Advanced Micro Devices, Inc.
ARCHITECTURE FOR DEEP Q LEARNING

Abstract:

The deep Q learning technique trains weights of an artificial neural network using a number of unique features, including separate target and prediction networks, random experience replay to avoid issues with temporally correlated training samples, and others. A hardware architecture is described that is tuned to perform deep Q learning. Inference cores use a prediction network to determine an action to apply to an environment. A replay memory stores the results of the action. Training cores use a loss function derived from outputs from both the target and prediction networks to update weights of the prediction neural networks. A high speed copy engine periodically copies weights from the prediction neural network to the target neural network.