[Docs] FIx links

docs/source/conf.py

@@ -40,8 +40,8 @@
     "python": ("https://docs.python.org/3/", None),
     "sphinx": ("https://www.sphinx-doc.org/en/master/", None),
     "torch": ("https://pytorch.org/docs/master", None),
-    "torchrl": ("https://pytorch.org/rl", None),
-    "tensordict": ("https://pytorch.org/tensordict", None),
+    "torchrl": ("https://pytorch.org/rl/stable/", None),
+    "tensordict": ("https://pytorch.org/tensordict/stable", None),
     "benchmarl": ("https://benchmarl.readthedocs.io/en/latest/", None),
 }
 intersphinx_disabled_domains = ["std"]

docs/source/usage/notebooks.rst

@@ -5,6 +5,6 @@ In the following you can find a list of  :colab:`null` Google Colab notebooks to
 
 - :colab:`null` `Using a VMAS environment <https://colab.research.google.com/github/proroklab/VectorizedMultiAgentSimulator/blob/main/notebooks/VMAS_Use_vmas_environment.ipynb>`_. Here is a simple notebook that you can run to create, step and render any scenario in VMAS. It reproduces the ``use_vmas_env.py`` script in the ``examples`` folder
 - :colab:`null` `Training VMAS in BenchMARL (suggested) <https://colab.research.google.com/github/facebookresearch/BenchMARL/blob/main/notebooks/run.ipynb>`_.  In this notebook, we show how to use VMAS in BenchMARL, TorchRL's MARL training library
-- :colab:`null` `Training VMAS in TorchRL <https://colab.research.google.com/github/pytorch/rl/blob/gh-pages/_downloads/a977047786179278d12b52546e1c0da8/multiagent_ppo.ipynb>`_. In this notebook, `available in the TorchRL docs <https://pytorch.org/rl/tutorials/multiagent_ppo.html>`_, we show how to use any VMAS scenario in TorchRL. It will guide you through the full pipeline needed to train agents using MAPPO/IPPO.
-- :colab:`null` `Training competitive VMAS MPE in TorchRL <https://colab.research.google.com/github/pytorch/rl/blob/gh-pages/_downloads/d30bb6552cc07dec0f1da33382d3fa02/multiagent_competitive_ddpg.py>`_. In this notebook, `available in the TorchRL docs <https://pytorch.org/rl/main/tutorials/multiagent_competitive_ddpg.html>`_, we show how to solve a Competitive Multi-Agent Reinforcement Learning (MARL) problem using MADDPG/IDDPG.
+- :colab:`null` `Training VMAS in TorchRL <https://colab.research.google.com/github/pytorch/rl/blob/gh-pages/_downloads/a977047786179278d12b52546e1c0da8/multiagent_ppo.ipynb>`_. In this notebook, `available in the TorchRL docs <https://pytorch.org/rl/tutorials/multiagent_ppo.html>`__, we show how to use any VMAS scenario in TorchRL. It will guide you through the full pipeline needed to train agents using MAPPO/IPPO.
+- :colab:`null` `Training competitive VMAS MPE in TorchRL <https://colab.research.google.com/github/pytorch/rl/blob/gh-pages/_downloads/d30bb6552cc07dec0f1da33382d3fa02/multiagent_competitive_ddpg.py>`_. In this notebook, `available in the TorchRL docs <https://pytorch.org/rl/main/tutorials/multiagent_competitive_ddpg.html>`__, we show how to solve a Competitive Multi-Agent Reinforcement Learning (MARL) problem using MADDPG/IDDPG.
 - :colab:`null` `Training VMAS in RLlib <https://colab.research.google.com/github/proroklab/VectorizedMultiAgentSimulator/blob/main/notebooks/VMAS_RLlib.ipynb>`_. In this notebook, we show how to use any VMAS scenario in RLlib. It reproduces the ``rllib.py`` script in the ``examples`` folder.