[bug-fix] Fix ONNX export/Barracuda import for continuous actions (#4608)

* Use torch.ones rather than expand

* use tf.cat instead of tf.expand for Barracuda

* fix error msg handling

* handle loading exception

Co-authored-by: Chris Elion <chris.elion@unity3d.com>

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/ModelOverrider.cs

             var bp = m_Agent.GetComponent<BehaviorParameters>();
             var behaviorName = bp.BehaviorName;
 
-            var nnModel = GetModelForBehaviorName(behaviorName);
+            NNModel nnModel = null;
+            try
+            {
+                nnModel = GetModelForBehaviorName(behaviorName);
+            }
+            catch (Exception e)
+            {
+                overrideError = $"Exception calling GetModelForBehaviorName: {e}";
+            }
+
-                overrideError =
-                    $"Didn't find a model for behaviorName {behaviorName}. Make " +
-                    $"sure the behaviorName is set correctly in the commandline " +
-                    $"and that the model file exists";
+                if (string.IsNullOrEmpty(overrideError))
+                {
+                    overrideError =
+                        $"Didn't find a model for behaviorName {behaviorName}. Make " +
+                        "sure the behaviorName is set correctly in the commandline " +
+                        "and that the model file exists";
+                }
             }
             else
             {

ml-agents/mlagents/trainers/torch/distributions.py

         if self.conditional_sigma:
             log_sigma = torch.clamp(self.log_sigma(inputs), min=-20, max=2)
         else:
-            # Expand so that entropy matches batch size
-            log_sigma = self.log_sigma.expand(inputs.shape[0], -1)
+            # Expand so that entropy matches batch size. Note that we're using
+            # torch.cat here instead of torch.expand() becuase it is not supported in the
+            # verified version of Barracuda (1.0.2).
+            log_sigma = torch.cat([self.log_sigma] * inputs.shape[0], axis=0)
         if self.tanh_squash:
             return [TanhGaussianDistInstance(mu, torch.exp(log_sigma))]
         else: