MLAgents error: SubprocessEnvManager had workers that didn't signal shutdown

Hi, I have an issue, where when I try to train, it simply loads for a couple seconds and then fails.

mlagents 0.28 with MLAgents package 2.2.1-exp1.

See full printout below:

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  ╖╬│││││╬╜        ╙╬│││││╖╖                               ╗╗╗
  ╬╬╬╬╖││╦╖        ╖╬││╗╣╣╣╬      ╟╣╣╬    ╟╣╣╣             ╜╜╜  ╟╣╣
  ╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╒╣╣╖╗╣╣╣╗   ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖   ╣╣╣
  ╬╬╬╬┐  ╙╬╬╬╬│╓╣╣╣╝╜  ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣  ╣╣╣ ╙╟╣╣╜╙  ╫╣╣  ╟╣╣
  ╬╬╬╬┐     ╙╬╬╣╣      ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣     ╣╣╣┌╣╣╜
  ╬╬╬╜       ╬╬╣╣      ╙╝╣╣╬      ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣╦╓    ╣╣╣╣╣
  ╙   ╓╦╖    ╬╬╣╣   ╓╗╗╖            ╙╝╣╣╣╣╝╜   ╘╝╝╜   ╝╝╝  ╝╝╝   ╙╣╣╣    ╟╣╣╣
    ╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝                                             ╫╣╣╣╣
       ╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜
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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.
 [INFO] Connected to Unity environment with package version 2.2.1-exp.1 and communication version 1.5.0
 [INFO] Connected new brain: MoveToGoal?team=0
 [WARNING] Behavior name MoveToGoal does not match any behaviors specified in the trainer configuration file. A default configuration will be used.
 [WARNING] Deleting TensorBoard data events.out.tfevents.1648051984.DESKTOP-FHQ6GMJ.11680.0 that was left over from a previous run.
 [INFO] Hyperparameters for behavior name MoveToGoal:
         trainer_type:   ppo
         hyperparameters:
           batch_size:   1024
           buffer_size:  10240
           learning_rate:        0.0003
           beta: 0.005
           epsilon:      0.2
           lambd:        0.95
           num_epoch:    3
           learning_rate_schedule:       linear
           beta_schedule:        linear
           epsilon_schedule:     linear
         network_settings:
           normalize:    False
           hidden_units: 128
           num_layers:   2
           vis_encode_type:      simple
           memory:       None
           goal_conditioning_type:       hyper
           deterministic:        False
         reward_signals:
           extrinsic:
             gamma:      0.99
             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:   64
         summary_freq:   50000
         threaded:       False
         self_play:      None
         behavioral_cloning:     None
 [INFO] Exported results\ppo\MoveToGoal\MoveToGoal-0.onnx
 [INFO] Copied results\ppo\MoveToGoal\MoveToGoal-0.onnx to results\ppo\MoveToGoal.onnx.
 [ERROR] SubprocessEnvManager had workers that didn't signal shutdown
 [ERROR] A SubprocessEnvManager worker did not shut down correctly so it was forcefully terminated.
 Traceback (most recent call last):
   File "C:\Users\hjalte\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 193, in _run_module_as_main
     "__main__", mod_spec)
   File "C:\Users\hjalte\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 85, in _run_code
     exec(code, run_globals)
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\Scripts\mlagents-learn.exe\__main__.py", line 7, in <module>
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents\trainers\learn.py", line 260, in main
     run_cli(parse_command_line())
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents\trainers\learn.py", line 256, in run_cli
     run_training(run_seed, options, num_areas)
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents\trainers\learn.py", line 132, in run_training
     tc.start_learning(env_manager)
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
     return func(*args, **kwargs)
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents\trainers\trainer_controller.py", line 176, in start_learning
     n_steps = self.advance(env_manager)
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents_envs\timers.py", line 305, in wrapped
     return func(*args, **kwargs)
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents\trainers\trainer_controller.py", line 234, in advance
     new_step_infos = env_manager.get_steps()
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents\trainers\env_manager.py", line 124, in get_steps
     new_step_infos = self._step()
   File "C:\Users\hjalte\Desktop\Szrot\Szrot ML\venv\lib\site-packages\mlagents\trainers\subprocess_env_manager.py", line 417, in _step
     step: EnvironmentResponse = self.step_queue.get_nowait()
   File "C:\Users\hjalte\AppData\Local\Programs\Python\Python37\lib\multiprocessing\queues.py", line 126, in get_nowait
     return self.get(False)
   File "C:\Users\hjalte\AppData\Local\Programs\Python\Python37\lib\multiprocessing\queues.py", line 109, in get
     self._sem.release()
 ValueError: semaphore or lock released too many times

This exact code worked when I was using mlagents 0.16. I completely removed python and everything, before installing 0.28, so this is definitely a clean install.

For completeness, I'm including the agent I'm trying to train below:

 using System.Collections;
 using System.Collections.Generic;
 using Unity.MLAgents;
 using Unity.MLAgents.Actuators;
 using Unity.MLAgents.Sensors;
 using UnityEngine;
 
 public class TestAgent : Agent
 {
     public Transform target;
     public float speed = 5f;
 
     private MeshRenderer floorRender;
     private MaterialPropertyBlock mpb;
 
     private void Start()
     {
         floorRender = transform.parent.Find("Ground").GetComponent<MeshRenderer>();
         mpb = new MaterialPropertyBlock();
     }
 
     public override void OnEpisodeBegin()
     {
         base.OnEpisodeBegin();
 
         transform.localPosition = new Vector3(Random.Range(-3f, 3f), 0, Random.Range(-3f, 3f));
         target.localPosition    = new Vector3(Random.Range(-3f, 3f), 0, Random.Range(-3f, 3f));
     }
 
     public override void CollectObservations(VectorSensor sensor)
     {
         base.CollectObservations(sensor);
 
         sensor.AddObservation(transform.localPosition);
         sensor.AddObservation(target.localPosition);
     }
 
     public override void OnActionReceived(ActionBuffers actions)
     {
         base.OnActionReceived(actions);
 
         float moveX = actions.ContinuousActions[0];
         float moveZ = actions.ContinuousActions[1];
 
         transform.localPosition += new Vector3(moveX, 0, moveZ) * Time.deltaTime * speed;
     }
 
     public override void Heuristic(in ActionBuffers actionsOut)
     {
         base.Heuristic(actionsOut);
 
         ActionSegment<float> contActions = actionsOut.ContinuousActions;
         contActions[0] = Input.GetAxisRaw("Horizontal");
         contActions[1] = Input.GetAxisRaw("Vertical");
     }
 
     private void OnTriggerEnter(Collider other)
     {
         if (other.CompareTag("Wall"))
         {
             SetReward(-1f);
             mpb.SetColor("_Color", Color.red);
             floorRender.SetPropertyBlock(mpb);
             EndEpisode();
         }
         if (other.CompareTag("Goal"))
         {
             SetReward(1f);
             mpb.SetColor("_Color", Color.green);
             floorRender.SetPropertyBlock(mpb);
             EndEpisode();
         }
     }
 }