RL is a very powerful algorithm, but can become very computationally complex when we start to look at massive state inputs. To account for massive states, many powerful RL algorithms use the concept of model-free or policy-based learning, something we will cover in a later chapter. As we already know, Unity uses a policy-based algorithm that allows it to learn any size of state space by generalizing to a policy. This allows us to easily input a state space of 15 vectors in the example we just ran to something more massive, as in the VisualHallway example.
Let's open up Unity to the VisualHallway example scene and look at how to reduce the visual input space in the following exercise:
- With the VisualHallway scene open, locate the HallwayLearningBrain in the Assets | ML-Agents | Examples | Hallway | Brains folder and select it.
- Modify the Brain Parameters...
