diff --git a/movement_primitives/promp.py b/movement_primitives/promp.py
index e5e3c05..f25baae 100644
--- a/movement_primitives/promp.py
+++ b/movement_primitives/promp.py
@@ -9,8 +9,9 @@
 class ProMP:
     """Probabilistic Movement Primitive (ProMP).
 
-    ProMPs have been proposed first in [1] and have been used later in [2,3].
-    The learning algorithm is a specialized form of the one presented in [4].
+    ProMPs have been proposed first in [1]_ and have been used later in [2]_,
+    [3]_. The learning algorithm is a specialized form of the one presented in
+    [4]_.
 
     Note that internally we represented trajectories with the task space
     dimension as the first axis and the time step as the second axis while
@@ -28,27 +29,27 @@ class ProMP:
 
     References
     ----------
-    [1] Paraschos, A., Daniel, C., Peters, J., Neumann, G. (2013).
-    Probabilistic movement primitives, In C.J. Burges and L. Bottou and M.
-    Welling and Z. Ghahramani and K.Q. Weinberger (Eds.), Advances in Neural
-    Information Processing Systems, 26,
-    https://papers.nips.cc/paper/2013/file/e53a0a2978c28872a4505bdb51db06dc-Paper.pdf
-
-    [3] Maeda, G. J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O.,
-    Peters, J. (2017). Probabilistic movement primitives for coordination of
-    multiple human–robot collaborative tasks. Autonomous Robots, 41, 593-612.
-    DOI: 10.1007/s10514-016-9556-2,
-    https://link.springer.com/article/10.1007/s10514-016-9556-2
-
-    [2] Paraschos, A., Daniel, C., Peters, J., Neumann, G. (2018).
-    Using probabilistic movement primitives in robotics. Autonomous Robots, 42,
-    529-551. DOI: 10.1007/s10514-017-9648-7,
-    https://www.ias.informatik.tu-darmstadt.de/uploads/Team/AlexandrosParaschos/promps_auro.pdf
-
-    [4] Lazaric, A., Ghavamzadeh, M. (2010).
-    Bayesian Multi-Task Reinforcement Learning. In Proceedings of the 27th
-    International Conference on International Conference on Machine Learning
-    (ICML'10) (pp. 599-606). https://hal.inria.fr/inria-00475214/document
+    .. [1] Paraschos, A., Daniel, C., Peters, J., Neumann, G. (2013).
+       Probabilistic movement primitives, In C.J. Burges and L. Bottou and M.
+       Welling and Z. Ghahramani and K.Q. Weinberger (Eds.), Advances in Neural
+       Information Processing Systems, 26,
+       https://papers.nips.cc/paper/2013/file/e53a0a2978c28872a4505bdb51db06dc-Paper.pdf
+
+    .. [3] Maeda, G. J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O.,
+       Peters, J. (2017). Probabilistic movement primitives for coordination of
+       multiple human–robot collaborative tasks. Autonomous Robots, 41, 593-612.
+       DOI: 10.1007/s10514-016-9556-2,
+       https://link.springer.com/article/10.1007/s10514-016-9556-2
+
+    .. [2] Paraschos, A., Daniel, C., Peters, J., Neumann, G. (2018).
+       Using probabilistic movement primitives in robotics. Autonomous Robots,
+       42, 529-551. DOI: 10.1007/s10514-017-9648-7,
+       https://www.ias.informatik.tu-darmstadt.de/uploads/Team/AlexandrosParaschos/promps_auro.pdf
+
+    .. [4] Lazaric, A., Ghavamzadeh, M. (2010).
+       Bayesian Multi-Task Reinforcement Learning. In Proceedings of the 27th
+       International Conference on International Conference on Machine Learning
+       (ICML'10) (pp. 599-606). https://hal.inria.fr/inria-00475214/document
     """
     def __init__(self, n_dims, n_weights_per_dim=10):
         self.n_dims = n_dims