Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.

中文翻译：

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云服务器环境下一种新颖的差分隐私建议框架

由于云计算环境的快速发展，人们普遍认为云服务器对于用户提高工作效率很重要。用户需要了解服务器的功能，并在为用户任务选择最佳可用服务器时做出最佳决策。我们将用户学习服务器能力的过程视为多智能体强化学习过程。强化学习的学习速度和效率可以通过在学习代理之间共享学习经验来提高，这被定义为建议。然而，现有的建议框架受到以下要求的限制：在强化学习环境中建议所有学习代理必须具有完全相同的动作。为了解决上述限制，本文提出了一种用于多智能体强化学习的新型差分隐私建议框架。当代理有不同的动作时，我们提出的方法可以显着改善传统建议框架的应用。该方法还可以通过在具有不同动作的代理之间触发更多潜在的建议过程来扩大建议的适用领域并加速强化学习。