基于Q-强化学习的多Agent协商策略及算法

Strategy and Algorithm of Multi-Agent Negotiation Based on Q-reinforcement Learning

针对传统Agent协商策略学习能力不足,不能满足现代电子商务环境需要的问题,采用Q-强化学习理论对Agent的双边协商策略加以改进,提出基于Q-强化学习的Agent双边协商策略,并设计实现该策略的算法。通过与时间协商策略比较,证明改进后的Agent协商策略在协商时间、算法效率上优于未经学习的时间策略,能够增强电子商务系统的在线学习能力,缩短协商时间,提高协商效率。

As lack of sufficient learning ability in traditional negotiation strategy of Agents, Agents＇ techniques are still unable to meet the needs of modern E-commerce. Aiming at this problem, Q-reinforcement learning theory is adapted to improve the bilateral negotiation strategy of Agents and the corresponding negotiation algorithm is designed to achieve the negotiation strategies. Comparing with the negotiation strategy of time strategy, the proposed Agent negotiation strategy is better than time strategy in terms of negotiation time and algorithm efficiency. It shows that algorithm can strengthen online learning ability of E-commerce system, shorten the negotiation time, and improve the negotiation efficiency.