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[rllib] Rename sample_batch_size => rollout_fragment_length (#7503)
* bulk rename * deprecation warn * update doc * update fig * line length * rename * make pytest comptaible * fix test * fi sys * rename * wip * fix more * lint * update svg * comments * lint * fix use of batch steps
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@@ -1,6 +1,35 @@
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RLlib Algorithms
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================
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.. tip::
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Check out the `environments <rllib-env.html>`__ page to learn more about different environment types.
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Feature Compatibility Matrix
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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============= ======================= ================== =========== ===========================
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Algorithm Discrete Actions Continuous Multi-Agent Model Support
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============= ======================= ================== =========== ===========================
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A2C, A3C **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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PPO, APPO **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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PG **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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IMPALA **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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DQN, Rainbow **Yes** `+parametric`_ No **Yes**
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DDPG, TD3 No **Yes** **Yes**
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APEX-DQN **Yes** `+parametric`_ No **Yes**
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APEX-DDPG No **Yes** **Yes**
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SAC **Yes** **Yes** **Yes**
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ES **Yes** **Yes** No
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ARS **Yes** **Yes** No
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QMIX **Yes** No **Yes** `+RNN`_
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MARWIL **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_
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============= ======================= ================== =========== ===========================
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.. _`+parametric`: rllib-models.html#variable-length-parametric-action-spaces
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.. _`+RNN`: rllib-models.html#recurrent-models
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.. _`+autoreg`: rllib-models.html#autoregressive-action-distributions
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High-throughput architectures
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@@ -321,7 +350,7 @@ Soft Actor Critic (SAC)
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SAC architecture (same as DQN)
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RLlib's soft-actor critic implementation is ported from the `official SAC repo <https://github.com/rail-berkeley/softlearning>`__ to better integrate with RLlib APIs. Note that SAC has two fields to configure for custom models: ``policy_model`` and ``Q_model``, and currently has no support for non-continuous action distributions.
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RLlib's soft-actor critic implementation is ported from the `official SAC repo <https://github.com/rail-berkeley/softlearning>`__ to better integrate with RLlib APIs. Note that SAC has two fields to configure for custom models: ``policy_model`` and ``Q_model``.
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Tuned examples: `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/regression_tests/pendulum-sac.yaml>`__, `HalfCheetah-v3 <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/halfcheetah-sac.yaml>`__
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