Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems online. When the vehicle approaches its physical limits, existing model-based controllers struggle to handle highly nonlinear dynamics, and cannot leverage the large volume of data generated by simulation or real-world driving. To circumvent these limitations, we propose a new learning-based method to tackle the autonomous overtaking problem. We evaluate our approach in the popular car racing game Gran Turismo Sport, which is known for its detailed modeling of various cars and tracks. By leveraging curriculum learning, our approach leads to faster convergence as well as increased performance compared to vanilla reinforcement learning. As a result, the trained controller outperforms the built-in model-based game AI and achieves comparable overtaking performance with an experienced human driver.
翻译:职业车驾驶员可以实施极端超负荷操作。 但是,现有的自动超载算法要么依靠对车辆动态的简化假设,要么试图解决昂贵的轨道优化在线问题。 当车辆接近其物理极限时,现有基于模型的调度员努力处理高度非线性动态,无法利用模拟或现实世界驱动产生的大量数据。为了绕过这些限制,我们提出了一种新的基于学习的方法来解决自动超载问题。我们评价了我们在流行的汽车赛车游戏Gran Turismo Sport(Gran Turismo Sport)中的做法,该游戏以对各种汽车和轨迹进行详细模拟而著称 。 通过利用课程学习,我们的方法导致更快的趋同,以及与香草强化学习相比,性能提高。 结果,受过训练的控制员超越了基于模型的内置游戏AI,实现了与有经验的人驾驶员的类似性超载。