Understanding Backplay

In late 2018, Cinjon Resnick released an innovative paper, titled Backplay: Man muss immer umkehren, (https://arxiv.org/abs/1807.06919) that introduced a refined form of Curriculum Learning called Backplay. The basic premise is that you start the agent more or less at the goal, and then progressively move the agent back during training. This method may not work for all situations, but we will use this method with Curriculum Training to see how we can improve the VisualHallway example in the following exercise:

1. Open the VisualHallway scene from the Assets | ML-Agents | Examples | Hallway | Scenes folder.
2. Make sure the scene is reset to the default starting point. If you need to, pull down the source from ML-Agents again.
3. Set the scene for learning using the VisualHallwayLearning brain, and make sure that the agent is just using the default visual observations...