论文标题
强大的强化学习基于学习的自主驱动剂用于模拟和现实世界
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
论文作者
论文摘要
深度加强学习（DRL）已成功地用于解决不同的挑战，例如复杂的板和电脑游戏，最近。但是，使用DRL解决现实世界的机器人技术似乎是一个更困难的挑战。所需的方法是在模拟器中训练代理商并将其转移到现实世界中。尽管如此，由于差异，在模拟器中接受训练的模型在现实环境中的性能往往很差。在本文中，我们提出了一种基于DRL的算法，该算法能够使用深Q-Networks（DQN）执行自动机器人控制。在我们的方法中，代理在模拟环境中进行了训练，并且能够在模拟和现实世界中浏览。该方法是在鸭子环境中评估的，该环境必须根据单眼摄像头输入遵循车道。受过训练的代理能够在有限的硬件资源上运行，其性能与最先进的方法相媲美。
Deep Reinforcement Learning (DRL) has been successfully used to solve different challenges, e.g. complex board and computer games, recently. However, solving real-world robotics tasks with DRL seems to be a more difficult challenge. The desired approach would be to train the agent in a simulator and transfer it to the real world. Still, models trained in a simulator tend to perform poorly in real-world environments due to the differences. In this paper, we present a DRL-based algorithm that is capable of performing autonomous robot control using Deep Q-Networks (DQN). In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment. The method is evaluated in the Duckietown environment, where the agent has to follow the lane based on a monocular camera input. The trained agent is able to run on limited hardware resources and its performance is comparable to state-of-the-art approaches.
