Curriculum Learning allows for an agent to progressively learn a difficult task by stepping up the reward function. While the reward remains absolute, the agent finds or achieves the goal in a simpler manner, and so learns the purpose of the reward. Then, as the training progresses and as the agent learns, the difficulty of receiving a reward increases, which, in turn, forces the agent to learn.
Unity, of course, has a few samples of this, and we will look at the WallJump example of how a Curriculum Learning sample is set up in the following exercise:
- Open the WallJump scene from the Assets | ML-Agents | Examples | WallJump | Scenes folder.
- Select the Academy object in the Hierarchy window.
- Click both Control options on Wall Jump Academy | Brains | Control parameter as shown in the following excerpt:
Setting the multiple brains to learning
- This sample...
