基于深度逆强化学习的MPC时空代价图推断
Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning
摘要：
可能难以自主地产生驾驶员行为，使得其在其他交通参与者看来是自然的。通过逆向强化学习（IRL），我们可以通过从人类演示中学习底层奖励函数来自动化这个过程。我们提出了一个新的IRL算法，学习目标条件的时空奖励函数。模型预测控制器（MPC）使用所得到的成本图来执行任务，而无需对成本函数进行任何手动设计或手动调整。我们评估了我们提出的目标条件时空归零最大熵深度IRL（GSTZ）-MEDIRL框架以及CARLA模拟器中的MPC，用于在具有挑战性的密集交通高速公路场景中进行自动驾驶，车道保持和车道变换任务。我们提出的方法显示出更高的成功率相比，其他基线方法，包括行为克隆，国家的最先进的RL政策，MPC与基于学习的行为预测模型。
英文摘要：
It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this process by learning the underlying reward function from human demonstrations. We propose a new IRL algorithm that learns a goal-conditioned spatiotemporal reward function. The resulting costmap is used by Model Predictive Controllers (MPCs) to perform a task without any hand-designing or hand-tuning of the cost function. We evaluate our proposed Goal-conditioned SpatioTemporal Zeroing Maximum Entropy Deep IRL (GSTZ)-MEDIRL framework together with MPC in the CARLA simulator for autonomous driving, lane keeping, and lane changing tasks in a challenging dense traffic highway scenario. Our proposed methods show higher success rates compared to other baseline methods including behavior cloning, state-of-the-art RL policies, and MPC with a learning-based behavior prediction model.
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