This　study　develops　a　novel　end-to-end　formation　strategy　for　leader-follower　formation　control　of　mobile　robots　that　uses　onboard　LiDAR　sensors　in　non-communication　environments.　The　main　contributions　of　this　letter　are　twofold:　Firstly,　we　propose　a　point　cloud-based　LiDAR　servoing　control　method　(PCLS)　aimed　at　ensuring　mobile　robots　achieve　the　predefined　formation　performance　without　direct　communication.　Secondly,　an　innovative　two-stage　Soft　Actor-Critic　(TSSAC)　algorithm　is　presented,　specifically　designed　for　end-to-end　training　of　PCLS.　This　algorithm　skillfully　combines　the　strengths　of　a　distance-based　agent　(serving　as　a　＂teacher＂)　and　a　point　cloud-based　agent　(serving　as　a　＂student＂),　effectively　addressing　the　issues　of　slow　convergence　and　insufficient　generalization　in　deep　reinforcement　learning　methods　that　use　high-dimensional　features　(such　as　point　clouds,　images)　as　inputs.　Furthermore,　as　part　of　our　method,　we　designed　a　novel　reward　function　and　normalized　the　point　cloud　inputs　to　provide　consistent　incentives　for　the　agent　across　diverse　formation　tasks,　thereby　facilitating　better　learning　and　adaptation　to　formation　tasks　in　different　environments.　Finally,　through　extensive　experiments　conducted　in　the　Gazebo　simulator　and　real-world　environments,　we　confirmed　the　effectiveness　of　the　proposed　method.　Compared　to　other　formation　control　strategies,　our　approach　relies　solely　on　onboard　LiDAR　sensors,　without　the　need　for　additional　communication　devices,　while　ensuring　excellent　transient　and　steady-state　performance.