diff --git a/pyrobolearn/samplers/README.md b/pyrobolearn/samplers/README.md
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+## Samplers
+
+This folder contains the various samplers used to sample batches from the storages. The sampler is tightly coupled 
+with the `Storage` classes.
+
+## What to look/check next?
+
+Have a look at the `storages` folder.
diff --git a/pyrobolearn/samplers/__init__.py b/pyrobolearn/samplers/__init__.py
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+
+# import samplers
+from .sampler import *
diff --git a/pyrobolearn/samplers/sampler.py b/pyrobolearn/samplers/sampler.py
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+#!/usr/bin/env python
+"""Provide the Sampler class from which all sampling strategies inherit from.
+
+Sampling strategies can be classified into two categories: probability or deterministic sampling methods.
+
+Deterministic sampling methods include: convenience sampling, purposive sampling, quota sampling, and
+referral/snowball sampling.
+
+Probability sampling methods include: random sampling, stratified sampling, systematic sampling, cluster sampling,
+multi-stage sampling, importance sampling, MCMC sampling, etc.
+
+Others include maximum variation sampling, extreme case sampling, etc.
+
+References:
+    [1] https://towardsdatascience.com/sampling-techniques-a4e34111d808
+    [2] https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/sampling-in-statistics/
+    [3] https://pytorch.org/docs/stable/_modules/torch/utils/data/dataset.html
+    [4] https://pytorch.org/docs/stable/_modules/torch/utils/data/sampler.html
+"""
+
+import torch.utils.data.sampler as torch_sampler
+import torch.utils.data.dataset as torch_dataset
+
+from pyrobolearn.storages import RolloutStorage
+
+__author__ = "Brian Delhaisse"
+__copyright__ = "Copyright 2018, PyRoboLearn"
+__credits__ = ["Brian Delhaisse"]
+__license__ = "MIT"
+__version__ = "1.0.0"
+__maintainer__ = "Brian Delhaisse"
+__email__ = "briandelhaisse@gmail.com"
+__status__ = "Development"
+
+
+class Sampler(object):
+    pass
+
+
+class RandomSampler(Sampler):
+    pass
+
+
+class StorageSampler(Sampler):
+    r"""Storage sampler
+
+    Sampler used with the storage.
+    """
+
+    def __init__(self, storage, sampler=None, num_batches=10):
+        """
+        Initialize the storage sampler.
+
+        Args:
+            storage (RolloutStorage): rollout storage.
+            sampler (Sampler, None): If None, it will use a sampler that randomly sample batches of the storage. It
+                will by default sample :attr:`num_batches`.
+            num_batches (int): number of batches
+        """
+        # set the storage
+        self.storage = storage
+
+        # set the sampler
+        if sampler is None:
+            batch_size = self.size // num_batches
+            if batch_size > self.size:
+                raise ValueError("Expecting the batch size (={}) to be smaller than the size of the storage (={})"
+                                 ".".format(batch_size, self.size))
+            sampler = torch_sampler.BatchSampler(sampler=torch_sampler.SubsetRandomSampler(range(self.size)),
+                                                 batch_size=batch_size, drop_last=False)
+        self.sampler = sampler
+
+    ##############
+    # Properties #
+    ##############
+
+    @property
+    def storage(self):
+        """Return the storage instance."""
+        return self._storage
+
+    @storage.setter
+    def storage(self, storage):
+        """Set the storage instance."""
+        if not isinstance(storage, RolloutStorage):
+            raise TypeError("Expecting the storage to be an instance of `RolloutStorage`, instead got: "
+                            "{}".format(type(storage)))
+        self._storage = storage
+
+    @property
+    def size(self):
+        """Return the total size of the storage number of time steps * number of processes."""
+        return self.storage.size
+
+    @property
+    def sampler(self):
+        """Return the sampler."""
+        return self._sampler
+
+    @sampler.setter
+    def sampler(self, sampler):
+        """Set the sampler."""
+        if not isinstance(sampler, (torch_sampler.Sampler, Sampler)):
+            raise TypeError("Expecting the sampler to be an instance of `torch.utils.data.sampler` or `Sampler`, "
+                            "instead got: {}".format(type(sampler)))
+        self._sampler = sampler
+
+    @property
+    def batch_size(self):
+        """Return the number of batches."""
+        return self.sampler.batch_size
+
+    @batch_size.setter
+    def batch_size(self, size):
+        """Set the batch size."""
+        if size > self.size:
+            raise ValueError("Expecting the batch size (={}) to be smaller than the size of the storage (={})"
+                             ".".format(size, self.size))
+        self.sampler.batch_size = size
+
+    @property
+    def num_batches(self):
+        """Return the number of batches (based on the size of the storage and the batch size)."""
+        return self.size // self.batch_size
+
+    @num_batches.setter
+    def num_batches(self, num_batches):
+        """Set the number of batches."""
+        self.batch_size = self.size // num_batches
+
+    ###########
+    # Methods #
+    ###########
+
+    def generator(self, num_batches):
+        """Return a generator.
+
+        Args:
+            num_batches (int): number of batches
+        """
+        self.batch_size = self.size // num_batches
+
+        for indices in self.sampler:
+            yield self.storage.get_batch(indices)
+
+    def __iter__(self):
+        """Iterate over the storage."""
+        for indices in self.sampler:
+            yield self.storage.get_batch(indices)