Monitoring training with TensorBoard

Training an agent with RL, or any DL model for that matter, while enjoyable, is not often a simple task and requires some attention to detail. Fortunately, TensorFlow ships with a set of graph tools called TensorBoard we can use to monitor training progress. Follow these steps to run TensorBoard:

1. Open an Anaconda or Python window. Activate the ml-agents virtual environment. Don't shut down the window running the trainer; we need to keep that going.
2. Navigate to the ML-Agents/ml-agents folder and run the following command:

tensorboard --logdir=summaries

3. This will run TensorBoard with its own built-in web server. You can load the page by using the URL that is shown after you run the previous command.
4. Enter the URL for TensorBoard as shown in the window, or use localhost:6006 or machinename:6006 in your browser. After an hour or so, you...