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34 lines
1.1 KiB
ReStructuredText
34 lines
1.1 KiB
ReStructuredText
Environments
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============
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- World
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- States
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- Actions
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- Rewards
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Available environments include ...
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How to use an environment?
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--------------------------
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Design
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------
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How to create my own environment?
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---------------------------------
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What are the differences with the OpenAI gym's environments?
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------------------------------------------------------------
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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 ...
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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.
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- Actions
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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.
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