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 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.