ml-agents/docs/Training-Behavioral-Cloning.md

Training with Behavioral Cloning

There are a variety of possible imitation learning algorithms which can be used, the simplest one of them is Behavioral Cloning. It works by collecting demonstrations from a teacher, and then simply uses them to directly learn a policy, in the same way the supervised learning for image classification or other traditional Machine Learning tasks work.

Offline Training

With offline behavioral cloning, we can use demonstrations (.demo files) generated using the Demonstration Recorder as the dataset used to train a behavior.

1. Choose an agent you would like to learn to imitate some set of demonstrations.
2. Record a set of demonstration using the Demonstration Recorder (see here). For illustrative purposes we will refer to this file as AgentRecording.demo.
3. Build the scene, assigning the agent a Learning Brain, and dragging it in the Broadcast Hub. For more information on Brains, see here.
4. Open the config/offline_bc_config.yaml file.
5. Modify the demo_path parameter in the file to reference the path to the demonstration file recorded in step 2. In our case this is: ./UnitySDK/Assets/Demonstrations/AgentRecording.demo
6. Launch mlagent-learn, providing ./config/offline_bc_config.yaml as the config parameter, and include the --run-id and --train as usual. Provide your environment as the --env parameter if it has been compiled as standalone, or omit to train in the editor.
7. (Optional) Observe training performance using TensorBoard.

This will use the demonstration file to train a neural network driven agent to directly imitate the actions provided in the demonstration. The environment will launch and be used for evaluating the agent's performance during training.