Proceedings of the 2020 the 3rd International Conference on Information Science and System 2020
DOI: 10.1145/3388176.3388199
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Road Detection for Reinforcement Learning Based Autonomous Car

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Cited by 9 publications
(8 citation statements)
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“…Training a self-driving car to stay on the road with Proximal Policy Optimization (PPO) has been demonstrated by Holen et al [8]. However, the transfer learning was from 2D simulation to 3D (and vise versa), while in this paper it is demonstrated for 3D simulation to a real vehicle.…”
Section: Related Workmentioning
confidence: 84%
“…Training a self-driving car to stay on the road with Proximal Policy Optimization (PPO) has been demonstrated by Holen et al [8]. However, the transfer learning was from 2D simulation to 3D (and vise versa), while in this paper it is demonstrated for 3D simulation to a real vehicle.…”
Section: Related Workmentioning
confidence: 84%
“…Therefore, it has become a trend to introduce deep learning structures such as convolutional and recurrent neural networks in reinforcement learning. Deep Q-learning takes r + γmax a q(s , a ) as the target Q-value, and defines the loss function between the network output Q value and the target Q-value function shown in Equation (4).…”
Section: Deep Q-networkmentioning
confidence: 99%
“…With the continuous optimization of reinforcement learning theory and algorithms, the application of reinforcement learning algorithms for path planning has gradually become a research hotspot for solving path planning problems. Reinforcement learning does not require prior knowledge of complex environment models, which helps achieve a high level of human intelligence and becomes an attractive approach for path planning [2,3], unmanned driving [4], video games [5], robot control [6] and USV path planning [7]. Although traditional Q-learning algorithms [8] have better results for path planning, they still have slow convergence speed and cannot solve the real-world problems of large scale and high complexity [9].…”
Section: Introductionmentioning
confidence: 99%
“…The trained policy is transferred to realworld testing experiments for analyzing the perception and control perspective of the AC. Another group of researchers in [11] uses PPO for proposing a road detecting algorithm in an urban driving environment. They carry out experiments in a Udacity racing game simulator [12] as well as in a small OpenAI gym carracing-v0 environment [13].…”
Section: B Deep Reinforcement Learning Algorithms For Autonomous Drivingmentioning
confidence: 99%