Deep Reinforcement Learning-Based Off-Road Path Planning via Low-Dimensional Simulation

Path planning is a critical aspect of autonomous driving. Traditional methods have excelled in urban environments but struggle off-road due to unpredictable conditions. This paper introduces a Deep Reinforcement Learning-based off-road path planning approach called PPOAC by training the agent in a low-dimensional simulator built by Occupancy Grid Maps (OGMs). A self-supervised auxiliary task is proposed to help the agent to learn environmental knowledge. Moreover, an adaptive curriculum learning method is introduced to enhance the agent's training efficiency and generalization ability. The agent is trained and evaluated in real-world data, and experimental results demonstrate that it can obtain traversable and safe paths while generalizing effectively to unknown environments.