Feudal Graph Reinforcement Learning

• URL: http://arxiv.org/abs/2304.05099v4
• Date: Tue, 25 Jun 2024 16:16:49 GMT
• Title: Feudal Graph Reinforcement Learning
• Authors: Tommaso Marzi, Arshjot Khehra, Andrea Cini, Cesare Alippi,
• Abstract summary: Graph-based representations and message-passing modular policies constitute prominent approaches to tackling composable control problems in Reinforcement Learning. We propose a novel methodology, named Feudal Graph Reinforcement Learning (FGRL), that addresses such challenges by relying on hierarchical RL and a pyramidal message-passing architecture. In particular, FGRL defines a hierarchy of policies where high-level commands are propagated from the top of the hierarchy down through a layered graph structure.
• Score: 18.069747511100132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based representations and message-passing modular policies constitute prominent approaches to tackling composable control problems in Reinforcement Learning (RL). However, as shown by recent graph deep learning literature, such local message-passing operators can create information bottlenecks and hinder global coordination. The issue becomes more serious in tasks requiring high-level planning. In this work, we propose a novel methodology, named Feudal Graph Reinforcement Learning (FGRL), that addresses such challenges by relying on hierarchical RL and a pyramidal message-passing architecture. In particular, FGRL defines a hierarchy of policies where high-level commands are propagated from the top of the hierarchy down through a layered graph structure. The bottom layers mimic the morphology of the physical system, while the upper layers correspond to higher-order sub-modules. The resulting agents are then characterized by a committee of policies where actions at a certain level set goals for the level below, thus implementing a hierarchical decision-making structure that can naturally implement task decomposition. We evaluate the proposed framework on a graph clustering problem and MuJoCo locomotion tasks; simulation results show that FGRL compares favorably against relevant baselines. Furthermore, an in-depth analysis of the command propagation mechanism provides evidence that the introduced message-passing scheme favors learning hierarchical decision-making policies.

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