diff --git a/docs/Background-PyTorch.md b/docs/Background-PyTorch.md
index f63a066745..b78e77c558 100644
--- a/docs/Background-PyTorch.md
+++ b/docs/Background-PyTorch.md
@@ -24,7 +24,7 @@ One component of training models with PyTorch is setting the values of
 certain model attributes (called _hyperparameters_). Finding the right values of
 these hyperparameters can require a few iterations. Consequently, we leverage a
 visualization tool called
-[TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
+[TensorBoard](https://www.tensorflow.org/tensorboard).
 It allows the visualization of certain agent attributes (e.g. reward) throughout
 training which can be helpful in both building intuitions for the different
 hyperparameters and setting the optimal values for your Unity environment. We
diff --git a/docs/Using-Tensorboard.md b/docs/Using-Tensorboard.md
index feaf800fe8..d1bf469e91 100644
--- a/docs/Using-Tensorboard.md
+++ b/docs/Using-Tensorboard.md
@@ -2,7 +2,7 @@
 
 The ML-Agents Toolkit saves statistics during learning session that you can view
 with a TensorFlow utility named,
-[TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
+[TensorBoard](https://www.tensorflow.org/tensorboard).
 
 The `mlagents-learn` command saves training statistics to a folder named
 `results`, organized by the `run-id` value you assign to a training session.
diff --git a/markdown-link-check.full.json b/markdown-link-check.full.json
index 57f2875228..bf6adeaa8b 100644
--- a/markdown-link-check.full.json
+++ b/markdown-link-check.full.json
@@ -23,6 +23,10 @@
         {
             "pattern": "https://www.researchgate.net/",
             "comment": "Issue with GHE certs / firewall"
+        },
+        {
+            "pattern": "https://www.tensorflow.org/",
+            "comment": "Valid links failing with errors like 'https://www.tensorflow.org/tensorboard → Status: 0 Error: Exceeded maxRedirects. Probably stuck in a redirect loop'"
         }
     ]
 }