Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine
This addresses the problem of autonomous driving in complex settings for researchers and developers, though it appears incremental as it builds on existing reinforcement learning methods.
The paper tackled the challenge of learning to drive in realistic environments by adaptively restricting the agent's action space based on the driving situation, resulting in swift learning using the Deep Q-Network algorithm.
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.