diff --git a/docs/index.md b/docs/index.md
index 8eb2fab83c..c030981f8f 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -62,6 +62,7 @@ and how to implement new MDPs and new algorithms.
    RL2 <user/algo_rl2>
    SAC <user/algo_sac>
    TD3 <user/algo_td3>
+   TEPPO <user/algo_teppo>
    TRPO <user/algo_trpo>
    REINFORCE <user/algo_vpg>
 
diff --git a/docs/user/algo_teppo.md b/docs/user/algo_teppo.md
new file mode 100644
index 0000000000..605f8b5551
--- /dev/null
+++ b/docs/user/algo_teppo.md
@@ -0,0 +1,76 @@
+# Proximal Policy Optimization with Task Embedding (TEPPO)
+
+
+```eval_rst
+.. list-table::
+   :header-rows: 0
+   :stub-columns: 1
+   :widths: auto
+
+   * - **Paper**
+     - Learning Skill Embeddings for Transferable Robot Skills :cite:`hausman2018learning`
+   * - **Framework(s)**
+     - .. figure:: ./images/tf.png
+        :scale: 20%
+        :class: no-scaled-link
+
+        Tensorflow
+   * - **API Reference**
+     - `garage.tf.algos.TEPPO <../_autoapi/garage/tf/algos/index.html#garage.tf.algos.TEPPO>`_
+   * - **Code**
+     - `garage/tf/algos/te_ppo.py <https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_ppo.py>`_
+   * - **Examples**
+     - :ref:`te_ppo_metaworld_mt1_push`, :ref:`te_ppo_metaworld_mt10`, :ref:`te_ppo_metaworld_mt50`, :ref:`te_ppo_point`
+```
+
+Proximal Policy Optimization Algorithms (PPO) is a family of policy gradient methods which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. TEPPO parameterizes the PPO policy via a shared skill embedding space.
+
+## Default Parameters
+
+```py
+discount=0.99,
+gae_lambda=0.98,
+lr_clip_range=0.01,
+max_kl_step=0.01,
+policy_ent_coeff=1e-3,
+encoder_ent_coeff=1e-3,
+inference_ce_coeff=1e-3
+```
+
+## Examples
+
+### te_ppo_metaworld_mt1_push
+
+```eval_rst
+.. literalinclude:: ../../examples/tf/te_ppo_metaworld_mt1_push.py
+```
+
+### te_ppo_metaworld_mt10
+
+```eval_rst
+.. literalinclude:: ../../examples/tf/te_ppo_metaworld_mt10.py
+```
+
+### te_ppo_metaworld_mt50
+
+```eval_rst
+.. literalinclude:: ../../examples/tf/te_ppo_metaworld_mt50.py
+```
+
+### te_ppo_point
+
+```eval_rst
+.. literalinclude:: ../../examples/tf/te_ppo_point.py
+```
+
+## References
+
+```eval_rst
+.. bibliography:: references.bib
+   :style: unsrt
+   :filter: docname in docnames
+```
+
+----
+
+*This page was authored by Nicole Shin Ying Ng ([@nicolengsy](https://github.com/nicolengsy)).*
diff --git a/docs/user/references.bib b/docs/user/references.bib
index 740c4f71fa..87a37a1b80 100644
--- a/docs/user/references.bib
+++ b/docs/user/references.bib
@@ -83,6 +83,15 @@ @article{yu2019metaworld
     journal={arXiv:1910.10897},
 }
 
+@inproceedings{hausman2018learning,
+  title={Learning an Embedding Space for Transferable Robot Skills},
+  author={Karol Hausman and Jost Tobias Springenberg and Ziyu Wang and Nicolas Heess and Martin Riedmiller},
+  booktitle={International Conference on Learning Representations},
+  year={2018},
+  journal={},
+  url={https://openreview.net/forum?id=rk07ZXZRb},
+}
+
 @article{lillicrap2015continuous,
   title={Continuous control with deep reinforcement learning},
   author={Lillicrap, Timothy P and Hunt, Jonathan J and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},