Self-Autonomous Car Simulation Using Deep Q-Learning Algorithm

Engineering notes

Key topics: self-driving car, self-driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This paper has proposed a car game using the algorithm Deep Q-Learning for simulation of self-driving autonomous car. It creates an environment where the self-driving car moves in left right up down direction starting from source to destination. Q-Learning is a RL algorithm which is an of f-policy algorithm that is completely different from a Deep Q-Learning as it replaces a normal Q table with neural network. It maps neural network with input states as their actions and Q values. This Game is reward based where agent gets the reward when it reaches the destination and does not hit any obstacles else it gets penalty which is then kept as an experience for future plays.

Links and sources

• Official / arXiv page

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