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[rllib] example and docs on how to use parametric actions with DQN / PG algorithms (#3384)

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Eric Liang
2018-11-27 23:35:19 -08:00
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doc/source/rllib-env.rst
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@@ -7,20 +7,22 @@ RLlib works with several different types of environments, including `OpenAI Gym
**Compatibility matrix**:
============= ================ ================== =========== ==================
Algorithm Discrete Actions Continuous Actions Multi-Agent Recurrent Policies
============= ================ ================== =========== ==================
A2C, A3C **Yes** **Yes** **Yes** **Yes**
PPO **Yes** **Yes** **Yes** **Yes**
PG **Yes** **Yes** **Yes** **Yes**
IMPALA **Yes** No **Yes** **Yes**
DQN, Rainbow **Yes** No **Yes** No
DDPG, TD3 No **Yes** **Yes** No
APEX-DQN **Yes** No **Yes** No
APEX-DDPG No **Yes** **Yes** No
ES **Yes** **Yes** No No
ARS **Yes** **Yes** No No
============= ================ ================== =========== ==================
============= ======================= ================== =========== ==================
Algorithm Discrete Actions Continuous Actions Multi-Agent Recurrent Policies
============= ======================= ================== =========== ==================
A2C, A3C **Yes** `+parametric`_ **Yes** **Yes** **Yes**
PPO **Yes** `+parametric`_ **Yes** **Yes** **Yes**
PG **Yes** `+parametric`_ **Yes** **Yes** **Yes**
IMPALA **Yes** `+parametric`_ No **Yes** **Yes**
DQN, Rainbow **Yes** `+parametric`_ No **Yes** No
DDPG, TD3 No **Yes** **Yes** No
APEX-DQN **Yes** `+parametric`_ No **Yes** No
APEX-DDPG No **Yes** **Yes** No
ES **Yes** **Yes** No No
ARS **Yes** **Yes** No No
============= ======================= ================== =========== ==================
.. _`+parametric`: rllib-models.html#variable-length-parametric-action-spaces
In the high-level agent APIs, environments are identified with string names. By default, the string will be interpreted as a gym `environment name <https://gym.openai.com/envs>`__, however you can also register custom environments by name: