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 Version information:
  ml-agents: 0.29.0,
  ml-agents-envs: 0.29.0,
  Communicator API: 1.5.0,
  PyTorch: 1.12.0+cpu
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
[INFO] Connected to Unity environment with package version 2.0.1 and communication version 1.5.0
[INFO] Connected new brain: BoxPerson?team=0
[WARNING] Deleting TensorBoard data events.out.tfevents.1656957925.KevinDesktop.3480.0 that was left over from a previous run.
[WARNING] Deleting TensorBoard data events.out.tfevents.1656957925.KevinDesktop.3480.0.meta that was left over from a previous run.
[INFO] Hyperparameters for behavior name BoxPerson:
        trainer_type:   ppo
        hyperparameters:
          batch_size:   32
          buffer_size:  256
          learning_rate:        0.0003
          beta: 0.005
          epsilon:      0.2
          lambd:        0.95
          num_epoch:    5
          learning_rate_schedule:       linear
          beta_schedule:        linear
          epsilon_schedule:     linear
        network_settings:
          normalize:    False
          hidden_units: 256
          num_layers:   3
          vis_encode_type:      simple
          memory:       None
          goal_conditioning_type:       hyper
          deterministic:        False
        reward_signals:
          extrinsic:
            gamma:      0.9
            strength:   1.0
            network_settings:
              normalize:        False
              hidden_units:     128
              num_layers:       2
              vis_encode_type:  simple
              memory:   None
              goal_conditioning_type:   hyper
              deterministic:    False
        init_path:      None
        keep_checkpoints:       5
        checkpoint_interval:    500000
        max_steps:      500000
        time_horizon:   3
        summary_freq:   2000
        threaded:       False
        self_play:      None
        behavioral_cloning:     None
C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\torch\networks.py:91: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Trigger
ed internally at  C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\utils\tensor_new.cpp:204.)
  enc.update_normalization(torch.as_tensor(vec_input))
C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\torch\utils.py:320: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matricesor
 `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at  C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:2985.)
  return (tensor.T * masks).sum() / torch.clamp(
[WARNING] Restarting worker[0] after 'Communicator has exited.'
[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.
[INFO] Exported results\test\BoxPerson\BoxPerson-782.onnx
[INFO] Copied results\test\BoxPerson\BoxPerson-782.onnx to results\test\BoxPerson.onnx.
Traceback (most recent call last):
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\Scripts\mlagents-learn.exe\__main__.py", line 7, in <module>
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\learn.py", line 260, in main
    run_cli(parse_command_line())
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\learn.py", line 256, in run_cli
    run_training(run_seed, options, num_areas)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\learn.py", line 132, in run_training
    tc.start_learning(env_manager)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
    return func(*args, **kwargs)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\trainer_controller.py", line 176, in start_learning
    n_steps = self.advance(env_manager)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
    return func(*args, **kwargs)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\trainer_controller.py", line 234, in advance
    new_step_infos = env_manager.get_steps()
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\env_manager.py", line 124, in get_steps
    new_step_infos = self._step()
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 420, in _step
    self._restart_failed_workers(step)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 328, in _restart_failed_workers
    self.reset(self.env_parameters)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\lib\site-packages\mlagents\trainers\env_manager.py", line 68, in reset
    self.first_step_infos = self._reset_env(config)
  File "C:\Users\kevin\AppData\Local\Programs\Python\Python37\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\kevin\AppData\Local\Programs\Python\Python37\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.