Unityとml-agentsを用いて機械学習したい

どのように環境構築を設定したら、Unityで学習ができるのでしょうか？

発生している問題・エラーメッセージ

mlagents-learn ./config/trainer_config.yaml --run-id=～～～と入力

2020-05-28 19:37:39.944015: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-05-28 19:37:39.948579: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:From D:\Anaconda\envs\ml-agents\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

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 Version information:
  ml-agents: 0.16.0,
  ml-agents-envs: 0.16.0,
  Communicator API: 1.0.0,
  TensorFlow: 2.2.0
2020-05-28 19:37:41.755400: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-05-28 19:37:41.760354: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:From D:\Anaconda\envs\ml-agents\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
2020-05-28 19:37:43 INFO [environment.py:201] Listening on port 5004\. Start training by pressing the Play button in the Unity Editor.

anaconda promptではタイムアウトとして以下のメッセージが出る。

2020-05-28 19:38:43 INFO [subprocess_env_manager.py:191] UnityEnvironment worker 0: environment stopping.
Traceback (most recent call last):
  File "D:\Anaconda\envs\ml-agents\Scripts\mlagents-learn-script.py", line 11, in <module>
    load_entry_point('mlagents', 'console_scripts', 'mlagents-learn')()
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\learn.py", line 554, in main
    run_cli(parse_command_line())
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\learn.py", line 550, in run_cli
    run_training(run_seed, options)
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\learn.py", line 407, in run_training
    tc.start_learning(env_manager)
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped
    return func(*args, **kwargs)
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\trainer_controller.py", line 223, in start_learning
    self._reset_env(env_manager)
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents-envs\mlagents_envs\timers.py", line 305, in wrapped
    return func(*args, **kwargs)
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\trainer_controller.py", line 154, in _reset_env
    env.reset(config=sampled_reset_param)
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\env_manager.py", line 67, in reset
    self.first_step_infos = self._reset_env(config)
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 295, in _reset_env
    ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {})
  File "c:\users\kator\onedrive\ドキュメント\ml-agents-release_1\ml-agents-release_1\ml-agents\mlagents\trainers\subprocess_env_manager.py", line 92, in recv
    raise env_exception
mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that :
         The environment does not need user interaction to launch
         The Agents are linked to the appropriate Brains
         The environment and the Python interface have compatible versions.