Optimising Traffic Using Reinforcement Learning – Becoming Human: Artificial Intelligence Magazine

Fundamentally, the root of the urban traffic distribution problem is in multi-criteria decision making. The Reinforcement Learning framework, in which an agent learns from a model with optimal policy based on its environment, could provide an advantageous method for algorithmic development and network improvement. Each action that the agent would take will lead to a reward or punishment with the new observation of the state. Through its learning progress, the agent will learn a distributed routing policy that could maximise the capacity of an urban transport network. This process could be treated as a Markov Decision Process (MDP), which ultimately aims for the best solution by optimising specific policy step by step.