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

• URL: http://arxiv.org/abs/2009.11212v1
• Date: Wed, 23 Sep 2020 15:23:54 GMT
• Title: Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
• Authors: P\'eter Alm\'asi, R\'obert Moni, B\'alint Gyires-T\'oth
• Abstract summary: 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 trained agent is able to run on limited hardware resources and its performance is comparable to state-of-the-art approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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