Lane change in dense traffic is considered a challenging problem that typically requires the recognization of an opportune and appropriate time for maneuvers. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). The embodied high-level MPC in our proposed framework is parameterized with augmented decision variables, where full-state references and regulatory factors concerning their importance are introduced. In this sense, improved adaptiveness to dense and dynamic environments with high complexity is exhibited. Furthermore, to improve the convergence speed and ensure a high-quality policy, effective curriculum design is integrated into the reinforcement learning (RL) framework with policy transfer and enhancement. With comprehensive experiments towards the chance-aware lane-change scenario, accelerated convergence speed and improved reward performance are demonstrated through comparisons with representative baseline methods. It is noteworthy that, given a narrow chance in the dense and dynamic traffic flow, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate.
翻译:在这项工作中,我们通过强化课程学习(CRL)提出一个具有高层次模型预测控制(MPC)的 " 偶然改变航道战略 " 。我们拟议框架中体现的高层次移动车道的参数是增加决定变量的参数,在变量中引入了全州参照基准和与其重要性有关的监管因素。从这个意义上讲,对复杂程度高的密集和动态环境的适应性得到了提高。此外,为了提高趋同速度和确保高质量的政策,将有效的课程设计纳入强化学习(RL)框架,并进行政策转移和加强。随着对机会改变车道设想的全面实验,通过与有代表性的基准方法的比较,展示了加速的趋同速度和更好的奖励业绩。值得注意的是,鉴于交通流量密集和动态变化的机会较小,拟议办法会产生高质量的换车道调整,使车辆以高成功率并入交通流量。</s>