We consider a cloud-based control architecture in which the local plants outsource the control synthesis task to the cloud. In particular, we consider a cloud-based reinforcement learning (RL), where updating the value function is outsourced to the cloud. To achieve confidentiality, we implement computations over Fully Homomorphic Encryption (FHE). We use a CKKS encryption scheme and a modified SARSA(0) reinforcement learning to incorporate the encryption-induced delays. We then give a convergence result for the delayed updated rule of SARSA(0) with a blocking mechanism. We finally present a numerical demonstration via implementing on a classical pole-balancing problem.
翻译:我们考虑一个基于云的控制架构,让当地工厂将控制合成任务外包给云层。特别是,我们考虑基于云的强化学习(RL),更新值功能将外包给云层。为了保密,我们用CKKS加密计划和经修改的SASA(0)强化学习方法对完全同色加密进行计算。我们使用CKKS加密计划和经修改的SASA(0)强化学习方法将加密导致的延误纳入其中。我们随后得出一个趋同结果,延迟更新SASA(0)规则,并设置一个阻塞机制。我们最后通过执行典型的两极平衡问题来展示数字。