Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
This addresses the problem of simulation-to-reality transfer for autonomous driving agents, though it is incremental as it builds on existing DRL methods.
The paper tackles the challenge of transferring reinforcement learning agents from simulation to real-world autonomous driving by presenting a DRL-based algorithm using Deep Q-Networks, achieving performance comparable to state-of-the-art approaches in the Duckietown environment.
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.