论文标题
从基于视力的模仿学习中大概相反的增强学习
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning
论文作者
论文摘要
在这项工作中，我们提出了一种用于获得基于视觉导航的隐式目标函数的方法。所提出的方法依赖于模仿学习，模型预测控制（MPC）以及深度神经网络中使用的解释技术。我们使用模仿学习作为进行反向加固学习的一种手段，以创建一个近似的成本函数生成器，以进行视觉导航挑战。最终的成本函数（COSTMAP）与MPC一起用于实时控制，并且在新型环境中优于其他最先进的Costmap发电机。提出的过程允许简单的培训和鲁棒性来取样数据。我们将我们的方法应用于在多个真实和模拟环境中基于视觉自主驾驶的任务，并显示其概括性。
In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and an interpretation technique used in Deep Neural Networks. We use Imitation Learning as a means to do Inverse Reinforcement Learning in order to create an approximate cost function generator for a visual navigation challenge. The resulting cost function, the costmap, is used in conjunction with MPC for real-time control and outperforms other state-of-the-art costmap generators in novel environments. The proposed process allows for simple training and robustness to out-of-sample data. We apply our method to the task of vision-based autonomous driving in multiple real and simulated environments and show its generalizability.
