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웨이브

64비트 빌드를 해야함 
https://developer.android.com/distribute/best-practices/develop/64-bit
2019년 8월 1일부터 Google Play에 게시되는 앱에서는 64비트 아키텍처를 지원해야 합니다. 64비트 CPU는 사용자에게 더 빠르고 풍부한 환경을 제공합니다. 앱의 64비트 버전을 추가하면 성능이 향상되고 향후 혁신을 이룰 가능성이 높아지며 64비트 전용 하드웨어가 장착된 기기에 대응할 수 있습니다.

유니티 에디터 Player Setting > Target Architectures (ARMv7, x86) 둘다 체크 할것

참고: https://blogs.unity3d.com/kr/2019/03/05/android-support-update-64-bit-and-app-bundles-backported-to-2017-4-lts/

https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-Imitation-Learning.md

안드로이드 플랫폼 빌드후 기기에 설치 하고 강화 학습을 시도해보았다.

여러경로를 통해 현재는 불가능 하다는것을 알았다.

PC, Mac & Linux Standalone platform으로 빌드를 했다.

다음과 같이 실행파일이 생성되었다.

파일 경로는 다음과 같다.

C:\Users\smilejsu\Desktop\test-mlagent.exe

Anaconda Prompt 를 사용하여 다음 명령어를 통해 훈련을 시도 해보았다.

(base) C:\Users\smilejsu>activate ml-agents

(ml-agents) C:\Users\smilejsu>d:

(ml-agents) D:\>cd D:\workspace\unity\Test\UnitySDK

(ml-agents) D:\workspace\unity\Test\UnitySDK>mlagents-learn trainer_config.yaml --env=C:\Users\smilejsu\Desktop\test-mlagent.exe --run-id=train --train


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INFO:mlagents.trainers:{'--base-port': '5005',
 '--curriculum': 'None',
 '--debug': False,
 '--docker-target-name': 'None',
 '--env': 'C:\\Users\\smilejsu\\Desktop\\test-mlagent.exe',
 '--help': False,
 '--keep-checkpoints': '5',
 '--lesson': '0',
 '--load': False,
 '--no-graphics': False,
 '--num-envs': '1',
 '--num-runs': '1',
 '--run-id': 'train',
 '--save-freq': '50000',
 '--seed': '-1',
 '--slow': False,
 '--train': True,
 '<trainer-config-path>': 'trainer_config.yaml'}
c:\users\smilejsu\appdata\local\conda\conda\envs\ml-agents\lib\site-packages\mlagents\trainers\learn.py:141: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  trainer_config = yaml.load(data_file)
INFO:mlagents.envs:
'TestAcademy' started successfully!
Unity Academy name: TestAcademy
        Number of Brains: 1
        Number of Training Brains : 1
        Reset Parameters :

Unity brain name: RollerBallBrain
        Number of Visual Observations (per agent): 0
        Vector Observation space size (per agent): 6
        Number of stacked Vector Observation: 1
        Vector Action space type: continuous
        Vector Action space size (per agent): [2]
        Vector Action descriptions: ,
2019-06-05 09:23:09.796920: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
INFO:mlagents.envs:Hyperparameters for the PPO Trainer of brain RollerBallBrain:
        batch_size:     10
        beta:   0.005
        buffer_size:    100
        epsilon:        0.2
        gamma:  0.99
        hidden_units:   128
        lambd:  0.95
        learning_rate:  0.0003
        max_steps:      5.0e4
        normalize:      False
        num_epoch:      3
        num_layers:     2
        time_horizon:   64
        sequence_length:        64
        summary_freq:   1000
        use_recurrent:  False
        summary_path:   ./summaries/train-0_RollerBallBrain
        memory_size:    256
        use_curiosity:  False
        curiosity_strength:     0.01
        curiosity_enc_size:     128
        model_path:     ./models/train-0/RollerBallBrain
c:\users\smilejsu\appdata\local\conda\conda\envs\ml-agents\lib\site-packages\numpy\core\fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
c:\users\smilejsu\appdata\local\conda\conda\envs\ml-agents\lib\site-packages\numpy\core\_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
INFO:mlagents.trainers: train-0: RollerBallBrain: Step: 1000. Time Elapsed: 8.189 s Mean Reward: -0.976. Std of Reward: 0.623. Training.
INFO:mlagents.trainers: train-0: RollerBallBrain: Step: 2000. Time Elapsed: 15.860 s Mean Reward: -1.394. Std of Reward: 1.200. Training.
INFO:mlagents.trainers: train-0: RollerBallBrain: Step: 3000. Time Elapsed: 23.409 s Mean Reward: -0.484. Std of Reward: 1.027. Training.
INFO:mlagents.trainers: train-0: RollerBallBrain: Step: 4000. Time Elapsed: 30.629 s Mean Reward: -0.551. Std of Reward: 0.405. Training.
INFO:mlagents.trainers: train-0: RollerBallBrain: Step: 5000. Time Elapsed: 37.751 s Mean Reward: -0.378. Std of Reward: 0.480. Training.

다음과 같이 실행파일 (test-mlagent.exe)이 자동으로 실행 되며 훈련이 잘 진행 되었다.

참고:

https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Executable.md

https://github.com/Unity-Technologies/ml-agents/issues/2099

학습이 끝나면 .nn파일도 만들어 졌으며 동적으로 훈련된 모델을 Brain에 넣어 실행 시키는것도 가능했다.

INFO:mlagents.envs:Saved Model
INFO:mlagents.trainers:List of nodes to export for brain :RollerBallBrain
INFO:mlagents.trainers: is_continuous_control
INFO:mlagents.trainers: version_number
INFO:mlagents.trainers: memory_size
INFO:mlagents.trainers: action_output_shape
INFO:mlagents.trainers: action
INFO:mlagents.trainers: action_probs
INFO:mlagents.trainers: value_estimate
INFO:tensorflow:Restoring parameters from ./models/train-0/RollerBallBrain\model-15901.cptk
INFO:tensorflow:Froze 17 variables.
Converted 17 variables to const ops.
Converting ./models/train-0/RollerBallBrain/frozen_graph_def.pb to ./models/train-0/RollerBallBrain.nn
IGNORED: StopGradient unknown layer
GLOBALS: 'is_continuous_control', 'version_number', 'memory_size', 'action_output_shape'
IN: 'vector_observation': [-1, 1, 1, 6] => 'main_graph_0/hidden_0/BiasAdd'
IN: 'vector_observation': [-1, 1, 1, 6] => 'main_graph_1/hidden_0/BiasAdd'
IN: 'epsilon': [-1, 1, 1, 2] => 'mul'
OUT: 'action', 'action_probs', 'value_estimate'
DONE: wrote ./models/train-0/RollerBallBrain.nn file.
INFO:mlagents.trainers:Exported ./models/train-0/RollerBallBrain.nn file

(ml-agents) D:\workspace\unity\Test\UnitySDK>

Android platform 빌드후 기기(안드로이드)에 apk를 설치 하고 앱을 실행시켜 강화 학습을 할수 있는지 궁금하다 

https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-Imitation-Learning.md

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