fix export input_name

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

     def __init__(self, policy):
         self.policy = policy
         batch_dim = [1]
+        seq_len_dim = [1]
         dummy_vec_obs = [torch.zeros(batch_dim + [self.policy.vec_obs_size])]
         dummy_vis_obs = [
             torch.zeros(batch_dim + list(shape))
         dummy_masks = torch.ones(batch_dim + [sum(self.policy.actor_critic.act_size)])
-        dummy_memories = torch.zeros(batch_dim + [1] + [self.policy.m_size])
+        dummy_memories = torch.zeros(batch_dim + seq_len_dim + [self.policy.m_size])
-        # Need to pass all possible inputs since currently keyword arguments is not
-        # supported by torch.nn.export()
-        # Input names can only contain actual input used since in torch.nn.export
-        # it maps input_names only to input nodes that exist in the graph
-        self.input_names = []
-        self.dynamic_axes = {"action": {0: "batch"}, "action_probs": {0: "batch"}}
-        if self.policy.use_vec_obs:
-            self.input_names.append("vector_observation")
-            self.dynamic_axes.update({"vector_observation": {0: "batch"}})
-        for i in range(self.policy.vis_obs_size):
-            self.input_names.append(f"visual_observation_{i}")
-            self.dynamic_axes.update({f"visual_observation_{i}": {0: "batch"}})
-        if not self.policy.use_continuous_act:
-            self.input_names.append("action_masks")
-            self.dynamic_axes.update({"action_masks": {0: "batch"}})
-        if self.policy.use_recurrent:
-            self.input_names.append("memories")
-            self.dynamic_axes.update({"memories": {0: "batch"}})
+        self.input_names = (
+            ["vector_observation"]
+            + [f"visual_observation_{i}" for i in range(self.policy.vis_obs_size)]
+            + ["action_masks", "memories"]
+        )
 
         self.output_names = [
             "action",
             "is_continuous_control",
             "action_output_shape",
         ]
+
+        self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}
+        self.dynamic_axes.update({"action": {0: "batch"}, "action_probs": {0: "batch"}})
 
     def export_policy_model(self, output_filepath: str) -> None:
         """
             self.policy.actor_critic,
             self.dummy_input,
             onnx_output_path,
-            verbose=False,
             opset_version=SerializationSettings.onnx_opset,
             input_names=self.input_names,
             output_names=self.output_names,

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

         vis_inputs: List[torch.Tensor],
         masks: Optional[torch.Tensor] = None,
         memories: Optional[torch.Tensor] = None,
-        sequence_length: int = 1,
     ) -> Tuple[torch.Tensor, torch.Tensor, int, int, int, int]:
         """
         Forward pass of the Actor for inference. This is required for export to ONNX, and
         vis_inputs: List[torch.Tensor],
         masks: Optional[torch.Tensor] = None,
         memories: Optional[torch.Tensor] = None,
-        sequence_length: int = 1,
-        dists, _ = self.get_dists(
-            vec_inputs, vis_inputs, masks, memories, sequence_length
-        )
+        dists, _ = self.get_dists(vec_inputs, vis_inputs, masks, memories, 1)
         action_list = self.sample_action(dists)
         sampled_actions = torch.stack(action_list, dim=-1)
         if self.act_type == ActionType.CONTINUOUS: