Summary of Generalized Multi-objective Reinforcement Learning with Envelope Updates in Urllc-enabled Vehicular Networks, by Zijiang Yan et al.
Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks
by Zijiang Yan, Hina Tabassum
First submitted to arxiv on: 18 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
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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 The novel multi-objective reinforcement learning (MORL) framework developed in this paper jointly optimizes wireless network selection and autonomous driving policies in a multi-band vehicular network operating on conventional sub-6GHz spectrum and Terahertz frequencies. The framework maximizes traffic flow, minimizes collisions by controlling vehicle motion dynamics, and enhances ultra-reliable low-latency communication (URLLC) while minimizing handoffs (HOs). To achieve this, the authors cast the problem as a multi-objective Markov Decision Process (MOMDP) and develop solutions for both predefined and unknown preferences of the conflicting objectives. The proposed framework is designed to optimize policies that consider scalarizing transportation and telecommunication rewards using predefined preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a novel MORL framework that optimizes wireless network selection and autonomous driving policies in a multi-band vehicular network. This framework maximizes traffic flow, minimizes collisions, and enhances communication while minimizing handoffs. The authors use reinforcement learning to solve this complex problem. |
Keywords
» Artificial intelligence » Reinforcement learning
