Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目，它使游戏和模拟能够作为训练智能代理的环境。

unity3d unity unity-tech reinforcement-le deep-learning deep-reinforcement-learning neural-networks

14 KiB

原始文件 文件历史

Basic Guide

This guide will show you how to use a pre-trained model in an example Unity environment (3D Ball) and show you how to train the model yourself.

If you are not familiar with the Unity Engine, we highly recommend the Roll-a-ball tutorial to learn all the basic concepts of Unity.

Setting up the ML-Agents Toolkit within Unity

In order to use the ML-Agents toolkit within Unity, you first need to change a few Unity settings.

Launch Unity
On the Projects dialog, choose the Open option at the top of the window.
Using the file dialog that opens, locate the UnitySDK folder within the the ML-Agents toolkit project and click Open.
Go to Edit > Project Settings > Player
For each of the platforms you target (PC, Mac and Linux Standalone, iOS or Android):
1. Expand the Other Settings section.
2. Select Scripting Runtime Version to Experimental (.NET 4.6 Equivalent or .NET 4.x Equivalent)
Go to File > Save Project

Setting up TensorFlowSharp

We provide pre-trained models (.bytes files) for all the agents in all our demo environments. To be able to run those models, you'll first need to set-up TensorFlowSharp support. Consequently, you need to install the TensorFlowSharp plugin to be able to run these models within the Unity Editor.

Download the TensorFlowSharp Plugin
Import it into Unity by double clicking the downloaded file. You can check if it was successfully imported by checking the TensorFlow files in the Project window under Assets > ML-Agents > Plugins > Computer.
Go to Edit > Project Settings > Player and add ENABLE_TENSORFLOW to the Scripting Define Symbols for each type of device you want to use (PC, Mac and Linux Standalone, iOS or Android).

Note: If you don't see anything under Assets, drag the UnitySDK/Assets/ML-Agents folder under Assets within Project window.

Running a Pre-trained Model

We've included pre-trained models for the 3D Ball example.

In the Project window, go to Assets/ML-Agents/Examples/3DBall/Scenes folder and open the 3DBall scene file.
In the Project window, go to Assets/ML-Agents/Examples/3DBall/Prefabs folder. Expand Game and click on the Platform prefab. You should see the Platform prefab in the Inspector windows.

Note: The platforms in the 3DBall scene were created using the Platform prefab. Instead of updating all 12 platforms individually, you can update the Platform prefab instead.
In the Project window, drag the 3DBallLearning Brain located in Assets/ML-Agents/Examples/3DBall/Brains into the Brain property under Ball 3D Agent (Script) component in the Inspector window.
Make sure that all of the Agents in the Scene now have 3DBallLearning as Brain. Note : You can modify multiple game objects in a scene by selecting them all at once using the search bar in the Scene Hierarchy.
In the Project window, locate the Assets/ML-Agents/Examples/3DBall/TFModels folder.
Drag the 3DBallLearning model file from the Assets/ML-Agents/Examples/3DBall/TFModels folder to the Model field of the 3DBallLearning Brain.
Click the Play button and you will see the platforms balance the balls using the pretrained model.

Using the Basics Jupyter Notebook

The notebooks/getting-started.ipynb Jupyter notebook contains a simple walkthrough of the functionality of the Python API. It can also serve as a simple test that your environment is configured correctly. Within Basics, be sure to set env_name to the name of the Unity executable if you want to use an executable or to None if you want to interact with the current scene in the Unity Editor.

More information and documentation is provided in the Python API page.

Training the Brain with Reinforcement Learning

Adding a Brain to the training session

To set up the environment for training, you will need to specify which agents are contributing to the training and which Brain is being trained. You can only perform training with a Learning Brain.

From the Hierarchy panel, select Ball3DAcademy. the 3DBallLearning Brain to the agents you would like to train.
Note: You can assign the same Brain to multiple agents at once : To do so, you can use the prefab system. When an agent is created from a prefab, modifying the prefab will modify the agent as well. If the agent does not synchronize with the prefab, you can hit the Revert button on top of the Inspector. Alternatively, you can select multiple agents in the scene and modify their Brain property all at once.
Select the Ball3DAcademy GameObject and make sure the 3DBallLearning Brain is in the Broadcast Hub. In order to train, you need to toggle the Control checkbox.

Note: Assigning a Brain to an agent (dragging a Brain into the Brain property of the agent) means that the Brain will be making decision for that agent. Whereas dragging a Brain into the Broadcast Hub means that the Brain will be exposed to the Python process. The Control checkbox means that in addition to being exposed to Python, the Brain will be controlled by the Python process (required for training).

Training the environment

Open a command or terminal window.
Navigate to the folder where you cloned the ML-Agents toolkit repository. Note: If you followed the default installation, then you should be able to run mlagents-learn from any directory.
Run mlagents-learn <trainer-config-path> --run-id=<run-identifier> --train where:
- <trainer-config-path> is the relative or absolute filepath of the trainer configuration. The defaults used by example environments included in MLAgentsSDK can be found in config/trainer_config.yaml.
- <run-identifier> is a string used to separate the results of different training runs
- --train tells mlagents-learn to run a training session (rather than inference)

If you cloned the ML-Agents repo, then you can simply run

mlagents-learn config/trainer_config.yaml --run-id=firstRun --train

When the message "Start training by pressing the Play button in the Unity Editor" is displayed on the screen, you can press the ▶️ button in Unity to start training in the Editor.

Note: Alternatively, you can use an executable rather than the Editor to perform training. Please refer to this page for instructions on how to build and use an executable.

ml-agents$ mlagents-learn config/trainer_config.yaml --run-id=first-run --train


                        ▄▄▄▓▓▓▓
                   ╓▓▓▓▓▓▓█▓▓▓▓▓
              ,▄▄▄m▀▀▀'  ,▓▓▓▀▓▓▄                           ▓▓▓  ▓▓▌
            ▄▓▓▓▀'      ▄▓▓▀  ▓▓▓      ▄▄     ▄▄ ,▄▄ ▄▄▄▄   ,▄▄ ▄▓▓▌▄ ▄▄▄    ,▄▄
          ▄▓▓▓▀        ▄▓▓▀   ▐▓▓▌     ▓▓▌   ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌  ╒▓▓▌
        ▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓      ▓▀      ▓▓▌   ▐▓▓ ▐▓▓    ▓▓▓ ▓▓▓  ▓▓▌   ▐▓▓▄ ▓▓▌
        ▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄     ▓▓      ▓▓▌   ▐▓▓ ▐▓▓    ▓▓▓ ▓▓▓  ▓▓▌    ▐▓▓▐▓▓
          ^█▓▓▓        ▀▓▓▄   ▐▓▓▌     ▓▓▓▓▄▓▓▓▓ ▐▓▓    ▓▓▓ ▓▓▓  ▓▓▓▄    ▓▓▓▓`
            '▀▓▓▓▄      ^▓▓▓  ▓▓▓       └▀▀▀▀ ▀▀ ^▀▀    `▀▀ `▀▀   '▀▀    ▐▓▓▌
               ▀▀▀▀▓▄▄▄   ▓▓▓▓▓▓,                                      ▓▓▓▓▀
                   `▀█▓▓▓▓▓▓▓▓▓▌
                        ¬`▀▀▀█▓


INFO:mlagents.learn:{'--curriculum': 'None',
 '--docker-target-name': 'Empty',
 '--env': 'None',
 '--help': False,
 '--keep-checkpoints': '5',
 '--lesson': '0',
 '--load': False,
 '--no-graphics': False,
 '--num-runs': '1',
 '--run-id': 'first-run',
 '--save-freq': '50000',
 '--seed': '-1',
 '--slow': False,
 '--train': True,
 '--worker-id': '0',
 '<trainer-config-path>': 'config/trainer_config.yaml'}
INFO:mlagents.envs:Start training by pressing the Play button in the Unity Editor.

Note: If you're using Anaconda, don't forget to activate the ml-agents environment first.

If mlagents-learn runs correctly and starts training, you should see something like this:

INFO:mlagents.envs:
'Ball3DAcademy' started successfully!
Unity Academy name: Ball3DAcademy
        Number of Brains: 1
        Number of Training Brains : 1
        Reset Parameters :

Unity brain name: 3DBallLearning
        Number of Visual Observations (per agent): 0
        Vector Observation space size (per agent): 8
        Number of stacked Vector Observation: 1
        Vector Action space type: continuous
        Vector Action space size (per agent): [2]
        Vector Action descriptions: ,
INFO:mlagents.envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning:
        batch_size:          64
        beta:                0.001
        buffer_size:         12000
        epsilon:             0.2
        gamma:               0.995
        hidden_units:        128
        lambd:               0.99
        learning_rate:       0.0003
        max_steps:           5.0e4
        normalize:           True
        num_epoch:           3
        num_layers:          2
        time_horizon:        1000
        sequence_length:     64
        summary_freq:        1000
        use_recurrent:       False
        summary_path:        ./summaries/first-run-0
        memory_size:         256
        use_curiosity:       False
        curiosity_strength:  0.01
        curiosity_enc_size:  128
        model_path:	./models/first-run-0/3DBallLearning
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.

After training

You can press Ctrl+C to stop the training, and your trained model will be at models/<run-identifier>/<brain_name>.bytes where <brain_name> is the name of the Brain corresponding to the model. (Note: There is a known bug on Windows that causes the saving of the model to fail when you early terminate the training, it's recommended to wait until Step has reached the max_steps parameter you set in trainer_config.yaml.) This file corresponds to your model's latest checkpoint. You can now embed this trained model into your Learning Brain by following the steps below, which is similar to the steps described above.

Move your model file into UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/.
Open the Unity Editor, and select the 3DBall scene as described above.
Select the 3DBallLearning Learning Brain from the Scene hierarchy.
Drag the <brain_name>.bytes file from the Project window of the Editor to the Model placeholder in the 3DBallLearning inspector window.
Press the ▶️ button at the top of the Editor.

Next Steps

For more information on the ML-Agents toolkit, in addition to helpful background, check out the ML-Agents Toolkit Overview page.
For a more detailed walk-through of our 3D Balance Ball environment, check out the Getting Started page.
For a "Hello World" introduction to creating your own Learning Environment, check out the Making a New Learning Environment page.
For a series of Youtube video tutorials, checkout the Machine Learning Agents PlayList page.

14 KiB 原始文件 文件历史