More and more researchers are trying to use deep reinforcement learning (DRL) for mobile robot tasks due to its powerful inference capability. However, deep reinforcement learning requires a large amount of data for DRL training in the pre-experimental stage, which hinders the application of the algorithm. On the other hand, the inconsistency between the ROS data interface and DRL GYM-Like data interface leads to a high cost of migration of the algorithm verification. This paper proposes a fast simulator generation method using linear approximate kinematics model and bake-based lidar rendering methods to generate a fast approximate simulator used in the pre-experiment stage to solve the problem of data cost. At the same time, an experimental system design scheme that converts the ROS interface into a GYM-like interface is also proposed to simplify the deployment process of deep reinforcement learning. We evaluate our proposed method on collision avoidance tasks in a variety of kinematics models and lidar scenarios. Our Method achieves about 14.2 times kinematics simulation speedup and 2.56 times lidar rendering speedup. We open-sourced our simulation environment and robot system software at https://github.com/efc-robot/MultiVehicleEnv and https://github.com/efc-robot/NICS_MultiRobot_Platform