Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World [article]

Péter Almási, Róbert Moni, Bálint Gyires-Tóth
2020 arXiv   pre-print
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
more » ... apable 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.
arXiv:2009.11212v1 fatcat:dkbjmr4fujc5lapxngrhua6cny