Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.

该论文介绍了一种将规划和学习结合起来的决策框架，通过蒙特卡罗树搜索和深度强化学习，并将其应用于高速公路驾驶案例，并与基线方法进行比较。结果表明该框架综合了规划和学习的优势，能够更好地应对自动驾驶中具有挑战性的决策制定问题。

在自主驾驶的战术决策中结合规划和深度强化学习