Latent Space Reinforcement Learning for Steering Angle Prediction

Abstract--Model-free reinforcement learning has recently been shown to successfully learn navigation policies from raw sensor data. In this work, we address the problem of learning driving policies for an autonomous agent in a high-fidelity simulator. Building upon recent research that applies deep reinforcement learning to navigation problems, we present a modular deep reinforcement learning approach to predict the steering angle of the car from raw images. The control module trained with reinforcement learning takes the latent vector as input to predict the correct steering angle. The experimental results have showed that our method is capable of learning to maneuver the car without any human control signals. I. INTRODUCTION Reinforcement learning (RL) is gaining interest as a promising avenueto training end-to-end autonomous driving policies. These algorithms have recently been shown to solve complex tasks such as navigation from raw vision-sensor modalities. However, training those algorithms require vast amounts of data and interactions with the environment to cover a wide variety of driving scenarios. The collection of such data if even possible is costly and time-consuming.