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A revised reinforcement learning algorithm to model complicated vehicle continuous actions in traffic

An agent-based multi-layer reinforcement learning (RL) framework for naturalistic driving behavior simulation in traffic is introduced. Each agent is a replication of an individual driver. Each agent is implemented by applying artificial intelligence concepts, including: fuzzy logic, neural networks, and reinforcement learning algorithms. A revised Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) is proposed to simulate vehicle actions during safety-critical events when the traffic state is complicated. The revised NFACRL algorithm can handle state dimension problems and continuous vehicle actions.