Abstract: There is provided a system and method for traffic signal control of a traffic network with a learned model. The method including: receiving sensor readings from the traffic network, the sensor readings including positions and speeds of vehicles approaching each intersection; using a learned dynamics model that takes the sensor readings as input, predicting a plurality of possibilities for position and velocity of the vehicles approaching each intersection in a future timestep; determining a action for the one or more intersections by performing a tree search on the plurality of possibilities and selecting the possibility with a highest action value; and outputting the action to the traffic network for implementation as a traffic control action at the one or more intersections.

Abstract: There is provided a system and method for traffic signal control of a traffic network with a learned model. The method including: receiving sensor readings from the traffic network, the sensor readings including positions and speeds of vehicles approaching each intersection; using a learned dynamics model that takes the sensor readings as input, predicting a plurality of possibilities for position and velocity of the vehicles approaching each intersection in a future timestep; determining a action for the one or more intersections by performing a tree search on the plurality of possibilities and selecting the possibility with a highest action value; and outputting the action to the traffic network for implementation as a traffic control action at the one or more intersections.

Abstract: A method for scoring training data samples according to an ability to preserve latent decision boundaries for previously observed classes while promoting learning from an input batch of new images from an online data stream, comprising: receiving the input batch of the new images from the online data stream, performing a memory retrieval process that retrieves data to be learned along with a new set of data from the memory to retain the previously learned knowledge, and performing a memory update process that selects and exchanges a small set of data to be saved in the memory in the memory update process. In addition, the method performs data valuation based on KNN-SV for both the memory retrieval and memory update processes to perform strategic and intuitive data selection based on the properties of KNN-SV.