operator, which can be used both in holistic planning and as independent, reusable policies. Our approach offers a flexible solution for long-horizon tasks, e.g., stacking a cube. The experimental results show that our proposed method obtained an average of 97.2% success rate for learning and executing the whole stack sequence, and the success rate for learning independent policies, e.g. reach (98.9%), lift (99.7%), stack (85%), etc. The training time is also reduced by 68% when using our proposed approach.