As we learned in Chapter 9, Rewards and Reinforcement Learning, intrinsic reward systems and the concept of agent motivation is currently implemented as just curiosity learning in ML-Agents. This whole area of applying intrinsic rewards or motivation combined with RL has wide applications to gaming and interpersonal applications such as servant agents.
In the next exercise, we are going to add intrinsic rewards to a couple of our agents and see what effect this has on the game. Open up the scene from the previous exercise and follow these steps:
- Open up the ML-Agents/ml-agents/config/trainer_config.yaml file in a text editor. We never did add any specialized configuration to our agents, but we are going to rectify that now and add some extra configurations.
- Add the following four new brain configurations to the file:
BlueStrikerLearning...
