基于强化学习的自适应多Agent系统的构造

自适应系统所处的环境往往是不确定的,其变化事先难以预测,如何支持这种环境下复杂自适应系统的开发已经成为软件工程领域面临的一项重要挑战.强化学习是机器学习领域中的一个重要分支,强化学习系统能够通过不断试错的方式,学习环境状态到可执行动作的最优对应策略.本文针对自适应系统环境不确定的问题,将Agent技术与强化学习技术相结...

The Construction of a Self-adaptive Multi-Agent System Based on Reinforcement Learning

The environment of self-adaptive systems is often uncertain,and the changes are difficult to predict.To develop such complex self-adaptive software systems has become a great challenge in the domain of software engineering.Reinforcement learning is an important branch of machine learning.A reinforcement learning system can learn the optimal mapping policy from the states of environment to the actions by means of trail-and-error.Aiming to deal with the uncertainty of environments,this paper combines the agent technology and the reinforcement learning technology together,and proposes an adaptive mechanism based on reinforcement learning and the corresponding approach to construct complex self-adaptive systems that can adapt to the changes of uncertain environments.A case is illustrated to validate the effectiveness of the proposed mechanism and approach.