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The document outlines the Learning Mechanism using Deep Q-Networks (DQN) for optimizing traffic light timings in an intelligent traffic system. It describes the components involved, including the AI agent, the simulated environment, and the experience memory, which collectively enable the AI to learn through trial and error. The training process involves simulating traffic scenarios, allowing the AI to make decisions, receive feedback, and improve its strategies over time.

Author

sharmasoham1427

Language

EN