论文标题
通过合并最佳控制和强化学习，在高度受约束空间中解决多车辆冲突
Multi-vehicle Conflict Resolution in Highly Constrained Spaces by Merging Optimal Control and Reinforcement Learning
论文作者
论文摘要
我们提出了一种新的方法，可以解决高度受约束空间中多车冲突的问题。提出了最佳控制问题，以结合非线性，非全面车辆动力学和确切的碰撞避免约束。可以通过在简化的离散环境中使用强化学习（RL）的首先学习配置策略来获得解决问题的方法，然后使用这些策略来塑造原始问题的约束空间。仿真结果表明，我们的方法可以探索有效的动作，以解决限制空间中的冲突，并产生灵巧的动作，这些动作既无碰撞又可行。
We present a novel method to address the problem of multi-vehicle conflict resolution in highly constrained spaces. An optimal control problem is formulated to incorporate nonlinear, non-holonomic vehicle dynamics and exact collision avoidance constraints. A solution to the problem can be obtained by first learning configuration strategies with reinforcement learning (RL) in a simplified discrete environment, and then using these strategies to shape the constraint space of the original problem. Simulation results show that our method can explore efficient actions to resolve conflicts in confined space and generate dexterous maneuvers that are both collision-free and kinematically feasible.
