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Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
[article]
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
arXiv:2009.11212v1
fatcat:dkbjmr4fujc5lapxngrhua6cny