⚡ Quick Summary
This study introduces a novel approach to joint computation offloading and resource allocation in vehicular edge computing (VEC) systems using multi-agent reinforcement learning. The proposed method, termed AR-MAD4PG, significantly enhances performance by addressing the challenges of resource wastage and latency in intelligent transportation systems.
🔍 Key Details
- 📊 Focus: Joint computation offloading and resource allocation in VEC systems
- ⚙️ Technology: Multi-agent distributed deep deterministic policy gradient (AR-MAD4PG)
- 🧩 Mechanisms: Attention mechanism, recurrent neural networks (RNN), shared agent graph
- 📈 Simulation: Based on actual vehicle trajectories
🔑 Key Takeaways
- 🚗 VEC systems are crucial for reducing service latency in vehicular applications.
- 💡 Existing methods often lead to resource wastage due to static resource allocation.
- 🔍 AR-MAD4PG effectively addresses the partial observability of agents.
- 📉 Historical data is utilized through an RNN-based feature extraction network.
- ✨ Multi-head attention compresses the observation-action space, enhancing efficiency.
- 🏆 Experimental results demonstrate superior performance compared to existing approaches.
- 🌐 Authors: Wang C, Wang Y, Yuan Y, Peng S, Li G, Yin P.
- 📅 Published in: Neural Networks, 2024.
📚 Background
The emergence of vehicular edge computing (VEC) represents a significant advancement in the development of intelligent transportation systems. VEC aims to provide lower service latency for vehicular applications, which is essential for enhancing the overall efficiency and safety of transportation networks. However, achieving this goal is challenging due to the limited resources available in VEC systems and the stringent latency requirements of various applications.
🗒️ Study
The study addresses the critical issue of real-time task offloading and heterogeneous resource allocation in VEC systems. The authors propose a decentralized solution that leverages the attention mechanism and recurrent neural networks (RNN) to optimize resource allocation dynamically. By constructing a shared agent graph and implementing a periodic communication mechanism, the study enhances the ability of edge nodes to aggregate and utilize information effectively.
📈 Results
The experimental results indicate that the proposed AR-MAD4PG method outperforms existing approaches in terms of efficiency and resource utilization. The integration of RNNs for historical state and resource allocation information, combined with the multi-head attention mechanism, significantly reduces the dimensionality of the observation-action space, leading to improved decision-making capabilities for agents in the VEC system.
🌍 Impact and Implications
The findings of this study have profound implications for the future of intelligent transportation systems. By optimizing computation offloading and resource allocation, the proposed method can lead to enhanced performance in VEC systems, ultimately contributing to safer and more efficient vehicular networks. This research paves the way for further advancements in the integration of AI and machine learning technologies in transportation.
🔮 Conclusion
This study highlights the potential of multi-agent reinforcement learning in addressing the challenges faced by vehicular edge computing systems. The innovative approach of AR-MAD4PG not only improves resource allocation and task offloading but also sets a precedent for future research in this domain. As intelligent transportation systems continue to evolve, the integration of such advanced technologies will be crucial for achieving optimal performance.
💬 Your comments
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Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning.
Abstract
Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks’ different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.
Author: [‘Wang C’, ‘Wang Y’, ‘Yuan Y’, ‘Peng S’, ‘Li G’, ‘Yin P’]
Journal: Neural Netw
Citation: Wang C, et al. Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning. Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning. 2024; 179:106621. doi: 10.1016/j.neunet.2024.106621