# Frequently Asked Questions

## Installation problems

### Tensorflow dependency

ML Agents requires TensorFlow; if you don't already have it installed, `pip`
will try to install it when you install the ml-agents package.

If you see a message like this

```console
ERROR: Could not find a version that satisfies the requirement tensorflow<2.0,>=1.7 (from mlagents) (from versions: none)
ERROR: No matching distribution found for tensorflow<2.0,>=1.7 (from mlagents)
```

it means that there is no version of TensorFlow for your python environment.
Some known potential causes are:

- You're using 32-bit python instead of 64-bit. See the answer
  [here](https://stackoverflow.com/a/1405971/224264) for how to tell which you
  have installed.
- You have the `tensorflow-gpu` package installed. This is equivalent to
  `tensorflow`, however `pip` doesn't recognize this. The best way to resolve
  this is to update to `tensorflow==1.15.0` which provides GPU support in the
  same package (see the
  [release notes](https://github.com/tensorflow/tensorflow/issues/33374) for
  more details.)
- You're on another architecture (e.g. ARM) which requires vendor provided
  packages.

In all of these cases, the issue is a pip/python environment setup issue. Please
search the tensorflow github issues for similar problems and solutions before
creating a new issue.

#### Visual C++ Dependency (Windows Users)
When running `mlagents-learn`, if you see a stack trace with a message like this:

```console
ImportError: DLL load failed: The specified module could not be found.
```

then either of the required DLLs, `msvcp140.dll` (old) or `msvcp140_1.dll` (new), are missing on your machine. The `import tensorflow` command will print this warning message.

To solve it, download and install (with a reboot) the install [Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019](https://support.microsoft.com/en-my/help/2977003/the-latest-supported-visual-c-downloads).

For more details, please see the [TensorFlow 2.1.0 release notes](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
and the [TensorFlow github issue](https://github.com/tensorflow/tensorflow/issues/22794#issuecomment-573297027).

## 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:

```sh
chmod -R 755 *.app
```

or on Linux:

```sh
chmod -R 755 *.x86_64
```

On Windows, you can find
[instructions](<https://technet.microsoft.com/en-us/library/cc754344(v=ws.11).aspx>).

## Environment Connection Timeout

If you are able to launch the environment from `UnityEnvironment` but then
receive a timeout error like this:

```
UnityAgentsException: The Communicator was unable to connect. Please make sure the External process is ready to accept communication with Unity.
```

There may be a number of possible causes:

- _Cause_: There may be no agent in the scene
- _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](https://support.apple.com/en-us/HT201642).
- _Cause_: An error happened in the Unity Environment preventing communication.
  _Solution_: Look into the
  [log files](https://docs.unity3d.com/Manual/LogFiles.html) generated by the
  Unity Environment to figure what error happened.
- _Cause_: You have assigned `HTTP_PROXY` and `HTTPS_PROXY` values in your
  environment variables. _Solution_: Remove these values and try again.
- _Cause_: You are running in a headless environment (e.g. remotely connected
  to a server). _Solution_: Pass `--no-graphics` to `mlagents-learn`, or
  `no_graphics=True` to `RemoteRegistryEntry.make()` or the `UnityEnvironment`
  initializer. If you need graphics for visual observations, you will need to
  set up `xvfb` (or equivalent).

## 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

```python
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 the 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.

## "File name" cannot be opened because the developer cannot be verified.

If you have downloaded the repository using the github website on macOS 10.15 (Catalina)
or later, you may see this error when attempting to play scenes in the Unity project.
Workarounds include installing the package using the Unity Package Manager (this is
the officially supported approach - see [here](Installation.md)), or following the
instructions [here](https://support.apple.com/en-us/HT202491) to verify the relevant
files on your machine on a file-by-file basis.