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Determination and optimization of reinforcement learning parameters for driver actions in traffic

An agent-based, artificial intelligence technique known as reinforcement learning has been used to capture vehicle behavior and simulate driver's actions in traffic, especially during emergency situations. This paper discusses the training parameters and their influence on agent simulation performance. A type of agent training technique called Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) is used to test the training parameters with an objective of improving simulation performance. A systematic parameter determination and optimization methodology is provided.