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

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5 Author(s)
Linsen Chong ; Virginia Tech Signal Control & Oper. Res. & Educ. Syst. Lab., Virginia Tech, Blacksburg, VA, USA ; Abbas, M. ; Higgs, B. ; Medina, A.
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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.

Published in:

Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on

Date of Conference:

5-7 Oct. 2011

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