Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine

Patrick Klose; Rudolf Mester

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arXiv · 1811.07868

Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine

Patrick Klose·Rudolf Mester

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Abstract

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-Network algorithm.

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Patrick Klose, Rudolf Mester. 2018-11-19. Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine. https://arxiv.org/abs/1811.07868

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