One of the brilliant aspects of the ML-Agents platform is the ability to switch from player control to AI/agent control very quickly and seamlessly. In order to do this, Unity uses the concept of a brain. A brain may be either player-controlled, a player brain, or agent-controlled, a learning brain. The brilliant part is that you can build a game and test it, as a player can then turn the game loose on an RL agent. This has the added benefit of making any game written in Unity controllable by an AI with very little effort. In fact, this is such a powerful workflow that we will spend an entire chapter, Chapter 12, Debugging/Testing a Game with DRL, on testing and debugging your games with RL.
Training an RL agent with Unity is fairly straightforward to set up and run. Unity uses Python externally to build the learning brain model. Using Python makes far...
