Can RL agents fairly stream multimedia?
FAIRSTREAM: FAIR MULTIMEDIA STREAMING BENCHMARK FOR REINFORCEMENT LEARNING AGENTS
October 29, 2024
https://arxiv.org/pdf/2410.21029This paper introduces FairStream, a new benchmark environment for multi-agent reinforcement learning (MARL) designed to tackle challenges in fair multimedia streaming. It focuses on issues relevant to real-world systems, such as clients with varying network conditions, resource demands, and asynchronous behavior.
The key takeaway for LLM-based multi-agent systems is the importance of considering partial observability, agent heterogeneity, and asynchronicity when designing these systems for real-world applications like multimedia streaming. The paper demonstrates that standard MARL algorithms like PPO may struggle with these challenges and highlights the need for further research in this area.