Counterfactual Reward Estimation for Credit Assignment in Multi-agent Deep Reinforcement Learning over Wireless Video Transmission

This study investigates frame-wise optimization in Mobile Edge Computing (MEC) for video transmission, emphasizing dynamic adaptation to diverse frame complexities and efficient resource utilization. The comprehensive system model captures the complexities of joint optimizations in MEC for real-time...

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Vydáno v:Proceedings of the International Conference on Distributed Computing Systems s. 1177 - 1189
Hlavní autoři: Wenhan, Y., Qian, Liangxin, Chua, Terence Jie, Zhao, Jun
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 23.07.2024
Témata:
Complexity theory
credit assignment
Dynamic scheduling
multi-agent deep reinforcement learning
Resource management
Streaming media
Training
User experience
Video transmission
Wireless communication
ISSN:2575-8411
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Shrnutí:This study investigates frame-wise optimization in Mobile Edge Computing (MEC) for video transmission, emphasizing dynamic adaptation to diverse frame complexities and efficient resource utilization. The comprehensive system model captures the complexities of joint optimizations in MEC for real-time video transmission, addressing challenges associated with error concealment techniques, and enhancing the user experience by addressing successive frame losses. To handle credit assignment in multi-agent scenarios, we integrate counterfactual reward shaping, introducing a counterfactual reward multi-agent proximal policy optimization (CRMAPPO). Results reveal the impact of the credit assignment parameter (β) on algorithm performance, demonstrating a trade-off between accurate credit assignment and policy bias. The study emphasizes CRMAPPO's performance, surpassing traditional MAPPO under optimal β choices, marking a substantial 109.18% improvement in total rewards. This research significantly contributes to optimizing resource allocation in video transmission within MEC frameworks, addressing challenges associated with frame-wise optimization and providing a nuanced understanding of credit assignment dynamics in multi-agent environments.
ISSN:2575-8411
DOI:10.1109/ICDCS60910.2024.00112