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Automated Driving Maneuvers under Interactive Environment based on Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
Safe and efficient autonomous driving maneuvers in an interactive and complex environment can be considerably challenging due to the unpredictable actions of other surrounding agents that may be cooperative or adversarial in their interactions with the ego vehicle. One of the state-of-the-art approaches is to apply Reinforcement Learning (RL) to learn a time-sequential driving policy, to execute proper control strategy or tracking trajectory in dynamic situations. However, direct application of
arXiv:1803.09200v3
fatcat:jas3fa7rkfeatpqwcnnlfjtm4e