Docs link test.

.github/workflows/ci-format.yml

@@ -100,7 +100,7 @@ jobs:
           pip install wheel==0.38.4
           pip install .[camera_obs,doc,train,ray,envision,argoverse,opendrive,waymo,sumo]
           cd ${GITHUB_WORKSPACE}/docs
-          make html SPHINXOPTS="-W -T -E -n --keep-going -b spelling"
+          make html SPHINXOPTS="-W -T -E -n --keep-going -b spelling -b linkcheck"
       - name: Check build output
         run: |
           cd $GITHUB_WORKSPACE

Makefile

@@ -154,7 +154,7 @@ format: gen-header
 
 .PHONY: docs
 docs:
-	cd docs && make clean html SPHINXOPTS="-W -T -n --keep-going -b spelling"
+	cd docs && make clean html SPHINXOPTS="-W -T -n --keep-going -b spelling -b linkcheck"
 
 .PHONY: wheel
 wheel: docs

docs/benchmarks/agent_zoo.rst

@@ -53,16 +53,16 @@ Available zoo agents
      - driving_smarts_2022==0.0
      - :attr:`~smarts.core.controllers.action_space_type.ActionSpaceType.TargetPose`
      - `code <https://github.com/smarts-project/smarts-project.rl/tree/master/interaction_aware_motion_prediction>`__
-     - Contributed as part of `NeurIPS 2022 Driving SMARTS <https://smarts-project.github.io/archive/2022_nips_driving_smarts/>`__ competition.
+     - Contributed as part of `NeurIPS 2022 Driving SMARTS <https://smarts-project.github.io/archive/2022_nips_driving_smarts/competition/>`__ competition.
    * - | zoo.policies:control-and-supervised-learning-agent-v0
        | zoo/policies/control_and_supervised_learning
      - driving_smarts_2022==0.0
      - :attr:`~smarts.core.controllers.action_space_type.ActionSpaceType.TargetPose`
      - `code <https://github.com/smarts-project/smarts-project.rl/tree/master/control_and_supervised_learning>`__
-     - Contributed as part of `NeurIPS 2022 Driving SMARTS <https://smarts-project.github.io/archive/2022_nips_driving_smarts/>`__ competition.
+     - Contributed as part of `NeurIPS 2022 Driving SMARTS <https://smarts-project.github.io/archive/2022_nips_driving_smarts/competition/>`__ competition.
    * - | zoo.policies:discrete-soft-actor-critic-agent-v0
        | zoo/policies/discrete_soft_actor_critic
      - driving_smarts_2022==0.0
      - :attr:`~smarts.core.controllers.action_space_type.ActionSpaceType.TargetPose`
      - `code <https://github.com/smarts-project/smarts-project.rl/tree/master/discrete_soft_actor_critic>`__
-     - Contributed as part of `NeurIPS 2022 Driving SMARTS <https://smarts-project.github.io/archive/2022_nips_driving_smarts/>`__ competition.
+     - Contributed as part of `NeurIPS 2022 Driving SMARTS <https://smarts-project.github.io/archive/2022_nips_driving_smarts/competition/>`__ competition.

docs/conf.py

@@ -148,6 +148,14 @@
     (r"py:class", r".*\.?S"),
 }
 
+# -- Options for broken link checks ------------------------------------------
+linkcheck_anchors = False
+linkcheck_ignore = [
+    r"https?://localhost:\d+/?",
+    r"https?://ops.fhwa.dot.gov.*", # The ngsim domain (us government) appears to go down sometimes.
+]
+linkcheck_retries = 2
+
 # -- Options for spelling ----------------------------------------------------
 spelling_exclude_patterns = ["ignored_*", "**/*_pb2*"]
 spelling_ignore_pypi_package_names = True

docs/ecosystem/ngsim.rst

@@ -88,7 +88,7 @@ To consume the generated observations you could use the following approach:
             ...
 
 
-Alternatively, an approach like :examples:`traffic_histories_vehicle_replacement.py` can be used to operate directly with the scenarios.
+Alternatively, an approach like :examples:`direct/traffic_histories_vehicle_replacement.py` can be used to operate directly with the scenarios.
 
 
 Samples

docs/ecosystem/rllib.rst

@@ -38,4 +38,4 @@ Resume training
 With respect to ``SMARTS/examples/e12_rllib`` examples, if you want to continue an aborted experiment, you can set ``resume_training=True``. But note that ``resume_training=True`` will continue to use the same configuration as was set in the original experiment.
 To make changes to a started experiment, you can edit the latest experiment file in ``./results``.
 
-Or if you want to start a new experiment but train from an existing checkpoint, you will need to look into `How to Save and Load Trial Checkpoints <https://docs.ray.io/en/latest/tune/tutorials/tune-trial-checkpoints>`_.
+Or if you want to start a new experiment but train from an existing checkpoint, you will need to look into `How to Save and Load Trial Checkpoints <https://docs.ray.io/en/latest/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints>`_.

docs/ecosystem/sumo.rst

@@ -24,6 +24,6 @@ Build from source
 If you wish to compile SUMO by yourself from source
 
 + Git repository can be found at `https://github.com/eclipse/sumo <https://github.com/eclipse/sumo>`_ .
-+ Use SUMO version `1.7.0 <https://github.com/eclipse/sumo/commits/v1_7_0>`_ or higher.
-+ Build instructions are available at `https://sumo.dlr.de/docs/Developer/Main.html#build_instructions <https://sumo.dlr.de/docs/Developer/Main.html#build_instructions>`_ . 
++ Use SUMO version `1.7.0 <https://github.com/eclipse-sumo/sumo/commits/v1_7_0>`_ or higher.
++ Build instructions are available at `https://sumo.dlr.de/docs/Developer/index.html#build_instructions <https://sumo.dlr.de/docs/Developer/index.html#build_instructions>`_ . 
 + Please note that building SUMO might not install all other vital dependencies that SUMO requires to run.

docs/examples/base_examples.rst

@@ -51,5 +51,5 @@ Several examples are made available to help you get started with SMARTS.
 
 #. Jupyter Notebook
 
-    + script: :examples:`e9_notebook.py`
+    + script: :examples:`e9_notebook.ipynb`
     + A primitive use of SMARTS in a Jupyter Notebook.

docs/resources/contributing.rst

@@ -78,7 +78,7 @@ You are encouraged to review other people's pull requests and tag in relevant re
 Code Format
 -----------
 
-The project follows a strict format requirement for python code. We made a decision early on in the project to use `Black <https://github.com/psf/black>`_. This makes formatting consistent while eliminating `bike shedding <http://bikeshed.com/>`_.
+The project follows a strict format requirement for python code. We made a decision early on in the project to use `Black <https://github.com/psf/black>`_. This makes formatting consistent while eliminating `bike shedding <https://bikeshed.com/>`_.
 If you do not already have it please install it via ``pip install black``.
 
 Formatting guarantees that your code will pass the CI formatting test case.

docs/sim/visualization.rst

@@ -7,7 +7,7 @@ Envision
 --------
 
 SMARTS includes a front-end visualization tool called *Envision* that provides real-time view of the environment state.
-Envision is built on web-technologies (including `React <https://reactjs.org/>`_, `WebGL <https://www.khronos.org/webgl/>`_, and `websockets <https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API>`_) allowing it to run easily in the browser. 
+Envision is built on web-technologies (including `React <https://react.dev/>`_, `WebGL <https://www.khronos.org/webgl/>`_, and `websockets <https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API>`_) allowing it to run easily in the browser. 
 It is composed of a few parts: a client which SMARTS uses directly; a server used for state broadcasting; and the web application where all the visualization and rendering happens.
 
 Run
@@ -90,7 +90,7 @@ Save an updated distribution if any changes were made to the Envision web applic
 Visdom
 ------
 
-Use the `visdom <https://github.com/facebookresearch/visdom>`_ integration to easily see the image-based observation outputs in real-time. 
+Use the `visdom <https://github.com/fossasia/visdom>`_ integration to easily see the image-based observation outputs in real-time. 
 Start the visdom server before running the scenario and open the server URL in your browser `http://localhost:8097 <http://localhost:8097>`_.
 
 .. code-block:: bash

smarts/core/controllers/trajectory_tracking_controller.py

@@ -533,7 +533,8 @@ def MPC(
     ):
         """Implementation of MPC, please see the following ref:
         Convex Optimization – Boyd and Vandenberghe
-        https://markcannon.github.io/assets/downloads/teaching/C21_Model_Predictive_Control/mpc_notes.pdf
+        https://github.com/markcannon/markcannon.github.io/blob/c3344814c9c79d5f0d5f3cf8fc2e7ef14cb4fad3/assets/downloads/teaching/C21_Model_Predictive_Control/mpc_notes.pdf
+        https://markcannon.github.io/teaching/
         """
         matrix_A = np.eye(state_matrix.shape[0]) + dt * state_matrix
         matrix_B = dt * input_matrix