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Background: TensorFlow
As discussed in our machine learning background page, many of the algorithms we provide in the ML-Agents toolkit leverage some form of deep learning. More specifically, our implementations are built on top of the open-source library TensorFlow. This means that the models produced by the ML-Agents toolkit are (currently) in a format only understood by TensorFlow. In this page we provide a brief overview of TensorFlow, in addition to TensorFlow-related tools that we leverage within the ML-Agents toolkit.
TensorFlow
TensorFlow is an open source library for performing computations using data flow graphs, the underlying representation of deep learning models. It facilitates training and inference on CPUs and GPUs in a desktop, server, or mobile device. Within the ML-Agents toolkit, when you train the behavior of an agent, the output is a TensorFlow model (.nn) file that you can then embed within a Learning Brain. Unless you implement a new algorithm, the use of TensorFlow is mostly abstracted away and behind the scenes.
TensorBoard
One component of training models with TensorFlow is setting the values of certain model attributes (called hyperparameters). Finding the right values of these hyperparameters can require a few iterations. Consequently, we leverage a visualization tool within TensorFlow called TensorBoard. It allows the visualization of certain agent attributes (e.g. reward) throughout training which can be helpful in both building intuitions for the different hyperparameters and setting the optimal values for your Unity environment. We provide more details on setting the hyperparameters in later parts of the documentation, but, in the meantime, if you are unfamiliar with TensorBoard we recommend our guide on using Tensorboard with ML-Agents or this tutorial.