Can DRL optimize vehicular task offloading?
Task Offloading in Vehicular Edge Computing using Deep Reinforcement Learning: A Survey
February 12, 2025
https://arxiv.org/pdf/2502.06963This paper surveys Deep Reinforcement Learning (DRL) methods for optimizing task offloading in vehicular edge computing. It analyzes how vehicles can efficiently use nearby resources (edge servers, fog servers, other vehicles, even UAVs) to handle computation-intensive tasks like autonomous driving and traffic management. The survey explores various network topologies (centralized, distributed, hierarchical) and DRL algorithms (DQN, Actor-Critic, MADDPG, etc.) for single and multi-agent scenarios, highlighting their strengths and limitations.
Key points for LLM-based multi-agent systems:
- Challenges in MDP Formulation: Accurately representing real-world dynamics like vehicle movement, network conditions, and task characteristics in the Markov Decision Process (MDP) is crucial but often oversimplified in existing research. LLMs can potentially be used to generate more realistic and dynamic MDPs.
- Reward Function Design: Balancing local and global objectives in reward functions is essential for multi-agent cooperation. LLMs could contribute to more sophisticated reward design, potentially enabling emergent cooperative behaviors.
- Coordination and Synchronization: Multi-agent systems require careful coordination and synchronization. LLMs may facilitate communication and negotiation between agents, leading to improved collaboration.
- Exploration-Exploitation Trade-off: Existing DRL algorithms struggle to balance exploration and exploitation effectively, particularly in complex, dynamic environments. LLMs might assist in developing more efficient exploration strategies.
- Non-Stationarity: The changing behavior of agents in multi-agent systems creates a non-stationary environment, making it difficult for individual agents to learn stable policies. LLMs could help predict and adapt to the changing environment, potentially leading to more robust learning.