It is often more intuitive to simply demonstrate the behavior we want an agent to perform, rather than attempting to have it learn via trial-and-error methods. Consider our [running example](ML-Agents-Overview.md#running-example-training-npc-behaviors) of training a medic NPC : instead of indirectly training a medic with the help of a reward function, we can give the medic real world examples of observations from the game and actions from a game controller to guide the medic's behavior. More specifically, in this mode, the Brain type during training is set to Player and all the actions performed with the controller (in addition to the agent observations) will be recorded and sent to the Python API. The imitation learning algorithm will then use these pairs of observations and actions from the human player to learn a policy.
It is often more intuitive to simply demonstrate the behavior we want an agent to perform, rather than attempting to have it learn via trial-and-error methods. Consider our [running example](ML-Agents-Overview.md#running-example-training-npc-behaviors) of training a medic NPC : instead of indirectly training a medic with the help of a reward function, we can give the medic real world examples of observations from the game and actions from a game controller to guide the medic's behavior. More specifically, in this mode, the Brain type during training is set to Player and all the actions performed with the controller (in addition to the agent observations) will be recorded and sent to the Python API. The imitation learning algorithm will then use these pairs of observations and actions from the human player to learn a policy. [Video Link](https://youtu.be/kpb8ZkMBFYs).
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