论文标题
多代理分布式强化学习，用于制定分散的下载决策
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
论文作者
论文摘要
我们将计算卸载作为自主代理的分散决策问题。我们设计了一种互动机制，该机制通过平衡竞争与合作之间的平衡来激励代理以对齐私人和系统目标。该机制在静态情况下证明具有NASH平衡，并具有最佳的资源分配。对于一个动态的环境，我们提出了一种新颖的多学院在线学习算法，该算法以部分，延迟和嘈杂的状态信息学习，以及在很大程度上减少信息需求的奖励信号。经验结果证实，通过学习，代理人显着改善了系统和个人绩效，例如40％的下载失败率降低，32％的沟通间接费用降低，高达38％的计算资源在低争夺中节省了38％的计算资源，18％的利用率随着降低的高负载变化而增加，并提高了公平性。结果还证实了该算法在显着不同环境中的良好收敛性和泛化性能。
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.
