Reinforcement learning is considered to be a strong AI paradigm which can be
used to teach machines through interaction with the environment and learning
from their mistakes, but it has not yet been successfully used for automotive
applications. There has recently been a revival of interest in the topic,
however, driven by the ability of deep learning algorithms to learn good
representations of the environment. Motivated by Google DeepMind's successful
demonstrations of learning for games from Breakout to Go, we will propose
different methods for autonomous driving using deep reinforcement learning.
This is of particular interest as it is difficult to pose autonomous driving as
a supervised learning problem as it has a strong interaction with the
environment including other vehicles, pedestrians and roadworks. As this is a
relatively new area of research for autonomous driving, we will formulate two
main categories of algorithms: 1) Discrete actions category, and 2) Continuous
actions category. For the discrete actions category, we will deal with Deep
Q-Network Algorithm (DQN) while for the continuous actions category, we will
deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to
that, We will also discover the performance of these two categories on an open
source car simulator for Racing called (TORCS) which stands for The Open Racing
car Simulator. Our simulation results demonstrate learning of autonomous
maneuvering in a scenario of complex road curvatures and simple interaction
with other vehicles. Finally, we explain the effect of some restricted
conditions, put on the car during the learning phase, on the convergence time
for finishing its learning phase.

本文提出了不同的深度强化学习方法用于自动驾驶，分别包括离散行动类别中的深度 Q 网络算法 (DQN) 和连续行动类别中的深度确定性演员 - 评论家算法 (DDAC)，并在 TORCS 模拟器中测试了其性能。