more use of item() and additional tests

ml-agents/mlagents/trainers/sac/optimizer_torch.py

         self.soft_update(self.policy.actor_critic.critic, self.target_network, self.tau)
         update_stats = {
             "Losses/Policy Loss": policy_loss.item(),
-            "Losses/Value Loss": ModelUtils.to_numpy(value_loss),
-            "Losses/Q1 Loss": ModelUtils.to_numpy(q1_loss),
-            "Losses/Q2 Loss": ModelUtils.to_numpy(q2_loss),
+            "Losses/Value Loss": value_loss.item(),
+            "Losses/Q1 Loss": q1_loss.item(),
+            "Losses/Q2 Loss": q2_loss.item(),
             "Policy/Entropy Coeff": torch.exp(self._log_ent_coef).item(),
             "Policy/Learning Rate": decay_lr,
         }

ml-agents/mlagents/trainers/tests/torch/test_bcmodule.py

 import pytest
 import mlagents.trainers.tests.mock_brain as mb
 
-import numpy as np
 import os
 
 from mlagents.trainers.policy.torch_policy import TorchPolicy
         default_num_epoch=3,
     )
     return bc_module
+
+
+def assert_stats_are_float(stats):
+    for _, item in stats.items():
+        assert isinstance(item, float)
 
 
 # Test default values
     )
     bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
     stats = bc_module.update()
-    for _, item in stats.items():
-        assert isinstance(item, np.float32)
+    assert_stats_are_float(stats)
 
 
 # Test with constant pretraining learning rate
     )
     bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
     stats = bc_module.update()
-    for _, item in stats.items():
-        assert isinstance(item, np.float32)
+    assert_stats_are_float(stats)
     old_learning_rate = bc_module.current_lr
 
     _ = bc_module.update()
     )
     bc_module = create_bc_module(mock_specs, bc_settings, True, is_sac)
     stats = bc_module.update()
-    for _, item in stats.items():
-        assert isinstance(item, np.float32)
+    assert_stats_are_float(stats)
 
 
 # Test with discrete control and visual observations
     )
     bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
     stats = bc_module.update()
-    for _, item in stats.items():
-        assert isinstance(item, np.float32)
+    assert_stats_are_float(stats)
 
 
 # Test with discrete control, visual observations and RNN
     )
     bc_module = create_bc_module(mock_specs, bc_settings, True, is_sac)
     stats = bc_module.update()
-    for _, item in stats.items():
-        assert isinstance(item, np.float32)
+    assert_stats_are_float(stats)
 
 
 if __name__ == "__main__":

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_curiosity.py

     buffer = create_agent_buffer(behavior_spec, 5)
     for _ in range(200):
         curiosity_rp.update(buffer)
-    prediction = ModelUtils.to_numpy(curiosity_rp._network.predict_action(buffer)[0])
-    target = buffer["actions"][0]
-    error = float(torch.mean((prediction - target) ** 2))
+    prediction = curiosity_rp._network.predict_action(buffer)[0]
+    target = torch.tensor(buffer["actions"][0])
+    error = torch.mean((prediction - target) ** 2).item()
     assert error < 0.001