Exam AWS Certified Machine Learning - Specialty topic 1 question 164 discussion

answer : A, because the setting needs multi agents and is constrained with traffic light correlation.

A. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem. In this scenario, where the traffic behavior at each light is correlated, a multi-agent reinforcement learning (MARL) approach is well-suited to model the problem. In MARL, multiple agents interact with each other and the environment, and their behavior is influenced by the behavior of other agents. This approach is particularly useful in modeling traffic systems, where the behavior of each vehicle is affected by the behavior of other vehicles and traffic lights. Formulating the problem as a MARL problem can help the data scientist obtain a correlated equilibrium policy, which can optimize traffic flow across multiple traffic lights by taking into account the correlations between them. By optimizing traffic flow across all traffic lights in a correlated way, it may be possible to reduce congestion and improve overall traffic efficiency.

i am wondering how is this actually implemented, i am learning deep RL right now

It's too complex problem for supervised or unsupervised. It's a multi-agent problem.

Answer is A

https://www.researchgate.net/publication/221456376_Multi-Agent_Reinforcement_Learning_for_Simulating_Pedestrian_Navigation