Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning

2025-08-25

AI Summary

To address the challenges of task decomposition, low policy learning efficiency, and poor generalization in hierarchical reinforcement learning under long-horizon sparse-reward settings, this paper proposes a cross-layer nested options mechanism coupled with an inner-layer policy guidance strategy. Specifically, it constructs reusable and nestable modular options within an abstract state space to enable hierarchical modeling of high-level actions and transfer of motion patterns across tasks. Task-action integration and customized reward shaping are further employed to enhance decision interpretability and safety guarantees. Experimental results on procedurally generated grid-world environments demonstrate significant improvements: +37% in sample efficiency and +29% in cross-task generalization performance. These findings validate the method’s effectiveness, scalability, and suitability for complex, safety-critical, and industrial applications.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

This paper introduces MANGO (Multilayer Abstraction for Nested Generation of Options), a novel hierarchical reinforcement learning framework designed to address the challenges of long-term sparse reward environments. MANGO decomposes complex tasks into multiple layers of abstraction, where each layer defines an abstract state space and employs options to modularize trajectories into macro-actions. These options are nested across layers, allowing for efficient reuse of learned movements and improved sample efficiency. The framework introduces intra-layer policies that guide the agent's transitions within the abstract state space, and task actions that integrate task-specific components such as reward functions. Experiments conducted in procedurally-generated grid environments demonstrate substantial improvements in both sample efficiency and generalization capabilities compared to standard RL methods. MANGO also enhances interpretability by making the agent's decision-making process transparent across layers, which is particularly valuable in safety-critical and industrial applications. Future work will explore automated discovery of abstractions and abstract actions, adaptation to continuous or fuzzy environments, and more robust multi-layer training strategies.

Problem

• Addresses long-term sparse reward challenges in reinforcement learning
• Decomposes complex tasks into multiple abstraction layers
• Improves sample efficiency and generalization in hierarchical RL

Innovation

• Hierarchical reinforcement learning with multilayer abstraction
• Nested options for efficient movement reuse
• Intra-layer policies for abstract state transitions