Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
This addresses efficient resource allocation in distributed computing systems, though it appears incremental as it builds on existing multi-agent reinforcement learning approaches.
The paper tackles decentralized computation offloading by formulating it as a multi-agent decision problem and proposing a novel online learning algorithm that balances competition and cooperation. Empirical results show significant improvements, including 40% reduction in offloading failure rate, 32% reduction in communication overhead, and up to 38% computation resource savings.
We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private and system goals by balancing between competition and cooperation. The mechanism provably has Nash equilibria with optimal resource allocation in the static case. For a dynamic environment, we propose a novel multi-agent online learning algorithm that learns with partial, delayed and noisy state information, and a reward signal that reduces information need to a great extent. Empirical results confirm that through learning, agents significantly improve both system and individual performance, e.g., 40% offloading failure rate reduction, 32% communication overhead reduction, up to 38% computation resource savings in low contention, 18% utilization increase with reduced load variation in high contention, and improvement in fairness. Results also confirm the algorithm's good convergence and generalization property in significantly different environments.