deeptraffic and deep_traffic
The first is the official MIT competition platform while the second is a third-party submission that achieved top-ranking results within that competition, making them ecosystem siblings where one provides the framework and the other demonstrates a high-performing solution.
About deeptraffic
lexfridman/deeptraffic
DeepTraffic is a deep reinforcement learning competition, part of the MIT Deep Learning series.
Competitors design neural network architectures and tune hyperparameters (layer count, learning rate, discount factor) via an in-browser IDE to train DQN agents that navigate a 7-lane highway simulation using occupancy grid inputs. The platform provides real-time visualization of network activations and reward signals, enabling crowdsourced exploration of motion planning hyperparameter spaces for multi-agent autonomous driving scenarios where each vehicle independently executes the same learned policy.
About deep_traffic
gsurma/deep_traffic
MIT DeepTraffic top 2% solution (75.01 mph) 🚗.
Scores updated daily from GitHub, PyPI, and npm data. How scores work