Model selection in continual transfer learning reduces UAV trajectory optimization convergence time by 44-56% versus training from scratch in O-RAN simulations using city maps and ray tracing.
Meta-Offline and Distributional Multi-Agent RL for Risk-Aware Decision-Making
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abstract
Mission critical applications, such as UAV-assisted IoT networks require risk-aware decision-making under dynamic topologies and uncertain channels. We propose meta-conservative quantile regression (M-CQR), a meta-offline distributional MARL algorithm that integrates conservative Q-learning (CQL) for safe offline learning, quantile regression DQN (QR-DQN) for risk-sensitive value estimation, and model-agnostic meta-learning (MAML) for rapid adaptation. Two variants are developed: meta-independent CQR (M-I-CQR) and meta-CTDE-CQR. In a UAV-based communication scenario, M-CTDE-CQR achieves up to 50% faster convergence and outperforms baseline MARL methods, offering improved scalability, robustness, and adaptability for risk-sensitive decision-making. Code is available at https://github.com/Eslam211/MA_Meta_ODRL
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Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
Model selection in continual transfer learning reduces UAV trajectory optimization convergence time by 44-56% versus training from scratch in O-RAN simulations using city maps and ray tracing.