Skip to content

Latest commit

 

History

History
125 lines (91 loc) · 4.88 KB

FAQ.md

File metadata and controls

125 lines (91 loc) · 4.88 KB
Raw

Frequently Asked Questions

Scripting Runtime Environment not setup correctly

If you haven't switched your scripting runtime version from .NET 3.5 to .NET 4.6 or .NET 4.x, you will see such error message:

error CS1061: Type `System.Text.StringBuilder' does not contain a definition for `Clear' and no extension method `Clear' of type `System.Text.StringBuilder' could be found. Are you missing an assembly reference?

This is because .NET 3.5 doesn't support method Clear() for StringBuilder, refer to Setting Up The ML-Agents Toolkit Within Unity for solution.

TensorFlowSharp flag not turned on

If you have already imported the TensorFlowSharp plugin, but haven't set ENABLE_TENSORFLOW flag for your scripting define symbols, you will see the following error message:

You need to install and enable the TensorFlowSharp plugin in order to use the Internal Brain.

This error message occurs because the TensorFlowSharp plugin won't be usage without the ENABLE_TENSORFLOW flag, refer to Setting Up The ML-Agents Toolkit Within Unity for solution.

Instance of CoreBrainInternal couldn't be created

If you try to use ML-Agents in Unity versions 2017.1 - 2017.3, you might encounter an error that looks like this:

Instance of CoreBrainInternal couldn't be created. The the script
class needs to derive from ScriptableObject.
UnityEngine.ScriptableObject:CreateInstance(String)

You can fix the error by removing CoreBrain from CoreBrainInternal.cs:16, clicking on your Brain Gameobject to let the scene recompile all the changed C# scripts, then adding the CoreBrain back. Make sure your brain is in Internal mode, your TensorFlowSharp plugin is imported and the ENABLE_TENSORFLOW flag is set. This fix is only valid locally and unstable.

Tensorflow epsilon placeholder error

If you have a graph placeholder set in the Internal Brain inspector that is not present in the TensorFlow graph, you will see some error like this:

UnityAgentsException: One of the TensorFlow placeholder could not be found. In brain <some_brain_name>, there are no FloatingPoint placeholder named <some_placeholder_name>.

Solution: Go to all of your Brain object, find Graph placeholders and change its size to 0 to remove the epsilon placeholder.

Similarly, if you have a graph scope set in the Internal Brain inspector that is not correctly set, you will see some error like this:

UnityAgentsException: The node <Wrong_Graph_Scope>/action could not be found. Please make sure the graphScope <Wrong_Graph_Scope>/ is correct

Solution: Make sure your Graph Scope field matches the corresponding Brain object name in your Hierarchy Inspector when there are multiple Brains.

Environment Permission Error

If you directly import your Unity environment without building it in the editor, you might need to give it additional permissions to execute it.

If you receive such a permission error on macOS, run:

chmod -R 755 *.app

or on Linux:

chmod -R 755 *.x86_64

On Windows, you can find instructions.

Environment Connection Timeout

If you are able to launch the environment from UnityEnvironment but then receive a timeout error, there may be a number of possible causes.

  • Cause: There may be no Brains in your environment which are set to External. In this case, the environment will not attempt to communicate with python. Solution: Set the Brains(s) you wish to externally control through the Python API to External from the Unity Editor, and rebuild the environment.
  • Cause: On OSX, the firewall may be preventing communication with the environment. Solution: Add the built environment binary to the list of exceptions on the firewall by following instructions.
  • Cause: An error happened in the Unity Environment preventing communication. Solution: Look into the log files generated by the Unity Environment to figure what error happened.

Communication port {} still in use

If you receive an exception "Couldn't launch new environment because communication port {} is still in use. ", you can change the worker number in the Python script when calling

UnityEnvironment(file_name=filename, worker_id=X)

Mean reward : nan

If you receive a message Mean reward : nan when attempting to train a model using PPO, this is due to the episodes of the Learning Environment not terminating. In order to address this, set Max Steps for either the Academy or Agents within the Scene Inspector to a value greater than 0. Alternatively, it is possible to manually set done conditions for episodes from within scripts for custom episode-terminating events.