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[RLlib] Attention Net/Transformers docs improvement.
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@@ -5,40 +5,42 @@ RLlib Algorithms
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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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Available Algorithms - Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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=================== ========== ======================= ================== =========== =====================
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=================== ========== ======================= ================== =========== =============================================================
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Algorithm Frameworks Discrete Actions Continuous Actions Multi-Agent Model Support
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=================== ========== ======================= ================== =========== =====================
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`A2C, A3C`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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=================== ========== ======================= ================== =========== =============================================================
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`A2C, A3C`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
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`ARS`_ tf + torch **Yes** **Yes** No
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`ES`_ tf + torch **Yes** **Yes** No
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`DDPG`_, `TD3`_ tf + torch No **Yes** **Yes**
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`APEX-DDPG`_ tf + torch No **Yes** **Yes**
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`DQN`_, `Rainbow`_ tf + torch **Yes** `+parametric`_ No **Yes**
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`APEX-DQN`_ tf + torch **Yes** `+parametric`_ No **Yes**
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`IMPALA`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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`IMPALA`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
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`MAML`_ tf + torch No **Yes** No
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`MARWIL`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_
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`PG`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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`PPO`_, `APPO`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
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`PG`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
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`PPO`_, `APPO`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
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`QMIX`_ torch **Yes** `+parametric`_ No **Yes** `+RNN`_
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`SAC`_ tf + torch **Yes** **Yes** **Yes**
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------------------- ---------- ----------------------- ------------------ ----------- ---------------------
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------------------- ---------- ----------------------- ------------------ ----------- -------------------------------------------------------------
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`AlphaZero`_ torch **Yes** `+parametric`_ No No
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`LinUCB`_, `LinTS`_ torch **Yes** `+parametric`_ No **Yes**
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`MADDPG`_ tf **Yes** Partial **Yes**
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=================== ========== ======================= ================== =========== =====================
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=================== ========== ======================= ================== =========== =============================================================
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.. _`+autoreg`: rllib-models.html#autoregressive-action-distributions
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.. _`+LSTM auto-wrapping`: rllib-models.html#built-in-models
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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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.. _`+Transformer`: rllib-models.html#attention-networks
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.. _`A2C, A3C`: rllib-algorithms.html#a3c
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.. _`Rainbow`: rllib-algorithms.html#dqn
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.. _`TD3`: rllib-algorithms.html#ddpg
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.. _`APEX-DQN`: rllib-algorithms.html#apex
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.. _`APEX-DDPG`: rllib-algorithms.html#apex
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.. _`Rainbow`: rllib-algorithms.html#dqn
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.. _`TD3`: rllib-algorithms.html#ddpg
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High-throughput architectures
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@@ -156,6 +156,13 @@ You can check out the `rnn_model.py <https://github.com/ray-project/ray/blob/mas
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.. automethod:: forward_rnn
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.. automethod:: get_initial_state
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Attention Networks/Transformers
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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RLlib now also has experimental built-in support for attention/transformer nets (the GTrXL model in particular).
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Here is `an example script <https://github.com/ray-project/ray/blob/master/rllib/examples/attention_net.py>`__ on how to use these with some of our algorithms.
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`There is also a test case <https://github.com/ray-project/ray/blob/master/rllib/tests/test_attention_net_learning.py>`__, which confirms their learning capabilities in PPO and IMPALA.
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Batch Normalization
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~~~~~~~~~~~~~~~~~~~
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