[RLlib] Attention Net/Transformers docs improvement.

This commit is contained in:
Sven Mika
2020-08-17 22:07:17 +02:00
committed by GitHub
parent ca133e2699
commit fe0bdb23ff
2 changed files with 22 additions and 13 deletions
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@@ -5,40 +5,42 @@ RLlib Algorithms
Check out the `environments <rllib-env.html>`__ page to learn more about different environment types.
Feature Compatibility Matrix
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Available Algorithms - Overview
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=================== ========== ======================= ================== =========== =====================
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Algorithm Frameworks Discrete Actions Continuous Actions Multi-Agent Model Support
=================== ========== ======================= ================== =========== =====================
`A2C, A3C`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
=================== ========== ======================= ================== =========== =============================================================
`A2C, A3C`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
`ARS`_ tf + torch **Yes** **Yes** No
`ES`_ tf + torch **Yes** **Yes** No
`DDPG`_, `TD3`_ tf + torch No **Yes** **Yes**
`APEX-DDPG`_ tf + torch No **Yes** **Yes**
`DQN`_, `Rainbow`_ tf + torch **Yes** `+parametric`_ No **Yes**
`APEX-DQN`_ tf + torch **Yes** `+parametric`_ No **Yes**
`IMPALA`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
`IMPALA`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
`MAML`_ tf + torch No **Yes** No
`MARWIL`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_
`PG`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
`PPO`_, `APPO`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
`PG`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
`PPO`_, `APPO`_ tf + torch **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+LSTM auto-wrapping`_, `+Transformer`_, `+autoreg`_
`QMIX`_ torch **Yes** `+parametric`_ No **Yes** `+RNN`_
`SAC`_ tf + torch **Yes** **Yes** **Yes**
------------------- ---------- ----------------------- ------------------ ----------- ---------------------
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`AlphaZero`_ torch **Yes** `+parametric`_ No No
`LinUCB`_, `LinTS`_ torch **Yes** `+parametric`_ No **Yes**
`MADDPG`_ tf **Yes** Partial **Yes**
=================== ========== ======================= ================== =========== =====================
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.. _`+autoreg`: rllib-models.html#autoregressive-action-distributions
.. _`+LSTM auto-wrapping`: rllib-models.html#built-in-models
.. _`+parametric`: rllib-models.html#variable-length-parametric-action-spaces
.. _`+RNN`: rllib-models.html#recurrent-models
.. _`+autoreg`: rllib-models.html#autoregressive-action-distributions
.. _`+Transformer`: rllib-models.html#attention-networks
.. _`A2C, A3C`: rllib-algorithms.html#a3c
.. _`Rainbow`: rllib-algorithms.html#dqn
.. _`TD3`: rllib-algorithms.html#ddpg
.. _`APEX-DQN`: rllib-algorithms.html#apex
.. _`APEX-DDPG`: rllib-algorithms.html#apex
.. _`Rainbow`: rllib-algorithms.html#dqn
.. _`TD3`: rllib-algorithms.html#ddpg
High-throughput architectures
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@@ -156,6 +156,13 @@ You can check out the `rnn_model.py <https://github.com/ray-project/ray/blob/mas
.. automethod:: forward_rnn
.. automethod:: get_initial_state
Attention Networks/Transformers
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RLlib now also has experimental built-in support for attention/transformer nets (the GTrXL model in particular).
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.
`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.
Batch Normalization
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