基于强化学习的自动驾驶汽车路径规划方法研究综述

路径规划作为自动驾驶的关键技术，具有广阔的应用前景和科研价值。探索解决自动驾驶车辆路径规划问题的方法，着重关注基于强化学习的路径规划方法。在阐述基于常规方法和强化学习方法的路径规划技术的基础上，重点总结了基于强化学习和深度强化学习来解决自动驾驶车辆路径规划问题的算法，并将算法按照基于值和基于策略的方式进行分类，分析各类算法的特点、优缺点及改进措施。最后对基于强化学习的路径规划技术的未来发展方向进行了展望。

Review of research on path planning methods for autonomous vehicles based on reinforcement learning

As a key technology of autonomous driving, path planning has broad application prospects and scientific research value. This paper explored ways to solve the path planning problem for autonomous vehicles, focusing on reinforcement lear-ning-based path planning methods. On the basis of expounding the path planning technology based on conventional method and reinforcement learning method, this paper focused on summarizing the algorithm based on reinforcement learning and deep reinforcement learning method to solve the path planning problem of autonomous vehicles, classified the algorithm according to value-based and policy-based methods, analyzed the characteristics, benefits and drawbacks, and improvement measures of each type of algorithms. Finally, this paper looked forward to the future development direction of path planning technology based on reinforcement learning.