[2002.00444] Deep Reinforcement Learning for Autonomous Driving: A Surveycontact arXivarXiv Twitter

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions in RL and imitation learning.

2 mentions: @hardmaru@helioRocha_
Date: 2020/02/10 12:54

Referring Tweets

@hardmaru Deep Reinforcement Learning for Autonomous Driving: A Survey Summary of Deep RL algorithms and provides a taxonomy of automated driving tasks where Deep RL methods have been used. Highlights key challenges for real world deployment and role of simulators. t.co/Z4uiOXVb0i t.co/X2iuBd5GGg

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