Multi-task meta-initialized DQN for fast adaptation to unseen slicing tasks in O-RAN

Bosen Zeng and Xianhua Niu

PLOS ONE, 2025, vol. 20, issue 10, 1-15

Abstract: The open radio access network (O-RAN) architecture facilitates intelligent radio resource management via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are integrated into RICs to address dynamic O-RAN slicing challenges. However, DRL-based O-RAN slicing suffers from instability and performance degradation when deployed on unseen tasks. We propose M2DQN, a hybrid framework that combines multi-task learning (MTL) and meta-learning to optimize DQN initialization parameters for rapid adaptation. Our method decouples the DQN into two components: shared layers trained via MTL to capture cross-task representations, and task-specific layers optimized through meta-learning for efficient fine-tuning. Experiments in an open-source network slicing environment demonstrate that M2DQN outperforms MTL, meta-learning, and policy reuse baselines, achieving improved initial performance across 91 unseen tasks. This demonstrates an effective balance between transferability and adaptability. Code is available at: https://github.com/bszeng/M2DQN.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0330226

DOI: 10.1371/journal.pone.0330226

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