Wireless powered mobile edge computing networks provide a promising solution for supplying both computational capability and stable energy to Internet of Things applications. However, compared with cloud environments, task arrivals and wireless channel conditions at the network edge exhibit stronger temporal dynamics, making fixed deployment schemes insufficient for adapting to workload variations. To address this issue, this paper considers a multi-charger Wireless powered mobile edge computing network and formulates an optimization problem that maximizes the computation completion rate of wireless devices by jointly optimizing wireless charger online deployment decisions, task offloading ratios, and resource allocation. To solve this mixed-integer non-convex problem, the original problem is decomposed into a wireless charger deployment decision subproblem and a resource allocation subproblem. Then, a Gated Transformer-based joint optimization algorithm is proposed to effectively handle long-term network dynamics and high-dimensional action spaces. In this work, wireless charger deployment is modeled as an online discrete decision over a predefined set of candidate locations, where the deployment scheme is updated frame by frame to adapt to network dynamics. Simulation results show that, compared with baseline algorithms, the proposed method improves the average computation completion rate by about 48% under normal workloads and by up to 60% in high-load computationintensive scenarios, while maintaining good stability and convergence.