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docs/source/environments.rst

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+Environments
+============
+
+- World
+- States
+- Actions
+- Rewards
+
+Available environments include ...
+
+
+How to use an environment?
+--------------------------
+
+
+Design
+------
+
+
+How to create my own environment?
+---------------------------------
+
+
+What are the differences with the OpenAI gym's environments?
+------------------------------------------------------------
+
+To better depict the differences, let's consider an environment which contains a quadruped robot and the goal is that it learns to walk. Usually, as it can be seen on multiple repositories, people would inherit from the gym ``Env`` class and call it something similar to ``QuadrupedFlatTerrainWalkEnv(Env)``. Inside of ``step`` function, they would compute the next states and rewards. Now suppose, you would like to change ...
+
+In our framework, the ``world``, ``states``, and ``rewards`` are given to the PRL ``Env`` class. This means that if you would like to change the world, reward function, or states you can do it outside the function.
+
+- Actions
+
+Having said that, we tried to make PRL compatible with OpenAI gym at the exception that the returned state is not a array but a list of arrays.