# Deep Reinforcement Learning with Mixed Convolutional Network

arXiv: https://arxiv.org/abs/2010.00717 · HTML: https://mlsanity.com/paper/2010.00717/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem](https://mlsanity.com/paper/2109.04155/) (2021)

## Abstract (arXiv)

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.