Update docs (#90)

### What change is being made

Update exporter tutorial. Fix link.

### Why this change is being made

N/A

### Tested

N/A

GitOrigin-RevId: 2d1bd16fb21e4b9a31256c27ad0d1ecf2cb57d52

Author: dbellicoso-bdai

README.md

@@ -101,7 +101,7 @@ We use [Pixi](https://pixi.sh) to build this repository. See the
    target_link_libraries(my_controller PRIVATE exploy)
    ```
 
-  See [`examples/controller/`](examples/controller/) for a complete working example.
+  See [`examples/controller/`](https://github.com/bdaiinstitute/exploy/blob/main/examples/controller) for a complete working example.
 
 #### Initialize the environment and install dependencies
 

docs/tutorial/exporter/exporter_tutorial.md

@@ -396,55 +396,74 @@ This traces all registered torch operations — observation computation, the act
 processing — and writes them into a single ONNX graph. The resulting file contains the full
 computation pipeline from raw inputs to commanded outputs, along with the metadata you registered.
 
----
+### Inspecting and understanding the generated ONNX files
+When exporting an environment and its actor, Exploy produces `policy.onnx` (or whatever filename
+is passed to `export_environment_as_onnx()`). That exported file can then be used for evaluation
+(see [Putting It All Together](#putting-it-all-together)) or for deployment in a controller
+(see [Controller Tutorial](../controller/controller_tutorial.md)).
+
+The export contains two execution paths by design: **Default** and **ProcessActions**. The
+**Default** path runs at the policy rate and includes observation computation plus actor inference,
+while the **ProcessActions** path runs at the simulation rate and captures the computational graph
+for action post-processing and command application that occurs at every simulation sub-step. This
+separation preserves the original control timing (policy step vs. simulation sub-step) in the
+exported model.
+
+Additionally, Exploy will produce debug computational graphs. These are only to be used for
+debugging and visual inspection.
+
+- `debug/policy_default.onnx`: The `Default` path of the computational graph.
+- `debug/policy_process_actions.onnx`: The `ProcessActions` path of the computational graph.
+- `debug/policy_optimized.onnx`: The optimized version of the exported ONNX model.
+
+
+> **Note:** `debug/policy_optimized.onnx` is generated when `optimize=True` is passed to
+> {py:class}`SessionWrapper <exploy.exporter.core.session_wrapper.SessionWrapper>`. While fully
+> functional, it requires the same ONNX Runtime version to be used in both the exporter and the
+> controller. This constraint depends on the user's setup.
+> For deployment in a controller, only the `policy.onnx` file should be used.
 
-## Visualizing the Exported ONNX Graph
+
+### Visualizing the Exported ONNX Graph
 
 You can inspect the exported ONNX file using [Netron](https://github.com/lutzroeder/netron), an
 open-source viewer for neural network models. The screenshots below show the computational graphs
-produced by the export steps in this tutorial.
-
-### Overview
+produced by the export steps in this tutorial. The [unit test](https://github.com/bdaiinstitute/exploy/blob/main/python/exploy/exporter/core/tests/test_export_environment.py) runs on three different environments:
+- an environment that computes observations and uses an MLP actor
+- an environment that uses a torch module to compute parts of its observations and uses an MLP actor
+- an environment that uses a torch module to compute parts of its observations and uses an RNN-based actor
 
-The exported ONNX file contains two subgraphs: a **Default** graph that computes observations and
+Each exported ONNX file contains two subgraphs: a **Default** graph that computes observations and
 actions at the policy rate, and a **ProcessActions** graph that maps raw actions to commanded
 outputs at the simulation rate.
 
-![Overview of the exported ONNX graph](exporter_tutorial_netron_overview.png)
 
-### Default graph (basic environment)
+#### Environment and MLP Actor
 
 This is the full default graph for the basic `Environment` from Step 1. You can see the named
-inputs (`foo`, `bar`, `baz`, `memory.actions.in`) flowing through the observation computation
-and into the actor network, which produces actions and the named outputs (`out`,
-`memory.actions.out`).
-
-![Default graph for the basic environment](exporter_tutorial_netron_default.png)
+inputs (`foo`), the group inputs (`bar`, `baz`), and the memory inputs (`memory.actions.in`) flowing
+through the observation computation and into the actor network, which produces actions and the named
+outputs (`out`, `memory.actions.out`). The ProcessActions subgraph traces only `process_actions()`
+and `apply_actions()`. It runs at the simulation time-step rate (i.e., once per decimation sub-step)
+and maps actions to commanded outputs.
 
-### ProcessActions subgraph
+![Overview of the exported ONNX graph](exporter_tutorial_netron_env.svg)
 
-The ProcessActions subgraph traces only `process_actions()` and `apply_actions()`. It runs at the
-simulation time-step rate (i.e., once per decimation sub-step) and maps actions to commanded
-outputs.
-
-![ProcessActions subgraph](exporter_tutorial_netron_process_actions.png)
-
-### Default graph with a torch module
+#### Environment with a torch module and MLP Actor
 
 When the environment includes a learnable `torch.nn.Module` (see the
 [Advanced: Using Torch Modules in Observations](#advanced-using-torch-modules-in-observations)
 section), the module's parameters and operations appear in the graph:
 
-![Default graph for environment with torch module](exporter_tutorial_netron_envwithmodule_default.png)
+![Default graph for environment with torch module](exporter_tutorial_netron_env_with_module.svg)
 
-### Default graph with a torch module and an RNN actor
+#### Environment with a torch module and an RNN Actor
 
 When using an RNN-based actor (see
 [Advanced: RNN-Based Actors](#advanced-rnn-based-actors)), the graph includes the LSTM hidden
 states as additional memory inputs and outputs:
 
-![Default graph for environment with torch module and RNN actor](exporter_tutorial_netron_envwithmodule_rnnactor_default.png)
-
+![Default graph for environment with torch module and RNN actor](exporter_tutorial_netron_env_with_module_rnn_actor.svg)
 ---
 
 ## Step 6 — Load and Run the Exported Model