mlagents-learn 배치파일이 아닙니다 에러 해결
2022. 3. 10. 10:01ㆍUnityMLAgent/오류 해결
C:\WINDOWS\system32>pip install mlagents==0.28.0
Collecting mlagents==0.28.0
Downloading mlagents-0.28.0-py3-none-any.whl (164 kB)
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Collecting grpcio>=1.11.0
Using cached grpcio-1.44.0-cp37-cp37m-win_amd64.whl (3.4 MB)
Collecting cattrs<1.1.0
Downloading cattrs-1.0.0-py2.py3-none-any.whl (14 kB)
Collecting protobuf>=3.6
Downloading protobuf-3.19.4-cp37-cp37m-win_amd64.whl (896 kB)
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Collecting importlib-metadata
Downloading importlib_metadata-4.11.2-py3-none-any.whl (17 kB)
Collecting h5py>=2.9.0
Using cached h5py-3.6.0-cp37-cp37m-win_amd64.whl (2.8 MB)
Collecting tensorboard>=1.15
Downloading tensorboard-2.8.0-py3-none-any.whl (5.8 MB)
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Collecting attrs>=19.3.0
Downloading attrs-21.4.0-py2.py3-none-any.whl (60 kB)
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Collecting pypiwin32==223
Downloading pypiwin32-223-py3-none-any.whl (1.7 kB)
Requirement already satisfied: numpy<2.0,>=1.13.3 in c:\python_3_7_9\lib\site-packages (from mlagents==0.28.0) (1.21.5)
Collecting Pillow>=4.2.1
Downloading Pillow-9.0.1-cp37-cp37m-win_amd64.whl (3.2 MB)
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Collecting mlagents-envs==0.28.0
Downloading mlagents_envs-0.28.0-py3-none-any.whl (77 kB)
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Collecting pyyaml>=3.1.0
Downloading PyYAML-6.0-cp37-cp37m-win_amd64.whl (153 kB)
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Collecting cloudpickle
Downloading cloudpickle-2.0.0-py3-none-any.whl (25 kB)
Collecting pywin32>=223
Downloading pywin32-303-cp37-cp37m-win_amd64.whl (9.2 MB)
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Collecting six>=1.5.2
Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting cached-property
Downloading cached_property-1.5.2-py2.py3-none-any.whl (7.6 kB)
Collecting tensorboard-plugin-wit>=1.6.0
Downloading tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)
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Requirement already satisfied: setuptools>=41.0.0 in c:\python_3_7_9\lib\site-packages (from tensorboard>=1.15->mlagents==0.28.0) (47.1.0)
Collecting werkzeug>=0.11.15
Downloading Werkzeug-2.0.3-py3-none-any.whl (289 kB)
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Collecting tensorboard-data-server<0.7.0,>=0.6.0
Downloading tensorboard_data_server-0.6.1-py3-none-any.whl (2.4 kB)
Collecting requests<3,>=2.21.0
Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
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Collecting wheel>=0.26
Downloading wheel-0.37.1-py2.py3-none-any.whl (35 kB)
Collecting markdown>=2.6.8
Downloading Markdown-3.3.6-py3-none-any.whl (97 kB)
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Collecting google-auth-oauthlib<0.5,>=0.4.1
Downloading google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)
Collecting absl-py>=0.4
Downloading absl_py-1.0.0-py3-none-any.whl (126 kB)
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Collecting google-auth<3,>=1.6.3
Downloading google_auth-2.6.0-py2.py3-none-any.whl (156 kB)
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Requirement already satisfied: typing-extensions>=3.6.4 in c:\python_3_7_9\lib\site-packages (from importlib-metadata->mlagents==0.28.0) (4.1.1)
Collecting zipp>=0.5
Downloading zipp-3.7.0-py3-none-any.whl (5.3 kB)
Collecting cachetools<6.0,>=2.0.0
Downloading cachetools-5.0.0-py3-none-any.whl (9.1 kB)
Collecting pyasn1-modules>=0.2.1
Downloading pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
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Collecting rsa<5,>=3.1.4
Downloading rsa-4.8-py3-none-any.whl (39 kB)
Collecting requests-oauthlib>=0.7.0
Downloading requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)
Collecting idna<4,>=2.5
Downloading idna-3.3-py3-none-any.whl (61 kB)
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Collecting urllib3<1.27,>=1.21.1
Downloading urllib3-1.26.8-py2.py3-none-any.whl (138 kB)
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Collecting certifi>=2017.4.17
Downloading certifi-2021.10.8-py2.py3-none-any.whl (149 kB)
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Collecting charset-normalizer~=2.0.0
Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
Collecting pyasn1<0.5.0,>=0.4.6
Downloading pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
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Collecting oauthlib>=3.0.0
Downloading oauthlib-3.2.0-py3-none-any.whl (151 kB)
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Installing collected packages: tensorboard-plugin-wit, pywin32, pyasn1, certifi, cached-property, zipp, wheel, werkzeug, urllib3, tensorboard-data-server, six, rsa, pyyaml, pypiwin32, pyasn1-modules, protobuf, Pillow, oauthlib, idna, h5py, cloudpickle, charset-normalizer, cachetools, attrs, requests, importlib-metadata, grpcio, google-auth, cattrs, absl-py, requests-oauthlib, mlagents-envs, markdown, google-auth-oauthlib, tensorboard, mlagents
Successfully installed Pillow-9.0.1 absl-py-1.0.0 attrs-21.4.0 cached-property-1.5.2 cachetools-5.0.0 cattrs-1.0.0 certifi-2021.10.8 charset-normalizer-2.0.12 cloudpickle-2.0.0 google-auth-2.6.0 google-auth-oauthlib-0.4.6 grpcio-1.44.0 h5py-3.6.0 idna-3.3 importlib-metadata-4.11.2 markdown-3.3.6 mlagents-0.28.0 mlagents-envs-0.28.0 oauthlib-3.2.0 protobuf-3.19.4 pyasn1-0.4.8 pyasn1-modules-0.2.8 pypiwin32-223 pywin32-303 pyyaml-6.0 requests-2.27.1 requests-oauthlib-1.3.1 rsa-4.8 six-1.16.0 tensorboard-2.8.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 urllib3-1.26.8 werkzeug-2.0.3 wheel-0.37.1 zipp-3.7.0
C:\WINDOWS\system32>pip list
Package Version
----------------------- -----------
absl-py 1.0.0
attrs 21.4.0
cached-property 1.5.2
cachetools 5.0.0
cattrs 1.0.0
certifi 2021.10.8
charset-normalizer 2.0.12
cloudpickle 2.0.0
google-auth 2.6.0
google-auth-oauthlib 0.4.6
grpcio 1.44.0
h5py 3.6.0
idna 3.3
importlib-metadata 4.11.2
Markdown 3.3.6
mlagents 0.28.0
mlagents-envs 0.28.0
numpy 1.21.5
oauthlib 3.2.0
Pillow 9.0.1
pip 22.0.4
protobuf 3.19.4
pyasn1 0.4.8
pyasn1-modules 0.2.8
pypiwin32 223
pywin32 303
PyYAML 6.0
requests 2.27.1
requests-oauthlib 1.3.1
rsa 4.8
setuptools 47.1.0
six 1.16.0
tensorboard 2.8.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
torch 1.7.1+cu110
typing_extensions 4.1.1
urllib3 1.26.8
Werkzeug 2.0.3
wheel 0.37.1
zipp 3.7.0
C:\WINDOWS\system32>mlagents -learn
'mlagents'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는
배치 파일이 아닙니다.
C:\WINDOWS\system32>mlagents --learn
'mlagents'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는
배치 파일이 아닙니다.
C:\WINDOWS\system32>mlagents -learn --help
'mlagents'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는
배치 파일이 아닙니다.
C:\WINDOWS\system32>mlagents-learn --help
usage: mlagents-learn.exe [-h] [--env ENV_PATH] [--resume] [--deterministic]
[--force] [--run-id RUN_ID]
[--initialize-from RUN_ID] [--seed SEED]
[--inference] [--base-port BASE_PORT]
[--num-envs NUM_ENVS] [--num-areas NUM_AREAS]
[--debug] [--env-args ...]
[--max-lifetime-restarts MAX_LIFETIME_RESTARTS]
[--restarts-rate-limit-n RESTARTS_RATE_LIMIT_N]
[--restarts-rate-limit-period-s RESTARTS_RATE_LIMIT_PERIOD_S]
[--torch] [--tensorflow] [--results-dir RESULTS_DIR]
[--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] [--torch-device DEVICE]
[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)
--deterministic Whether to select actions deterministically in policy.
`dist.mean` for continuous action space, and
`dist.argmax` for deterministic action space (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)
--num-areas NUM_AREAS
The number of parallel training areas in each Unity
environment instance. (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)
--max-lifetime-restarts MAX_LIFETIME_RESTARTS
The max number of times a single Unity executable can
crash over its lifetime before ml-agents exits. Can be
set to -1 if no limit is desired. (default: 10)
--restarts-rate-limit-n RESTARTS_RATE_LIMIT_N
The maximum number of times a single Unity executable
can crash over a period of time (period set in
restarts-rate-limit-period-s). Can be set to -1 to not
use rate limiting with restarts. (default: 1)
--restarts-rate-limit-period-s RESTARTS_RATE_LIMIT_PERIOD_S
The period of time --restarts-rate-limit-n applies to.
(default: 60)
--torch (Removed) Use the PyTorch framework. (default: False)
--tensorflow (Removed) Use the TensorFlow framework. (default:
False)
--results-dir RESULTS_DIR
Results base directory (default: results)
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)
Torch Configuration:
--torch-device DEVICE
Settings for the default torch.device used in
training, for example, "cpu", "cuda", or "cuda:0"
(default: None)
C:\WINDOWS\system32>
C:\WINDOWS\system32>mlagents-learn
┐ ╖
╓╖╬│╡ ││╬╖╖
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╖╬│││││╬╜ ╙╬│││││╖╖ ╗╗╗
╬╬╬╬╖││╦╖ ╖╬││╗╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╜╜╜ ╟╣╣
╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╒╣╣╖╗╣╣╣╗ ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖ ╣╣╣
╬╬╬╬┐ ╙╬╬╬╬│╓╣╣╣╝╜ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣ ╣╣╣ ╙╟╣╣╜╙ ╫╣╣ ╟╣╣
╬╬╬╬┐ ╙╬╬╣╣ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣ ╣╣╣┌╣╣╜
╬╬╬╜ ╬╬╣╣ ╙╝╣╣╬ ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣╦╓ ╣╣╣╣╣
╙ ╓╦╖ ╬╬╣╣ ╓╗╗╖ ╙╝╣╣╣╣╝╜ ╘╝╝╜ ╝╝╝ ╝╝╝ ╙╣╣╣ ╟╣╣╣
╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝ ╫╣╣╣╣
╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜
╙╬╬╬╣╣╣╜
╙
Version information:
ml-agents: 0.28.0,
ml-agents-envs: 0.28.0,
Communicator API: 1.5.0,
PyTorch: 1.7.1+cu110
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.pip list 했을 때 mlagents가 안 깔린 걸 확인했다.
pip install mlagents==0.28.0 로 해결됐다.