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* pong on pixels working (not cleaned up) * make training compatible with all atari games * cartpole runs * Update documentation and usage for policy gradients.
34 lines
1.1 KiB
ReStructuredText
34 lines
1.1 KiB
ReStructuredText
Policy Gradient Methods
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=======================
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This code shows how to do reinforcement learning with policy gradient methods.
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View the `code for this example`_.
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To run this example, you will need to install `TensorFlow with GPU support`_ (at
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least version ``1.0.0``) and a few other dependencies.
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.. code-block:: bash
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pip install gym[atari]
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pip install tensorflow
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Then install the package as follows.
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.. code-block:: bash
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cd ray/examples/policy_gradient/
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python setup.py install
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Then you can run the example as follows.
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.. code-block:: bash
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python ray/examples/policy_gradient/examples/example.py --environment=Pong-ram-v3
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This will train an agent on the ``Pong-ram-v3`` Atari environment. You can also
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try passing in the ``Pong-v0`` environment or the ``CartPole-v0`` environment.
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If you wish to use a different environment, you will need to change a few lines
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in ``example.py``.
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.. _`TensorFlow with GPU support`: https://www.tensorflow.org/install/
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.. _`code for this example`: https://github.com/ray-project/ray/tree/master/examples/policy_gradient
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