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

• URL: http://arxiv.org/abs/2508.17751v1
• Date: Mon, 25 Aug 2025 07:44:35 GMT
• Title: Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning
• Abstract summary: MANGO (Multilayer Abstraction for Nested Generation of Options) is a novel hierarchical reinforcement learning framework.<n>It decomposes complex tasks into multiple layers of abstraction, where each layer defines an abstract state space.<n>Experiments conducted in procedurally-generated grid environments demonstrate substantial improvements in sample efficiency and generalization capabilities.
• Score: 9.231127413030926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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