An Improved Deep Reinforcement Learning Algorithm for Path Planning in Unmanned Driving

In the domain of intelligent transportation systems, the advent of autonomous driving technology represents a critical milestone, profoundly shaping the automotive industry's evolutionary path. This technology's core, particularly the algorithms facilitating driverless path planning, has attracted significant scholarly interest. This paper presents an advanced Deep Reinforcement Learning algorithm for Path Planning (DRL-PP), designed to rectify the shortcomings inherent in existing path planning techniques. Considering the complex nature of the environment, the DRL-PP algorithm is meticulously crafted to ascertain optimal actions, thereby effectively reducing the propensity for overfitting. The algorithm harnesses the capabilities of deep reinforcement learning, utilizing neural networks to identify the most advantageous action corresponding to a specific state. It then constructs an optimal action sequence, extending from the vehicle's initial position to its designated target. Additionally, the algorithm enhances the reward function by incorporating data pertinent to the objective. This refinement enables the nuanced differentiation of action values based on dynamically adjusted reward metrics, thereby augmenting the efficiency of the action selection process and yielding improved results in path planning. Empirical results validate the algorithm's proficiency in stabilizing the reward metric while minimizing exploratory steps, consistently surpassing comparative models in path-finding effectiveness.