[rllib] annotate public vs developer vs private APIs (#3808)

doc/source/rllib-dev.rst

@@ -6,6 +6,15 @@ Development Install
 
 You can develop RLlib locally without needing to compile Ray by using the `setup-rllib-dev.py <https://github.com/ray-project/ray/blob/master/python/ray/rllib/setup-rllib-dev.py>`__ script. This sets up links between the ``rllib`` dir in your git repo and the one bundled with the ``ray`` package. When using this script, make sure that your git branch is in sync with the installed Ray binaries (i.e., you are up-to-date on `master <https://github.com/ray-project/ray>`__ and have the latest `wheel <https://ray.readthedocs.io/en/latest/installation.html>`__ installed.)
 
+API Stability
+-------------
+
+Objects and methods annotated with ``@PublicAPI`` or ``@DeveloperAPI`` have the following API compatibility guarantees:
+
+.. autofunction:: ray.rllib.utils.annotations.PublicAPI
+
+.. autofunction:: ray.rllib.utils.annotations.DeveloperAPI
+
 Features
 --------
 

doc/source/rllib-env.rst

@@ -310,6 +310,6 @@ Note that envs can read from different partitions of the logs based on the ``wor
 Batch Asynchronous
 ------------------
 
-The lowest-level "catch-all" environment supported by RLlib is `AsyncVectorEnv <https://github.com/ray-project/ray/blob/master/python/ray/rllib/env/async_vector_env.py>`__. AsyncVectorEnv models multiple agents executing asynchronously in multiple environments. A call to ``poll()`` returns observations from ready agents keyed by their environment and agent ids, and actions for those agents can be sent back via ``send_actions()``. This interface can be subclassed directly to support batched simulators such as `ELF <https://github.com/facebookresearch/ELF>`__.
+The lowest-level "catch-all" environment supported by RLlib is `BaseEnv <https://github.com/ray-project/ray/blob/master/python/ray/rllib/env/base_env.py>`__. BaseEnv models multiple agents executing asynchronously in multiple environments. A call to ``poll()`` returns observations from ready agents keyed by their environment and agent ids, and actions for those agents can be sent back via ``send_actions()``. This interface can be subclassed directly to support batched simulators such as `ELF <https://github.com/facebookresearch/ELF>`__.
 
-Under the hood, all other envs are converted to AsyncVectorEnv by RLlib so that there is a common internal path for policy evaluation.
+Under the hood, all other envs are converted to BaseEnv by RLlib so that there is a common internal path for policy evaluation.