自动驾驶中的主动利他学习与信息充分性
Active Altruism Learning and Information Sufficiency for Autonomous Driving
摘要：
车辆之间的安全交互需要能够选择揭示其他车辆偏好的动作。由于探索性行动通常不会直接有助于实现其目标，因此交互式工具还必须能够确定何时适合执行这些行动。在这项工作中，我们演示了如何主动学习方法可以用来激励自动驾驶汽车（AV）选择的行动，揭示了另一辆车的利他主义倾向的信息。我们确定了一个属性，信息充分性，奖励功能应该有，以保持不必要的干扰追求的目标探索。我们的经验表明，奖励功能，不具有信息充分性容易探索不足，这可能会导致次优行为。我们提出了一个奖励的定义，具有信息充分性，并表明，它有助于AV选择探索性的行动，以估计利他主义的倾向，同时也补偿车辆之间的信念冲突的可能性。
英文摘要：
Safe interaction between vehicles requires the ability to choose actions that reveal the preferences of the other vehicles. Since exploratory actions often do not directly contribute to their objective, an interactive vehicle must also able to identify when it is appropriate to perform them. In this work we demonstrate how Active Learning methods can be used to incentivise an autonomous vehicle (AV) to choose actions that reveal information about the altruistic inclinations of another vehicle. We identify a property, Information Sufficiency, that a reward function should have in order to keep exploration from unnecessarily interfering with the pursuit of an objective. We empirically demonstrate that reward functions that do not have Information Sufficiency are prone to inadequate exploration, which can result in sub-optimal behaviour. We propose a reward definition that has Information Sufficiency, and show that it facilitates an AV choosing exploratory actions to estimate altruistic tendency, whilst also compensating for the possibility of conflicting beliefs between vehicles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
