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Add documentation on distributed Coach. (#158)
* Added documentation on distributed Coach.
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Gal Novik
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@@ -1,7 +1,7 @@
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Usage
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=====
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One of the mechanism Coach uses for running experiments is the **Preset** mechanism.
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One of the mechanisms Coach uses for running experiments is the **Preset** mechanism.
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As its name implies, a preset defines a set of predefined experiment parameters.
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This allows defining a *complex* agent-environment interaction, with multiple parameters, and later running it through
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a very *simple* command line.
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@@ -29,7 +29,7 @@ To list the available presets, use the `-l` flag.
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Multi-threaded Algorithms
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+++++++++++++++++++++++++
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Multi-threaded algorithms are very common this days.
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Multi-threaded algorithms are very common these days.
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They typically achieve the best results, and scale gracefully with the number of threads.
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In Coach, running such algorithms is done by selecting a suitable preset, and choosing the number of threads to run using the :code:`-n` flag.
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@@ -39,6 +39,20 @@ In Coach, running such algorithms is done by selecting a suitable preset, and ch
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coach -p CartPole_A3C -n 8
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Multi-Node Algorithms
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+++++++++++++++++++++++++
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Coach supports the multi-node runs in distributed mode. Specifically, the horizontal scale-out of rollout workers is implemented.
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In Coach, running such algorithms is done by selecting a suitable preset, enabling distributed coach using :code:`-dc` flag,
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passing distributed coach parameters using :code:`dcp` and choosing the number of to run using the :code:`-n` flag.
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For more details and instructions on how to use distributed Coach, see :ref:`dist-coach-usage`.
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*Example:*
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.. code-block:: python
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coach -p CartPole_ClippedPPO -dc -dcp <path-to-config-file> -n 8
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Evaluating an Agent
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-------------------
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@@ -155,4 +169,4 @@ The most up to date description can be found by using the :code:`-h` flag.
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.. argparse::
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:module: rl_coach.coach
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:func: create_argument_parser
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:prog: coach
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:prog: coach
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