Beta is over =)! V1.0rc0 (#334)

* Fix doc + bump version

* Removed cmd util

* Remove test

Author: araffin

README.md

@@ -3,14 +3,12 @@
 [![pipeline status](https://gitlab.com/araffin/stable-baselines3/badges/master/pipeline.svg)](https://gitlab.com/araffin/stable-baselines3/-/commits/master) [![Documentation Status](https://readthedocs.org/projects/stable-baselines/badge/?version=master)](https://stable-baselines3.readthedocs.io/en/master/?badge=master) [![coverage report](https://gitlab.com/araffin/stable-baselines3/badges/master/coverage.svg)](https://gitlab.com/araffin/stable-baselines3/-/commits/master)
 [![codestyle](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
-**WARNING: Stable Baselines3 is currently in a beta version, breaking changes may occur before 1.0 is released**
-
 
 # Stable Baselines3
 
 Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of [Stable Baselines](https://github.com/hill-a/stable-baselines).
 
-You can read a detailed presentation of Stable Baselines in the [Medium article](https://medium.com/@araffin/stable-baselines-a-fork-of-openai-baselines-reinforcement-learning-made-easy-df87c4b2fc82).
+You can read a detailed presentation of Stable Baselines3 in the [v1.0 blog post](https://araffin.github.io/post/sb3/).
 
 
 These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.

docs/guide/examples.rst

@@ -312,6 +312,7 @@ will compute a running average and standard deviation of input features (it can
 
 .. code-block:: python
 
+  import os
   import gym
   import pybullet_envs
 
@@ -521,7 +522,7 @@ Behind the scene, SB3 uses an :ref:`EvalCallback <callbacks>`.
   # Note: if you don't save the complete model with `model.save()`
   # you cannot continue training afterward
   policy = model.policy
-  policy.save("sac_policy_pendulum.pkl")
+  policy.save("sac_policy_pendulum")
 
   # Retrieve the environment
   env = model.get_env()

docs/misc/changelog.rst

@@ -3,6 +3,18 @@
 Changelog
 ==========
 
+Release 1.0rc0 (2021-02-28)
+-------------------------------
+
+Breaking Changes:
+^^^^^^^^^^^^^^^^^
+- Removed ``stable_baselines3.common.cmd_util`` (already deprecated), please use ``env_util`` instead
+
+Documentation:
+^^^^^^^^^^^^^^
+- Fixed examples
+
+
 Pre-Release 0.11.1 (2021-02-27)
 -------------------------------
 

stable_baselines3/common/cmd_util.py

@@ -1 +1 @@
-0.11.1
+1.0rc0

stable_baselines3/version.txt

@@ -321,12 +321,6 @@ def test_zip_strict():
         pass
 
 
-def test_cmd_util_rename():
-    """Test that importing cmd_util still works but raises warning"""
-    with pytest.warns(FutureWarning):
-        from stable_baselines3.common.cmd_util import make_vec_env  # noqa: F401
-
-
 def test_is_wrapped():
     """Test that is_wrapped correctly detects wraps"""
     env = gym.make("Pendulum-v0")