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66 lines
3.6 KiB
ReStructuredText
66 lines
3.6 KiB
ReStructuredText
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RLlib Development
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=================
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Features
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--------
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Feature development and upcoming priorities are tracked on the `RLlib project board <https://github.com/ray-project/ray/projects/6>`__ (note that this may not include all development efforts). For discussion of issues and new features, we use the `Ray dev list <https://groups.google.com/forum/#!forum/ray-dev>`__ and `GitHub issues page <https://github.com/ray-project/ray/issues>`__.
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Benchmarks
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----------
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Currently we host a number of full training run results in the `rl-experiments repo <https://github.com/ray-project/rl-experiments>`__, and maintain a list of working hyperparameter configurations in `tuned_examples <https://github.com/ray-project/ray/tree/master/python/ray/rllib/tuned_examples>`__. Benchmark results are extremely valuable to the community, so if you happen to have results that may be of interest, consider making a pull request to either repo.
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Contributing Algorithms
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-----------------------
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These are the guidelines for merging new algorithms into RLlib:
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* Contributed algorithms (`rllib/contrib <https://github.com/ray-project/ray/tree/master/python/ray/rllib/contrib>`__):
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- must subclass Agent and implement the ``_train()`` method
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- must include a lightweight test (<30s to run) to sanity check functionality
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- should include tuned hyperparameter examples and documentation
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- should offer functionality not present in existing algorithms
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* Fully integrated algorithms (`rllib/agents <https://github.com/ray-project/ray/tree/master/python/ray/rllib/agents>`__) have the following additional requirements:
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- must fully implement the Agent API
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- must offer substantial new functionality not possible to add to other algorithms
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- should support custom models and preprocessors
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- should use RLlib abstractions and support distributed execution
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Both integrated and contributed algorithms ship with the ``ray`` PyPI package, and are tested as part of Ray's automated tests. The main difference between contributed and fully integrated algorithms is that the latter will be maintained by the Ray team to a much greater extent with respect to bugs and integration with RLlib features.
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How to add an algorithm to ``contrib``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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It takes just two changes to add an algorithm to `contrib <https://github.com/ray-project/ray/tree/master/python/ray/rllib/contrib>`__. A minimal example can be found `here <https://github.com/ray-project/ray/tree/master/python/ray/rllib/contrib/random_agent/random_agent.py>`__. First, subclass `Agent <https://github.com/ray-project/ray/tree/master/python/ray/rllib/agents/agent.py>`__ and implement the ``_init`` and ``_train`` methods:
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.. literalinclude:: ../../python/ray/rllib/contrib/random_agent/random_agent.py
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:language: python
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:start-after: __sphinx_doc_begin__
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:end-before: __sphinx_doc_end__
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Second, register the agent with a name in `contrib/registry.py <https://github.com/ray-project/ray/blob/master/python/ray/rllib/contrib/registry.py>`__.
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.. code-block:: python
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def _import_random_agent():
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from ray.rllib.contrib.random_agent.random_agent import RandomAgent
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return RandomAgent
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def _import_random_agent_2():
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from ray.rllib.contrib.random_agent_2.random_agent_2 import RandomAgent2
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return RandomAgent
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CONTRIBUTED_ALGORITHMS = {
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"contrib/RandomAgent": _import_random_agent,
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"contrib/RandomAgent2": _import_random_agent_2,
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# ...
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}
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After registration, you can run and visualize agent progress using ``rllib train``:
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.. code-block:: bash
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rllib train --run=contrib/RandomAgent --env=CartPole-v0
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tensorboard --logdir=~/ray_results
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