deeptraffic

Why we included this project

DeepTraffic turns a classic reinforcement-learning problem into something you can actually watch: your neural network has to drive a car as fast as possible through dense highway traffic, competing against other submissions on a public leaderboard. It comes from the MIT Deep Learning series and runs entirely in the browser, so you edit a network definition in JavaScript, run the simulation, and see your agent merge and dodge lanes as it learns. For people new to deep RL, that immediate visual feedback makes concepts like reward shaping and hyperparameter search much easier to grasp than reading theory alone. It also works well in courses or study groups, since a working example network is included to start from. The repository is more a learning and experimentation platform than a production library, but for anyone exploring autonomous driving and multi-agent reinforcement learning, it is a good place to get hands-on.