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

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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References in corpus (2)

• A Survey of Deep Learning Techniques for Autonomous Driving
• Deep Reinforcement Learning framework for Autonomous Driving

Cited by in corpus (5)

• Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies
• Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications
• Autonomous Driving Small-Scale Cars: A Survey of Recent Development
• Embedded out-of-distribution detection on an autonomous robot platform
• A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving