论文标题
与混合卷积网络的深度加固学习
Deep Reinforcement Learning with Mixed Convolutional Network
论文作者
论文摘要
最近的研究表明，直接从单个前置摄像头到转向命令的地图原始像素非常强大。本文提出了一个卷积神经网络（CNN），以使用Openai Gym中的模仿学习来播放Carracing-V0。数据集是通过在健身房手动玩游戏并使用数据增强方法来生成的，将数据集扩展到比以前大4倍。此外，我们为每个图像读取真实速度，四个ABS传感器，方向盘位置和陀螺仪，并通过组合传感器输入和图像输入来设计混合模型。训练后，该模型可以自动检测道路特征的边界，并像人类一样驱动机器人。通过使用Carracing-V0中的平均奖励与Alexnet和VGG16进行比较，我们的模型将赢得最大的整体系统性能。
Recent research has shown that map raw pixels from a single front-facing camera directly to steering commands are surprisingly powerful. This paper presents a convolutional neural network (CNN) to playing the CarRacing-v0 using imitation learning in OpenAI Gym. The dataset is generated by playing the game manually in Gym and used a data augmentation method to expand the dataset to 4 times larger than before. Also, we read the true speed, four ABS sensors, steering wheel position, and gyroscope for each image and designed a mixed model by combining the sensor input and image input. After training, this model can automatically detect the boundaries of road features and drive the robot like a human. By comparing with AlexNet and VGG16 using the average reward in CarRacing-v0, our model wins the maximum overall system performance.
