Summary of Dependency-aware Cav Task Scheduling Via Diffusion-based Reinforcement Learning, by Xiang Cheng et al.
Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning
by Xiang Cheng, Zhi Mao, Ying Wang, Wen Wu
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for scheduling tasks in connected autonomous vehicles (CAVs) aided by unmanned aerial vehicles (UAVs). The strategy assigns dependent subtasks to nearby CAVs or a base station to minimize average task completion time. A joint optimization problem is formulated as a Markov decision process, which is solved using a reinforcement learning algorithm called Synthetic DDQN based Subtasks Scheduling. This algorithm uses a diffusion model-based synthetic experience replay to accelerate convergence and improve sample efficiency. The proposed approach is evaluated through simulation results, demonstrating its effectiveness in reducing task completion time compared to benchmark schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make better decisions for connected cars and drones working together. It’s like solving a puzzle to get tasks done quickly! The researchers created a new way to schedule these tasks by giving subtasks to nearby cars or a central hub, making sure everything is completed efficiently. They also developed an algorithm that uses pretend data to learn faster and make better choices. This means we can use this approach in real-life scenarios to improve the performance of connected autonomous vehicles. |
Keywords
» Artificial intelligence » Diffusion model » Optimization » Reinforcement learning
