Eco-driving strategies have been shown to provide significant reductions in fuel consumption. This paper outlines an active driver assistance approach that uses a residual policy learning (RPL) agent trained to provide residual actions to default power train controllers while balancing fuel consumption against other driver-accommodation objectives. Using previous experiences, our RPL agent learns improved traction torque and gear shifting residual policies to adapt the operation of the powertrain to variations and uncertainties in the environment. For comparison, we consider a traditional reinforcement learning (RL) agent trained from scratch. Both agents employ the off-policy Maximum A Posteriori Policy Optimization algorithm with an actor-critic architecture. By implementing on a simulated commercial vehicle in various car-following scenarios, we find that the RPL agent quickly learns significantly improved policies compared to a baseline source policy but in some measures not as good as those eventually possible with the RL agent trained from scratch.

本文介绍一种主动驾驶辅助方法，使用剩余策略学习代理人来提供剩余操作以平衡燃料消耗和其他驾驶员适应性目标。通过实施在各种车辆尾随情境下的模拟商用车上，我们发现与基线源策略相比，剩余策略学习代理人很快学习到了显着改进的策略，但在某些方面不如从头开始训练的强化学习代理人所能达到的最终结果。

动力总成控制的残差策略学习