Meta-Offline and Distributional Multi-Agent RL for Risk-Aware Decision-Making

2026-04-23 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

Eslam Eldeeb and Hirley Alves propose a novel meta-offline multi-agent reinforcement learning (MARL) algorithm for timely Unmanned Aerial Vehicle (UAV) path planning and data collection in dynamic wireless networks. This framework combines Conservative Q-learning (CQL) for offline training with pre-collected datasets and Model Agnostic Meta-Learning (MAML) for adaptability to changing network configurations and objectives. The authors introduce two variants: meta-independent-CQL (M-I-CQL) and meta-centralized training decentralized execution-CQL (M-CTDE-CQL). Simulations, conducted on an NVIDIA Tesla V100 GPU using PyTorch, demonstrate that both proposed meta-MARL schemes outperform conventional MARL techniques without MAML. Specifically, M-CTDE-CQL achieves up to 50% faster convergence in dynamic scenarios compared to benchmarks, enhancing scalability, robustness, and adaptability in 6G wireless communication systems.

Key takeaway

Research Scientists developing UAV-based communication systems should consider implementing meta-offline MARL, particularly the M-CTDE-CQL variant, to overcome the limitations of online training and environment-specific models. This approach allows for rapid adaptation to dynamic network conditions and objectives, such as minimizing Age-of-Information and transmission power, using pre-collected data, which is crucial for robust 6G network deployments.

Key insights

Combining offline MARL with meta-learning enables UAVs to adapt quickly to dynamic wireless network conditions.

Principles

• Offline training addresses safety and practicality concerns.
• Meta-learning ensures adaptability to dynamic network changes.
• Centralized training improves multi-agent performance.

Method

The proposed M-CQL framework integrates CQL for offline learning from fixed datasets with MAML to find optimal initial parameters that rapidly adapt to new tasks (network configurations/objectives) via a few SGD steps.

In practice

• Use M-CTDE-CQL for faster convergence in dynamic UAV networks.
• Increase training tasks to enhance Q-network initial weights.
• Leverage offline datasets for safe, efficient policy optimization.

Topics

• Multi-Agent Reinforcement Learning
• Meta-Learning
• Conservative Q-Learning
• Unmanned Aerial Networks
• UAV Path Planning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.