Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine.
Proceedings of the 2nd International Conference on Applications of Intelligent Systems(2019)
摘要
In the field of autonomous driving, the system controlling the vehicle can be seen as an agent acting in a complex environment and thus naturally fits into the modern framework of reinforcement learning. However, learning to drive can be a challenging task and current results are often restricted to simplified driving environments. To advance the field, we present a method to adaptively restrict the action space of the agent according to its current driving situation and show that it can be used to swiftly learn to drive in a realistic environment based on the deep Q-learning algorithm.
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关键词
artificial intelligence, autonomous driving, machine learning, reinforcement learning, state machine
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