Description
Trying to set up the unity package and installing mlagents seems to have failed. After installing the mlagents, I tried mlagents-learn --help, and I don't get the Unity logo as I should.
To Reproduce
In command prompt type pip3 install mlagents
After installation try mlagents-learn --help
I'm not seeing the Unity Logo
Console logs / stack traces
2020-06-13 18:41:51.307807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
WARNING:tensorflow:From c:\users\jain-\appdata\local\programs\python\python38\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
usage: mlagents-learn.exe [-h] [--env ENV_PATH] [--resume] [--force] [--run-id RUN_ID] [--initialize-from RUN_ID] [--seed SEED] [--inference] [--base-port BASE_PORT]
[--num-envs NUM_ENVS] [--debug] [--env-args ...] [--cpu] [--width WIDTH] [--height HEIGHT] [--quality-level QUALITY_LEVEL]
[--time-scale TIME_SCALE] [--target-frame-rate TARGET_FRAME_RATE] [--capture-frame-rate CAPTURE_FRAME_RATE] [--no-graphics]
[trainer_config_path]
positional arguments:
trainer_config_path
optional arguments:
-h, --help show this help message and exit
--env ENV_PATH Path to the Unity executable to train (default: None)
--resume Whether to resume training from a checkpoint. Specify a --run-id to use this option. If set, the training code loads an already trained model to
initialize the neural network before resuming training. This option is only valid when the models exist, and have the same behavior names as the
current agents in your scene. (default: False)
--force Whether to force-overwrite this run-id's existing summary and model data. (Without this flag, attempting to train a model with a run-id that has
been used before will throw an error. (default: False)
--run-id RUN_ID The identifier for the training run. This identifier is used to name the subdirectories in which the trained model and summary statistics are
saved as well as the saved model itself. If you use TensorBoard to view the training statistics, always set a unique run-id for each training
run. (The statistics for all runs with the same id are combined as if they were produced by a the same session.) (default: ppo)
--initialize-from RUN_ID
Specify a previously saved run ID from which to initialize the model from. This can be used, for instance, to fine-tune an existing model on a
new environment. Note that the previously saved models must have the same behavior parameters as your current environment. (default: None)
--seed SEED A number to use as a seed for the random number generator used by the training code (default: -1)
--inference Whether to run in Python inference mode (i.e. no training). Use with --resume to load a model trained with an existing run ID. (default: False)
--base-port BASE_PORT
The starting port for environment communication. Each concurrent Unity environment instance will get assigned a port sequentially, starting from
the base-port. Each instance will use the port (base_port + worker_id), where the worker_id is sequential IDs given to each instance from 0 to
(num_envs - 1). Note that when training using the Editor rather than an executable, the base port will be ignored. (default: 5005)
--num-envs NUM_ENVS The number of concurrent Unity environment instances to collect experiences from when training (default: 1)
--debug Whether to enable debug-level logging for some parts of the code (default: False)
--env-args ... Arguments passed to the Unity executable. Be aware that the standalone build will also process these as Unity Command Line Arguments. You should
choose different argument names if you want to create environment-specific arguments. All arguments after this flag will be passed to the
executable. (default: None)
--cpu Forces training using CPU only (default: False)
Engine Configuration:
--width WIDTH The width of the executable window of the environment(s) in pixels (ignored for editor training). (default: 84)
--height HEIGHT The height of the executable window of the environment(s) in pixels (ignored for editor training) (default: 84)
--quality-level QUALITY_LEVEL
The quality level of the environment(s). Equivalent to calling QualitySettings.SetQualityLevel in Unity. (default: 5)
--time-scale TIME_SCALE
The time scale of the Unity environment(s). Equivalent to setting Time.timeScale in Unity. (default: 20)
--target-frame-rate TARGET_FRAME_RATE
The target frame rate of the Unity environment(s). Equivalent to setting Application.targetFrameRate in Unity. (default: -1)
--capture-frame-rate CAPTURE_FRAME_RATE
The capture frame rate of the Unity environment(s). Equivalent to setting Time.captureFramerate in Unity. (default: 60)
--no-graphics Whether to run the Unity executable in no-graphics mode (i.e. without initializing the graphics driver. Use this only if your agents don't use
visual observations. (default: False)