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 PyTorch. In this page we provide a brief overview of PyTorch and TensorBoard that we leverage within the ML-Agents Toolkit.
PyTorch 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 model (.onnx) file that you can then associate with an Agent. Unless you implement a new algorithm, the use of PyTorch is mostly abstracted away and behind the scenes.
One component of training models with PyTorch 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 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 the Training ML-Agents page. If you are unfamiliar with TensorBoard we recommend our guide on using TensorBoard with ML-Agents or this tutorial.