Reinforcement learning (RL) for autonomous driving faces challenges such as low sample efficiency and convergence difficulties when directly trained in complex scenarios. To address this issue, we propose a cross-simulation agent construction method based on unified data representation and implement the DrivingGym training environment. This method abstracts the input state into three layers: sensor data, vehicle states, and road network information. The control interface unification is achieved across different simulation environments through action adapters. Experiments on common simulation platforms such as CARLA and Metadrive demonstrate that the proposed method can support training with mainstream reinforcement learning frameworks like RLlib and Stable-Baselines3, and enable cross-simulation application of autonomous driving policies from simple to complex scenarios.
