text: Title: Error: PolicyBasedRL 
  Body: **Describe the issue**:
  I tried running the the following models space with PolicyBasedRL and I will also put in the experiment configuration:
  
  #BASELINE NAS USING v2.7
  from nni.retiarii.serializer import model_wrapper
  import torch.nn.functional as F
  import nni.retiarii.nn.pytorch as nn
        
  class Block1(nn.Module):
      def __init__(self, layer_size):
          super().__init__()
          self.conv1 = nn.Conv2d(3, layer_size*2, 3, stride=1,padding=1)
          self.pool = nn.MaxPool2d(2, 2)
          self.conv2 = nn.Conv2d(layer_size*2, layer_size*8, 3, stride=1, padding=1)
          
      def forward(self, x):
          x = F.relu(self.conv1(x))
          x = self.pool(x)
          x = F.relu(self.conv2(x))
          x = self.pool(x)
          return x
      
  class Block2(nn.Module):
      def __init__(self, layer_size):
          super().__init__()
          self.conv1 = nn.Conv2d(3, layer_size, 3, stride=1,padding=1)
          self.conv2 = nn.Conv2d(layer_size, layer_size*2, 3, stride=1,padding=1)
          self.pool = nn.MaxPool2d(2, 2)
          self.conv3 = nn.Conv2d(layer_size*2, layer_size*8, 3, stride=1,padding=1)
          
      def forward(self, x):
          x = F.relu(self.conv1(x))
          x = F.relu(self.conv2(x))
          x = self.pool(x)
          x = F.relu(self.conv3(x))
          x = self.pool(x)
  
          return x
      
  class Block3(nn.Module):
      def __init__(self, layer_size):
          super().__init__()
          self.conv1 = nn.Conv2d(3, layer_size, 3, stride=1,padding=1)
          self.conv2 = nn.Conv2d(layer_size, layer_size*2, 3, stride=1,padding=1)
          self.pool = nn.MaxPool2d(2, 2)
          self.conv3 = nn.Conv2d(layer_size*2, layer_size*4, 3, stride=1,padding=1)
          self.conv4 = nn.Conv2d(layer_size*4, layer_size*8, 3, stride=1, padding=1)
          
      def forward(self, x):
          x = F.relu(self.conv1(x))
          x = F.relu(self.conv2(x))
          x = self.pool(x)
          x = F.relu(self.conv3(x))
          x = F.relu(self.conv4(x))
          x = self.pool(x)
  
          return x
      
  @model_wrapper
  class Net(nn.Module):
      def __init__(self):
          super().__init__()
          rand_var = nn.ValueChoice([32,64])
          self.conv1 = nn.LayerChoice([Block1(rand_var),Block2(rand_var),Block3(rand_var)])
          self.conv2 = nn.Conv2d(rand_var*8,rand_var*16 , 3, stride=1, padding=1)
          self.fc1 = nn.Linear(rand_var*16*8*8, 120)
          self.fc2 = nn.Linear(120, 84)
          self.fc3 = nn.Linear(84, 10)
          
      def forward(self, x):
  
          x = self.conv1(x)
          x = F.relu(self.conv2(x))   
          x = x.reshape(x.shape[0],-1)
          x = F.relu(self.fc1(x))
          x = F.relu(self.fc2(x))
          x = self.fc3(x)
          return x
  model = Net()
  
  from nni.retiarii.experiment.pytorch import RetiariiExeConfig, RetiariiExperiment
  
  exp = RetiariiExperiment(model, trainer, [], RL_strategy)
  
  exp_config = RetiariiExeConfig('local')
  exp_config.experiment_name = '5%_RL_10_epochs_64_batch'
  exp_config.trial_concurrency = 2
  exp_config.max_trial_number = 100
  #exp_config.trial_gpu_number = 2
  exp_config.max_experiment_duration = '660m'
  exp_config.execution_engine = 'base'
  exp_config.training_service.use_active_gpu = False
  
  
  --> This led to the following error:
  
  [2022-04-24 23:49:22] ERROR (nni.runtime.msg_dispatcher_base/Thread-5) 3
  Traceback (most recent call last):
    File "/Users/sh/opt/anaconda3/lib/python3.7/site-packages/nni/runtime/msg_dispatcher_base.py", line 88, in command_queue_worker
      self.process_command(command, data)
    File "/Users/sh/opt/anaconda3/lib/python3.7/site-packages/nni/runtime/msg_dispatcher_base.py", line 147, in process_command
      command_handlers[command](data)
    File "/Users/sh/opt/anaconda3/lib/python3.7/site-packages/nni/retiarii/integration.py", line 170, in handle_report_metric_data
      self._process_value(data['value']))
    File "/Users/sh/opt/anaconda3/lib/python3.7/site-packages/nni/retiarii/execution/base.py", line 111, in _intermediate_metric_callback
      model = self._running_models[trial_id]
  KeyError: 3
  
  
  What does this error mean/ why does it occur/ how can I fix it?
  
  Thanks for your help!
  
  **Environment**:
  - NNI version:
  - Training service (local|remote|pai|aml|etc):
  - Client OS:
  - Server OS (for remote mode only):
  - Python version:
  - PyTorch/TensorFlow version:
  - Is conda/virtualenv/venv used?:
  - Is running in Docker?:
  
  
  **Configuration**:
   - Experiment config (remember to remove secrets!):
   - Search space:
  
  
  **Log message**:
   - nnimanager.log:
   - dispatcher.log:
   - nnictl stdout and stderr:
   
  <!--
  Where can you find the log files:
  LOG: https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/HowToDebug.md#experiment-root-director
  STDOUT/STDERR: https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/Nnictl.md#nnictl%20log%20stdout
  -->
  
  
  **How to reproduce it?**: