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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 summarizes deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in the real-world deployment of autonomous driving agents. It also delineatesdoi:10.1109/tits.2022.3174254 fatcat:o6z3ncdkgbh5dmvvywtok7hwke