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+## States
+
+In this folder, you will find some examples on how to use the `pyrobolearn.states.*` states.
+
+States are classes defined outside the environment and are basically containers. They can be updated by calling them `state()` (same as `state.read()`), or by setting their `data` variable.
+
+They can be combined using the addition operator. For instance, `s_a = s1 + s2`, `s_b = s2 + s3`, and `s=s_a + s_b`. Calling `s_a()` will update the states `s1` and `s2`, and thus the data contained in `s_b` as well (as it contains a pointer to `s2` which has been updated). You can just call `s()` to update in one loop `s1, s2, s3` altogether (`s_a` and `s_b` will reflect that change because they contain a pointer to these states `s1, s2, s3`).
+
+States are given to the policy and the environment. The environment is responsible to update them while policies read their `data` and feed it to the underlying learning model. In the case we use a physics simulator like PyBullet, the environment performs one step in the simulation and calls the `states()` which updates the `data` they contained. Instead, if you have a dynamical model function, the environment can call this one to update the `data` of the various `states` without having to call the `states()` itself to update their values.
+
+States can also be given to dynamical models (which predicts the next state given the current state and last action), value function approximators (which predicts a scalar value given a state and possibly an action), reward functions, etc.
+
+
+### Simple Example
+
+```python
+import pyrobolearn.states as states
+
+s1 = states.CumulativeTimeState()
+s2 = states.AbsoluteTimeState()
+s = s1 + s2
+print(s)
+
+# update s1
+s1.read()  # or s1()
+print(s1)
+print(s)  # just s1 changed
+
+# update s1 and s2 by calling s
+s()
+print(s)
+print(s1)
+print(s2)
+
+# get the data
+print(s.data)  # this will return a list of 2 arrays; each one of shape (1,). The size of the list is equal to the number of states that it contains
+print(s1.data)  # this will return a list with one array of shape (1,)
+print(s.merged_data)  # this will return a merged state; it will merge the states that have the same dimensions together and return a list of arrays which has a size equal to the number of different dimensions. The arrays inside that list are ordered by their dimensionality in an ascending way.
+```
+
+## What to test first?
+
+Try to launch `basics.py` first.
+
+
+## For the programmer
+
+Why states are defined outside and not inside the environments like usually done in `gym.envs`. States are defined outside for a better modularity, reusability, flexibility, and lower coupling. 
+* Why better modularity? Because you define a module for each possible state which you can combine at your taste later on. 
+* Why better reusability? Because it avoids you to define how to read similar state in different environments which often lead to code duplication. 
+* Why lower coupling? Coupling between two modules measures how much they are dependent on each other. There is a lower coupling, because instead of having a composition relationship between the modules we have an aggregation relationship (see [UML Association vs Aggregation vs Composition](https://www.visual-paradigm.com/guide/uml-unified-modeling-language/uml-aggregation-vs-composition/)). That is, because states are defined outside of the environment and given to the environment, even if we destroyed the environment, the states still exist.
+* Why better flexibility? Because we favor [composition over inheritance](https://en.wikipedia.org/wiki/Composition_over_inheritance). you can combine different states as you wish, give different states to different policies, and provide them at the end to the environment (which will update them). 
diff --git a/examples/states/basics.py b/examples/states/basics.py
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+#!/usr/bin/env python
+"""Test different states
+"""
+
+import pyrobolearn.states as states
+
+s1 = states.CumulativeTimeState()
+s2 = states.AbsoluteTimeState()
+s = s1 + s2
+
+print("\ns = s1 + s2 = {}".format(s))
+
+# update s1
+print("\nUpdating state s1 by calling s1():")
+s1.read()  # or s1()
+print("s after calling s1() = {}".format(s))
+print("s1 after calling s1() = {}".format(s1))
+print("s2 after calling s1() = {}".format(s2))
+
+# update s1 and s2 by calling s
+print("\nUpdating the combined state s=(s1+s2) by calling s():")
+s()
+print("s after calling s() = {}".format(s))
+print("s1 after calling s() = {}".format(s1))
+print("s2 after calling s() = {}".format(s2))
+
+# get the data
+print("\nGetting the state data:")
+
+# this will return a list of 2 arrays; each one of shape (1,). The size of the list 
+# is equal to the number of states that it contains
+print("s.data = {}".format(s.data))
+
+# this will return a list with one array of shape (1,).
+print("s1.data = {}".format(s1.data))
+
+# this will return a merged state; it will merge the states that have the same dimensions 
+# together and return a list of arrays which has a size equal to the number of different 
+# dimensions. The arrays inside that list are ordered by their dimensionality in an 
+# ascending way.
+print("s.merged_data = {}".format(s.merged_data))
+
+# this will return the same as s1.data because it just have one state
+print("s1.merged_data = {}\n".format(s1.merged_data))