Reward function weight combinations.

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.