Teaching machines to direct traffic through deep reinforcement learning

Rush hour. The dreaded time of day when traffic conditions seem bent on making you late. As your car slowly creeps in line behind countless others stuck at a stop light, you think to yourself, “Why aren’t these lights changing faster?” Traffic control scientists have long tried to solve this signaling problem. Unfortunately, the complexity of traffic situations has made the job extremely hard. A recent study suggests that machines can learn how to plan traffic signals just right to reduce wait times and make traffic queues shorter. Automating traffic control is notoriously tricky because it involves two challenging tasks: modeling traffic flow and then optimizing it. While traditional artificial intelligence and simulation techniques can learn and reproduce the dynamics of certain traffic situations, they cannot easily determine the best…


Link to Full Article: Teaching machines to direct traffic through deep reinforcement learning

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