Integrating Acting, Planning and Learning in Hierarchical Operational Models

TL;DR

This paper introduces hierarchical operational models and a UCT-like planning algorithm, UPOM, for dynamic task execution, along with learning strategies to enhance decision-making in the RAE system.

Contribution

It presents a novel hierarchical planning and learning framework, UPOM and associated strategies, for improving autonomous task performance in changing environments.

Findings

UPOM significantly improves efficiency and success ratio.

Learning strategies effectively map decision contexts to methods.

Experimental results validate the approach across multiple domains.

Abstract

We present new planning and learning algorithms for RAE, the Refinement Acting Engine. RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space of operational models in order to find a near-optimal method to use for the task and context at hand. Our learning strategies acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. Our experimental results show that UPOM and our learning strategies significantly improve RAE's performance in four test domains using two different metrics: efficiency and success ratio.