Navigating vast and visually intricate gaming environments poses unique challenges, especially when agents are deprived of absolute positions and orientations during testing. This paper addresses the challenge of training agents in such environments using a limited set of offline navigation data and a more substantial set of offline position data. We introduce the Image-Goal Network (IG-Net), an innovative solution tailored for these challenges. IG-Net is designed as an image-goal-conditioned navigation agent, which is trained end-to-end, directly outputting actions based on inputs without intermediary mapping steps. Furthermore, IG-Net harnesses position prediction, path prediction and distance prediction to bolster representation learning to encode spatial map information implicitly, an aspect overlooked in prior works. Results demonstrate IG-Net's potential in navigating large-scale gaming environments, providing both advancements in the field and tools for the broader research community.