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.