ML-agentsを用いた学習を開始することができない

mlagents-learn ./config/sample/RollerBall.yaml --run-id=RollerBall-1

            ┐  ╖
        ╓╖╬│╡  ││╬╖╖
    ╓╖╬│││││┘  ╬│││││╬╖
 ╖╬│││││╬╜        ╙╬│││││╖╖                               ╗╗╗
 ╬╬╬╬╖││╦╖        ╖╬││╗╣╣╣╬      ╟╣╣╬    ╟╣╣╣             ╜╜╜  ╟╣╣
 ╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╒╣╣╖╗╣╣╣╗   ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖   ╣╣╣
 ╬╬╬╬┐  ╙╬╬╬╬│╓╣╣╣╝╜  ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣  ╣╣╣ ╙╟╣╣╜╙  ╫╣╣  ╟╣╣
 ╬╬╬╬┐     ╙╬╬╣╣      ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣     ╣╣╣┌╣╣╜
 ╬╬╬╜       ╬╬╣╣      ╙╝╣╣╬      ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣╦╓    ╣╣╣╣╣
 ╙   ╓╦╖    ╬╬╣╣   ╓╗╗╖            ╙╝╣╣╣╣╝╜   ╘╝╝╜   ╝╝╝  ╝╝╝   ╙╣╣╣    ╟╣╣╣
   ╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝                                             ╫╣╣╣╣
      ╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜
          ╙╬╬╬╣╣╣╜
             ╙

 Version information:
  ml-agents: 0.28.0,
  ml-agents-envs: 0.28.0,
  Communicator API: 1.5.0,
  PyTorch: 1.8.2+cpu
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.

Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
Traceback (most recent call last):
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\Scripts\mlagents-learn.exe\__main__.py", line 7, in <module>
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\learn.py", line 260, in main
    run_cli(parse_command_line())
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\learn.py", line 256, in run_cli
    run_training(run_seed, options, num_areas)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\learn.py", line 132, in run_training
    tc.start_learning(env_manager)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
    return func(*args, **kwargs)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\trainer_controller.py", line 173, in start_learning
    self._reset_env(env_manager)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
    return func(*args, **kwargs)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\trainer_controller.py", line 105, in _reset_env
    env_manager.reset(config=new_config)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\env_manager.py", line 68, in reset
    self.first_step_infos = self._reset_env(config)
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 446, in _reset_env
    ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {})
  File "C:\Users\tnk05\anaconda3\envs\MLAgentsLowLevelAPI\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 101, 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' Behavior Parameters > Behavior Type is set to "Default"
         The environment and the Python interface have compatible versions.
         If you're running on a headless server without graphics support, turn off display by either passing --no-graphics option or build your Unity executable as server build

python3.7.16
Unity 2021.2.18f1
ML-Agents Release 19
PyTorch 1.8.2LTS