Can offline MARL improve RRM efficiency?
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
January 23, 2025
https://arxiv.org/pdf/2501.12991This paper proposes an offline multi-agent reinforcement learning (MARL) framework for optimizing radio resource management (RRM) in wireless networks. The goal is to develop efficient scheduling policies for multiple access points (APs) to jointly maximize both the overall data rate (sum rate) and fairness among users (tail rate). The system uses a pre-collected dataset of network conditions and actions to train the agents, eliminating the need for costly and potentially risky real-time interaction during the learning process.
Key points for LLM-based multi-agent systems:
- Offline Training: Emphasizes the benefits of offline training, which aligns well with the use of LLMs to train agents with pre-existing data, reducing the need for online environment interaction.
- Centralized Training with Decentralized Execution (CTDE): Explores CTDE, which offers a potential blueprint for training complex multi-agent systems with LLMs in a centralized manner, then deploying individual agents that can act independently based on their learned policies.
- Conservative Q-Learning (CQL): Uses CQL to mitigate the distributional shift between training data and real-world scenarios, a critical consideration when applying LLMs, which can be sensitive to such shifts.
- Focus on Communication Efficiency: Addresses the communication overhead common in multi-agent systems by using localized observations and decentralized execution, echoing concerns about the token limits and computational costs in LLM interactions.
- Dataset Quality & Size: Demonstrates the impact of dataset quality and size on performance, highlighting the importance of careful data curation and scaling for LLM-based agent training.