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[RLlib] Add pytorch sigils to toc and add links to algo overview table. (#7950)

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* Add torch sigils to toc-tree for DQN/APEX.

* WIP.
This commit is contained in:
Sven Mika
2020-04-09 10:40:18 -07:00
committed by GitHub
parent 59867dad75
commit d2b5c171cb
4 changed files with 28 additions and 22 deletions
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doc/source/rllib-algorithms.rst
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@@ -8,27 +8,34 @@ RLlib Algorithms
Feature Compatibility Matrix
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
============= ======================= ================== =========== ===========================
Algorithm Discrete Actions Continuous Multi-Agent Model Support
============= ======================= ================== =========== ===========================
A2C, A3C **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
PPO, APPO **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
PG **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
IMPALA **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
DQN, Rainbow **Yes** `+parametric`_ No **Yes**
DDPG, TD3 No **Yes** **Yes**
APEX-DQN **Yes** `+parametric`_ No **Yes**
APEX-DDPG No **Yes** **Yes**
SAC **Yes** **Yes** **Yes**
ES **Yes** **Yes** No
ARS **Yes** **Yes** No
QMIX **Yes** No **Yes** `+RNN`_
MARWIL **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_
============= ======================= ================== =========== ===========================
=================== ======================= ============== =========== =====================
Algorithm Discrete Actions Continuous Multi-Agent Model Support
=================== ======================= ============== =========== =====================
`A2C, A3C`_ **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
`PPO`_, `APPO`_ **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
`PG`_ **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
`IMPALA`_ **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_, `+autoreg`_
`DQN`_, `Rainbow`_ **Yes** `+parametric`_ No **Yes**
`DDPG`_, `TD3`_ No **Yes** **Yes**
`APEX-DQN`_ **Yes** `+parametric`_ No **Yes**
`APEX-DDPG`_ No **Yes** **Yes**
`SAC`_ **Yes** **Yes** **Yes**
`ES`_ **Yes** **Yes** No
`ARS`_ **Yes** **Yes** No
`QMIX`_ **Yes** No **Yes** `+RNN`_
`MARWIL`_ **Yes** `+parametric`_ **Yes** **Yes** `+RNN`_
`LinUCB`_, `LinTS`_ **Yes** `+parametric`_ No **Yes**
`AlphaZero`_ **Yes** `+parametric`_ No No
=================== ======================= ============== =========== =====================
.. _`+parametric`: rllib-models.html#variable-length-parametric-action-spaces
.. _`+RNN`: rllib-models.html#recurrent-models
.. _`+autoreg`: rllib-models.html#autoregressive-action-distributions
.. _`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
High-throughput architectures
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
doc/source/rllib-toc.rst
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@@ -87,7 +87,7 @@ Algorithms
* High-throughput architectures
- |tensorflow| :ref:`Distributed Prioritized Experience Replay (Ape-X) <apex>`
- |pytorch| |tensorflow| :ref:`Distributed Prioritized Experience Replay (Ape-X) <apex>`
- |tensorflow| :ref:`Importance Weighted Actor-Learner Architecture (IMPALA) <impala>`
@@ -103,7 +103,7 @@ Algorithms
- |tensorflow| :ref:`Deep Deterministic Policy Gradients (DDPG, TD3) <ddpg>`
- |tensorflow| :ref:`Deep Q Networks (DQN, Rainbow, Parametric DQN) <dqn>`
- |pytorch| |tensorflow| :ref:`Deep Q Networks (DQN, Rainbow, Parametric DQN) <dqn>`
- |pytorch| |tensorflow| :ref:`Policy Gradients <pg>`