Rename to stable-baselines3

.coveragerc

@@ -4,7 +4,7 @@ omit =
     tests/*
     setup.py
     # Require graphical interface
-    torchy_baselines/common/results_plotter.py
+    stable_baselines3/common/results_plotter.py
 
 [report]
 exclude_lines =

.github/ISSUE_TEMPLATE/issue-template.md

@@ -13,7 +13,7 @@ If you are submitting a bug report, please fill in the following details.
 If your issue is related to a custom gym environment, please check it first using:
 
 ```python
-from torchy_baselines.common.env_checker import check_env
+from stable_baselines3.common.env_checker import check_env
 
 env = CustomEnv(arg1, ...)
 # It will check your custom environment and output additional warnings if needed
@@ -30,7 +30,7 @@ Please use the [markdown code blocks](https://help.github.com/en/articles/creati
 for both code and stack traces.
 
 ```python
-from torchy_baselines import ...
+from stable_baselines3 import ...
 
 ```
 

NOTICE

@@ -1,4 +1,4 @@
-Large portion of the code of Torchy-Baselines (in `common/`) were ported from Stable-Baselines, a fork of OpenAI Baselines,
+Large portion of the code of Stable-Baselines3 (in `common/`) were ported from Stable-Baselines, a fork of OpenAI Baselines,
 both licensed under the MIT License:
 
 before the fork (June 2018):

README.md

@@ -2,7 +2,7 @@
 
 [![Build Status](https://travis-ci.com/hill-a/stable-baselines.svg?branch=master)](https://travis-ci.com/hill-a/stable-baselines) [![Documentation Status](https://readthedocs.org/projects/stable-baselines/badge/?version=master)](https://stable-baselines.readthedocs.io/en/master/?badge=master)
 
-# Torchy Baselines
+# Stable Baselines3
 
 PyTorch version of [Stable Baselines](https://github.com/hill-a/stable-baselines), a set of improved implementations of reinforcement learning algorithms.
 
@@ -58,7 +58,7 @@ To cite this repository in publications:
 ```
 @misc{torchy-baselines,
   author = {Raffin, Antonin and Hill, Ashley and Ernestus, Maximilian and Gleave, Adam and Kanervisto, Anssi and Dormann, Noah},
-  title = {Torchy Baselines},
+  title = {Stable Baselines3},
   year = {2019},
   publisher = {GitHub},
   journal = {GitHub repository},

docs/conf.py

@@ -44,19 +44,19 @@ MOCK_MODULES = []
 sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 
 
-import torchy_baselines
+import stable_baselines3
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'Torchy Baselines'
-copyright = '2020, Torchy Baselines'
-author = 'Torchy Baselines Contributors'
+project = 'Stable Baselines3'
+copyright = '2020, Stable Baselines3'
+author = 'Stable Baselines3 Contributors'
 
 # The short X.Y version
-version = 'master (' + torchy_baselines.__version__ + ' )'
+version = 'master (' + stable_baselines3.__version__ + ' )'
 # The full version, including alpha/beta/rc tags
-release = torchy_baselines.__version__
+release = stable_baselines3.__version__
 
 
 # -- General configuration ---------------------------------------------------
@@ -179,8 +179,8 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'TorchyBaselines.tex', 'Torchy Baselines Documentation',
-     'Torchy Baselines Contributors', 'manual'),
+    (master_doc, 'TorchyBaselines.tex', 'Stable Baselines3 Documentation',
+     'Stable Baselines3 Contributors', 'manual'),
 ]
 
 
@@ -189,7 +189,7 @@ latex_documents = [
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'torchybaselines', 'Torchy Baselines Documentation',
+    (master_doc, 'torchybaselines', 'Stable Baselines3 Documentation',
      [author], 1)
 ]
 
@@ -200,7 +200,7 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'TorchyBaselines', 'Torchy Baselines Documentation',
+    (master_doc, 'TorchyBaselines', 'Stable Baselines3 Documentation',
      author, 'TorchyBaselines', 'One line description of project.',
      'Miscellaneous'),
 ]

docs/guide/quickstart.rst

@@ -12,9 +12,9 @@ Here is a quick example of how to train and run SAC on a Pendulum environment:
 
   import gym
 
-  from torchy_baselines.sac.policies import MlpPolicy
-  from torchy_baselines.common.vec_env import DummyVecEnv
-  from torchy_baselines import SAC
+  from stable_baselines3.sac.policies import MlpPolicy
+  from stable_baselines3.common.vec_env import DummyVecEnv
+  from stable_baselines3 import SAC
 
   env = gym.make('Pendulum-v0')
 
@@ -34,6 +34,6 @@ the policy is registered:
 
 .. code-block:: python
 
-    from torchy_baselines import SAC
+    from stable_baselines3 import SAC
 
     model = SAC('MlpPolicy', 'Pendulum-v0').learn(10000)

docs/guide/vec_envs.rst

@@ -1,6 +1,6 @@
 .. _vec_env:
 
-.. automodule:: torchy_baselines.common.vec_env
+.. automodule:: stable_baselines3.common.vec_env
 
 Vectorized Environments
 =======================

docs/index.rst

@@ -3,10 +3,10 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to Torchy Baselines docs! - Pytorch RL Baselines
+Welcome to Stable Baselines3 docs! - Pytorch RL Baselines
 ========================================================
 
-`Torchy Baselines <https://github.com/hill-a/stable-baselines>`_ is the PyTorch version of `Stable Baselines <https://github.com/hill-a/stable-baselines>`_,
+`Stable Baselines3 <https://github.com/hill-a/stable-baselines>`_ is the PyTorch version of `Stable Baselines <https://github.com/hill-a/stable-baselines>`_,
 a set of improved implementations of reinforcement learning algorithms.
 
 RL Baselines Zoo (collection of pre-trained agents): https://github.com/araffin/rl-baselines-zoo
@@ -41,7 +41,7 @@ RL Baselines zoo also offers a simple interface to train, evaluate agents and do
   misc/changelog
 
 
-Citing Torchy Baselines
+Citing Stable Baselines3
 -----------------------
 To cite this project in publications:
 
@@ -49,7 +49,7 @@ To cite this project in publications:
 
     @misc{torchy-baselines,
       author = {Raffin, Antonin and Hill, Ashley and Ernestus, Maximilian and Gleave, Adam and Kanervisto, Anssi and Dormann, Noah},
-      title = {Torchy Baselines},
+      title = {Stable Baselines3},
       year = {2019},
       publisher = {GitHub},
       journal = {GitHub repository},

docs/misc/changelog.rst

@@ -111,7 +111,7 @@ Pre-Release 0.2.0 (2020-02-14)
 
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
-- Python 2 support was dropped, Torchy Baselines now requires Python 3.6 or above
+- Python 2 support was dropped, Stable Baselines3 now requires Python 3.6 or above
 - Return type of ``evaluation.evaluate_policy()`` has been changed
 - Refactored the replay buffer to avoid transformation between PyTorch and NumPy
 - Created `OffPolicyRLModel` base class
@@ -160,7 +160,7 @@ New Features:
 Maintainers
 -----------
 
-Torchy-Baselines is currently maintained by `Antonin Raffin`_ (aka `@araffin`_).
+Stable-Baselines3 is currently maintained by `Antonin Raffin`_ (aka `@araffin`_).
 
 .. _Antonin Raffin: https://araffin.github.io/
 .. _@araffin: https://github.com/araffin

docs/modules/a2c.rst

@@ -1,6 +1,6 @@
 .. _a2c:
 
-.. automodule:: torchy_baselines.a2c
+.. automodule:: stable_baselines3.a2c
 
 
 A2C
@@ -44,9 +44,9 @@ Train a A2C agent on `CartPole-v1` using 4 processes.
 
   import gym
 
-  from torchy_baselines.common.policies import MlpPolicy
-  from torchy_baselines.common import make_vec_env
-  from torchy_baselines import A2C
+  from stable_baselines3.common.policies import MlpPolicy
+  from stable_baselines3.common import make_vec_env
+  from stable_baselines3 import A2C
 
   # Parallel environments
   env = make_vec_env('CartPole-v1', n_envs=4)

docs/modules/base.rst

@@ -1,6 +1,6 @@
 .. _base_algo:
 
-.. automodule:: torchy_baselines.common.base_class
+.. automodule:: stable_baselines3.common.base_class
 
 
 Base RL Class

docs/modules/ppo.rst

@@ -1,6 +1,6 @@
 .. _ppo2:
 
-.. automodule:: torchy_baselines.ppo
+.. automodule:: stable_baselines3.ppo
 
 PPO
 ===
@@ -53,9 +53,9 @@ Train a PPO agent on `Pendulum-v0` using 4 processes.
 
    import gym
 
-   from torchy_baselines.ppo.policies import MlpPolicy
-   from torchy_baselines.common.vec_env import SubprocVecEnv
-   from torchy_baselines import PPO
+   from stable_baselines3.ppo.policies import MlpPolicy
+   from stable_baselines3.common.vec_env import SubprocVecEnv
+   from stable_baselines3 import PPO
 
    # multiprocess environment
    n_cpu = 4

docs/modules/sac.rst

@@ -1,6 +1,6 @@
 .. _sac:
 
-.. automodule:: torchy_baselines.sac
+.. automodule:: stable_baselines3.sac
 
 
 SAC
@@ -14,7 +14,7 @@ A key feature of SAC, and a major difference with common RL algorithms, is that
 
 .. warning::
 
-  The SAC model does not support ``torchy_baselines.common.policies`` because it uses double q-values
+  The SAC model does not support ``stable_baselines3.common.policies`` because it uses double q-values
   and value estimation, as a result it must use its own policy models (see :ref:`sac_policies`).
 
 
@@ -72,9 +72,9 @@ Example
   import gym
   import numpy as np
 
-  from torchy_baselines.sac.policies import MlpPolicy
-  from torchy_baselines.common.vec_env import DummyVecEnv
-  from torchy_baselines import SAC
+  from stable_baselines3.sac.policies import MlpPolicy
+  from stable_baselines3.common.vec_env import DummyVecEnv
+  from stable_baselines3 import SAC
 
   env = gym.make('Pendulum-v0')
   env = DummyVecEnv([lambda: env])

docs/modules/td3.rst

@@ -1,6 +1,6 @@
 .. _td3:
 
-.. automodule:: torchy_baselines.td3
+.. automodule:: stable_baselines3.td3
 
 
 TD3
@@ -14,7 +14,7 @@ We recommend reading `OpenAI Spinning guide on TD3 <https://spinningup.openai.co
 
 .. warning::
 
-  The TD3 model does not support ``torchy_baselines.common.policies`` because it uses double q-values
+  The TD3 model does not support ``stable_baselines3.common.policies`` because it uses double q-values
   estimation, as a result it must use its own policy models (see :ref:`td3_policies`).
 
 
@@ -64,9 +64,9 @@ Example
 
   import numpy as np
 
-  from torchy_baselines import TD3
-  from torchy_baselines.td3.policies import MlpPolicy
-  from torchy_baselines.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
+  from stable_baselines3 import TD3
+  from stable_baselines3.td3.policies import MlpPolicy
+  from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
 
   # The noise objects for TD3
   n_actions = env.action_space.shape[-1]

setup.cfg

@@ -18,4 +18,4 @@ filterwarnings =
 		ignore::UserWarning:gym
 
 [pytype]
-inputs = torchy_baselines
+inputs = stable_baselines3

setup.py

@@ -3,13 +3,13 @@ import sys
 import subprocess
 from setuptools import setup, find_packages
 
-with open(os.path.join('torchy_baselines', 'version.txt'), 'r') as file_handler:
+with open(os.path.join('stable_baselines3', 'version.txt'), 'r') as file_handler:
     __version__ = file_handler.read()
 
 
-setup(name='torchy_baselines',
+setup(name='stable_baselines3',
       packages=[package for package in find_packages()
-                if package.startswith('torchy_baselines')],
+                if package.startswith('stable_baselines3')],
       install_requires=[
           'gym[classic_control]>=0.11',
           'numpy',

stable_baselines3/__init__.py

@@ -1,9 +1,9 @@
 import os
 
-from torchy_baselines.a2c import A2C
-from torchy_baselines.ppo import PPO
-from torchy_baselines.sac import SAC
-from torchy_baselines.td3 import TD3
+from stable_baselines3.a2c import A2C
+from stable_baselines3.ppo import PPO
+from stable_baselines3.sac import SAC
+from stable_baselines3.td3 import TD3
 
 # Read version from file
 version_file = os.path.join(os.path.dirname(__file__), 'version.txt')

stable_baselines3/a2c/__init__.py

@@ -0,0 +1,2 @@
+from stable_baselines3.a2c.a2c import A2C
+from stable_baselines3.ppo.policies import MlpPolicy

stable_baselines3/a2c/a2c.py

@@ -3,11 +3,11 @@ import torch.nn.functional as F
 from gym import spaces
 from typing import Type, Union, Callable, Optional, Dict, Any
 
-from torchy_baselines.common import logger
-from torchy_baselines.common.type_aliases import GymEnv, MaybeCallback
-from torchy_baselines.common.utils import explained_variance
-from torchy_baselines.ppo.policies import PPOPolicy
-from torchy_baselines.ppo.ppo import PPO
+from stable_baselines3.common import logger
+from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
+from stable_baselines3.common.utils import explained_variance
+from stable_baselines3.ppo.policies import PPOPolicy
+from stable_baselines3.ppo.ppo import PPO
 
 
 class A2C(PPO):

stable_baselines3/common/base_class.py

@@ -11,17 +11,17 @@ import gym
 import torch as th
 import numpy as np
 
-from torchy_baselines.common import logger
-from torchy_baselines.common.policies import BasePolicy, get_policy_from_name
-from torchy_baselines.common.utils import set_random_seed, get_schedule_fn, update_learning_rate, get_device
-from torchy_baselines.common.vec_env import DummyVecEnv, VecEnv, unwrap_vec_normalize, VecNormalize, VecTransposeImage
-from torchy_baselines.common.preprocessing import is_image_space
-from torchy_baselines.common.save_util import data_to_json, json_to_data, recursive_getattr, recursive_setattr
-from torchy_baselines.common.type_aliases import GymEnv, TensorDict, RolloutReturn, MaybeCallback
-from torchy_baselines.common.callbacks import BaseCallback, CallbackList, ConvertCallback, EvalCallback
-from torchy_baselines.common.monitor import Monitor
-from torchy_baselines.common.noise import ActionNoise
-from torchy_baselines.common.buffers import ReplayBuffer
+from stable_baselines3.common import logger
+from stable_baselines3.common.policies import BasePolicy, get_policy_from_name
+from stable_baselines3.common.utils import set_random_seed, get_schedule_fn, update_learning_rate, get_device
+from stable_baselines3.common.vec_env import DummyVecEnv, VecEnv, unwrap_vec_normalize, VecNormalize, VecTransposeImage
+from stable_baselines3.common.preprocessing import is_image_space
+from stable_baselines3.common.save_util import data_to_json, json_to_data, recursive_getattr, recursive_setattr
+from stable_baselines3.common.type_aliases import GymEnv, TensorDict, RolloutReturn, MaybeCallback
+from stable_baselines3.common.callbacks import BaseCallback, CallbackList, ConvertCallback, EvalCallback
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.noise import ActionNoise
+from stable_baselines3.common.buffers import ReplayBuffer
 
 
 class BaseRLModel(ABC):

stable_baselines3/common/buffers.py

@@ -4,9 +4,9 @@ import numpy as np
 import torch as th
 from gym import spaces
 
-from torchy_baselines.common.vec_env import VecNormalize
-from torchy_baselines.common.type_aliases import RolloutBufferSamples, ReplayBufferSamples
-from torchy_baselines.common.preprocessing import get_action_dim, get_obs_shape
+from stable_baselines3.common.vec_env import VecNormalize
+from stable_baselines3.common.type_aliases import RolloutBufferSamples, ReplayBufferSamples
+from stable_baselines3.common.preprocessing import get_action_dim, get_obs_shape
 
 
 class BaseBuffer(object):

stable_baselines3/common/callbacks.py

@@ -7,12 +7,12 @@ from typing import Union, List, Dict, Any, Optional
 import gym
 import numpy as np
 
-from torchy_baselines.common.vec_env import DummyVecEnv, VecEnv, sync_envs_normalization
-from torchy_baselines.common.evaluation import evaluate_policy
-from torchy_baselines.common.logger import Logger
+from stable_baselines3.common.vec_env import DummyVecEnv, VecEnv, sync_envs_normalization
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.logger import Logger
 
 if typing.TYPE_CHECKING:
-    from torchy_baselines.common.base_class import BaseRLModel  # pytype: disable=pyi-error
+    from stable_baselines3.common.base_class import BaseRLModel  # pytype: disable=pyi-error
 
 
 class BaseCallback(ABC):

stable_baselines3/common/distributions.py

@@ -6,7 +6,7 @@ import torch.nn as nn
 from torch.distributions import Normal, Categorical
 from gym import spaces
 
-from torchy_baselines.common.preprocessing import get_action_dim
+from stable_baselines3.common.preprocessing import get_action_dim
 
 
 class Distribution(object):

stable_baselines3/common/evaluation.py

@@ -1,7 +1,7 @@
 # Copied from stable_baselines
 import numpy as np
 
-from torchy_baselines.common.vec_env import VecEnv
+from stable_baselines3.common.vec_env import VecEnv
 
 
 def evaluate_policy(model, env, n_eval_episodes=10, deterministic=True,

stable_baselines3/common/identity_env.py

@@ -5,7 +5,7 @@ from gym import Env
 from gym.spaces import Discrete, MultiDiscrete, MultiBinary, Box
 
 
-from torchy_baselines.common.type_aliases import GymStepReturn, GymObs
+from stable_baselines3.common.type_aliases import GymStepReturn, GymObs
 
 
 class IdentityEnv(Env):

stable_baselines3/common/policies.py

@@ -7,9 +7,9 @@ import torch as th
 import torch.nn as nn
 import numpy as np
 
-from torchy_baselines.common.preprocessing import preprocess_obs, get_flattened_obs_dim, is_image_space
-from torchy_baselines.common.utils import get_device
-from torchy_baselines.common.vec_env import VecTransposeImage
+from stable_baselines3.common.preprocessing import preprocess_obs, get_flattened_obs_dim, is_image_space
+from stable_baselines3.common.utils import get_device
+from stable_baselines3.common.vec_env import VecTransposeImage
 
 
 class BaseFeaturesExtractor(nn.Module):

stable_baselines3/common/results_plotter.py

@@ -6,7 +6,7 @@ import pandas as pd
 # matplotlib.use('TkAgg')  # Can change to 'Agg' for non-interactive mode
 import matplotlib.pyplot as plt
 
-from torchy_baselines.common.monitor import load_results
+from stable_baselines3.common.monitor import load_results
 
 
 X_TIMESTEPS = 'timesteps'

stable_baselines3/common/type_aliases.py

@@ -7,8 +7,8 @@ import numpy as np
 import torch as th
 import gym
 
-from torchy_baselines.common.vec_env import VecEnv
-from torchy_baselines.common.callbacks import BaseCallback
+from stable_baselines3.common.vec_env import VecEnv
+from stable_baselines3.common.callbacks import BaseCallback
 
 
 GymEnv = Union[gym.Env, VecEnv]

stable_baselines3/common/vec_env/__init__.py

@@ -3,17 +3,17 @@ import typing
 from typing import Optional, Union
 from copy import deepcopy
 
-from torchy_baselines.common.vec_env.base_vec_env import (AlreadySteppingError, NotSteppingError,
+from stable_baselines3.common.vec_env.base_vec_env import (AlreadySteppingError, NotSteppingError,
                                                           VecEnv, VecEnvWrapper, CloudpickleWrapper)
-from torchy_baselines.common.vec_env.dummy_vec_env import DummyVecEnv
-from torchy_baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
-from torchy_baselines.common.vec_env.vec_frame_stack import VecFrameStack
-from torchy_baselines.common.vec_env.vec_normalize import VecNormalize
-from torchy_baselines.common.vec_env.vec_transpose import VecTransposeImage
+from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
+from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv
+from stable_baselines3.common.vec_env.vec_frame_stack import VecFrameStack
+from stable_baselines3.common.vec_env.vec_normalize import VecNormalize
+from stable_baselines3.common.vec_env.vec_transpose import VecTransposeImage
 
 # Avoid circular import
 if typing.TYPE_CHECKING:
-    from torchy_baselines.common.type_aliases import GymEnv
+    from stable_baselines3.common.type_aliases import GymEnv
 
 
 def unwrap_vec_normalize(env: Union['GymEnv', VecEnv]) -> Optional[VecNormalize]:

stable_baselines3/common/vec_env/dummy_vec_env.py

@@ -3,8 +3,8 @@ from copy import deepcopy
 
 import numpy as np
 
-from torchy_baselines.common.vec_env.base_vec_env import VecEnv
-from torchy_baselines.common.vec_env.util import copy_obs_dict, dict_to_obs, obs_space_info
+from stable_baselines3.common.vec_env.base_vec_env import VecEnv
+from stable_baselines3.common.vec_env.util import copy_obs_dict, dict_to_obs, obs_space_info
 
 
 class DummyVecEnv(VecEnv):

stable_baselines3/common/vec_env/subproc_vec_env.py

@@ -4,7 +4,7 @@ from collections import OrderedDict
 import gym
 import numpy as np
 
-from torchy_baselines.common.vec_env.base_vec_env import VecEnv, CloudpickleWrapper
+from stable_baselines3.common.vec_env.base_vec_env import VecEnv, CloudpickleWrapper
 
 
 def _worker(remote, parent_remote, env_fn_wrapper):

stable_baselines3/common/vec_env/vec_frame_stack.py

@@ -3,7 +3,7 @@ import warnings
 import numpy as np
 from gym import spaces
 
-from torchy_baselines.common.vec_env.base_vec_env import VecEnv, VecEnvWrapper
+from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvWrapper
 
 
 class VecFrameStack(VecEnvWrapper):

stable_baselines3/common/vec_env/vec_normalize.py

@@ -2,8 +2,8 @@ import pickle
 
 import numpy as np
 
-from torchy_baselines.common.vec_env.base_vec_env import VecEnvWrapper
-from torchy_baselines.common.running_mean_std import RunningMeanStd
+from stable_baselines3.common.vec_env.base_vec_env import VecEnvWrapper
+from stable_baselines3.common.running_mean_std import RunningMeanStd
 
 
 class VecNormalize(VecEnvWrapper):

stable_baselines3/common/vec_env/vec_transpose.py

@@ -2,11 +2,11 @@ import typing
 import numpy as np
 from gym import spaces
 
-from torchy_baselines.common.vec_env.base_vec_env import VecEnv, VecEnvWrapper
-from torchy_baselines.common.preprocessing import is_image_space
+from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvWrapper
+from stable_baselines3.common.preprocessing import is_image_space
 
 if typing.TYPE_CHECKING:
-    from torchy_baselines.common.type_aliases import GymStepReturn
+    from stable_baselines3.common.type_aliases import GymStepReturn
 
 
 class VecTransposeImage(VecEnvWrapper):

stable_baselines3/ppo/__init__.py

@@ -0,0 +1,2 @@
+from stable_baselines3.ppo.ppo import PPO
+from stable_baselines3.ppo.policies import MlpPolicy

stable_baselines3/ppo/policies.py

@@ -6,10 +6,10 @@ import torch as th
 import torch.nn as nn
 import numpy as np
 
-from torchy_baselines.common.policies import (BasePolicy, register_policy, MlpExtractor,
+from stable_baselines3.common.policies import (BasePolicy, register_policy, MlpExtractor,
                                               create_sde_features_extractor, NatureCNN,
                                               BaseFeaturesExtractor, FlattenExtractor)
-from torchy_baselines.common.distributions import (make_proba_distribution, Distribution,
+from stable_baselines3.common.distributions import (make_proba_distribution, Distribution,
                                                    DiagGaussianDistribution, CategoricalDistribution,
                                                    StateDependentNoiseDistribution)
 

stable_baselines3/ppo/ppo.py

@@ -14,14 +14,14 @@ import torch.nn.functional as F
 #     SummaryWriter = None
 import numpy as np
 
-from torchy_baselines.common import logger
-from torchy_baselines.common.base_class import BaseRLModel
-from torchy_baselines.common.type_aliases import GymEnv, MaybeCallback
-from torchy_baselines.common.buffers import RolloutBuffer
-from torchy_baselines.common.utils import explained_variance, get_schedule_fn
-from torchy_baselines.common.vec_env import VecEnv
-from torchy_baselines.common.callbacks import BaseCallback
-from torchy_baselines.ppo.policies import PPOPolicy
+from stable_baselines3.common import logger
+from stable_baselines3.common.base_class import BaseRLModel
+from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
+from stable_baselines3.common.buffers import RolloutBuffer
+from stable_baselines3.common.utils import explained_variance, get_schedule_fn
+from stable_baselines3.common.vec_env import VecEnv
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.ppo.policies import PPOPolicy
 
 
 class PPO(BaseRLModel):

stable_baselines3/sac/__init__.py

@@ -0,0 +1,2 @@
+from stable_baselines3.sac.sac import SAC
+from stable_baselines3.sac.policies import MlpPolicy

stable_baselines3/sac/policies.py

@@ -4,11 +4,11 @@ import gym
 import torch as th
 import torch.nn as nn
 
-from torchy_baselines.common.preprocessing import get_action_dim
-from torchy_baselines.common.policies import (BasePolicy, register_policy, create_mlp,
+from stable_baselines3.common.preprocessing import get_action_dim
+from stable_baselines3.common.policies import (BasePolicy, register_policy, create_mlp,
                                               create_sde_features_extractor, NatureCNN,
                                               BaseFeaturesExtractor, FlattenExtractor)
-from torchy_baselines.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
+from stable_baselines3.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
 
 # CAP the standard deviation of the actor
 LOG_STD_MAX = 2

stable_baselines3/sac/sac.py

@@ -4,11 +4,11 @@ import torch as th
 import torch.nn.functional as F
 import numpy as np
 
-from torchy_baselines.common import logger
-from torchy_baselines.common.base_class import OffPolicyRLModel
-from torchy_baselines.common.type_aliases import GymEnv, MaybeCallback
-from torchy_baselines.common.noise import ActionNoise
-from torchy_baselines.sac.policies import SACPolicy
+from stable_baselines3.common import logger
+from stable_baselines3.common.base_class import OffPolicyRLModel
+from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
+from stable_baselines3.common.noise import ActionNoise
+from stable_baselines3.sac.policies import SACPolicy
 
 
 class SAC(OffPolicyRLModel):

stable_baselines3/td3/__init__.py

@@ -0,0 +1,2 @@
+from stable_baselines3.td3.td3 import TD3
+from stable_baselines3.td3.policies import MlpPolicy

stable_baselines3/td3/policies.py

@@ -4,11 +4,11 @@ import gym
 import torch as th
 import torch.nn as nn
 
-from torchy_baselines.common.preprocessing import get_action_dim
-from torchy_baselines.common.policies import (BasePolicy, register_policy, create_mlp,
+from stable_baselines3.common.preprocessing import get_action_dim
+from stable_baselines3.common.policies import (BasePolicy, register_policy, create_mlp,
                                               create_sde_features_extractor, NatureCNN,
                                               BaseFeaturesExtractor, FlattenExtractor)
-from torchy_baselines.common.distributions import StateDependentNoiseDistribution
+from stable_baselines3.common.distributions import StateDependentNoiseDistribution
 
 
 class Actor(BasePolicy):

stable_baselines3/td3/td3.py

@@ -2,11 +2,11 @@ import torch as th
 import torch.nn.functional as F
 from typing import List, Tuple, Type, Union, Callable, Optional, Dict, Any
 
-from torchy_baselines.common import logger
-from torchy_baselines.common.base_class import OffPolicyRLModel
-from torchy_baselines.common.noise import ActionNoise
-from torchy_baselines.common.type_aliases import GymEnv, MaybeCallback
-from torchy_baselines.td3.policies import TD3Policy
+from stable_baselines3.common import logger
+from stable_baselines3.common.base_class import OffPolicyRLModel
+from stable_baselines3.common.noise import ActionNoise
+from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
+from stable_baselines3.td3.policies import TD3Policy
 
 
 class TD3(OffPolicyRLModel):

tests/test_callbacks.py

@@ -4,8 +4,8 @@ import shutil
 import pytest
 import gym
 
-from torchy_baselines import A2C, PPO, SAC, TD3
-from torchy_baselines.common.callbacks import (CallbackList, CheckpointCallback, EvalCallback,
+from stable_baselines3 import A2C, PPO, SAC, TD3
+from stable_baselines3.common.callbacks import (CallbackList, CheckpointCallback, EvalCallback,
                                                EveryNTimesteps, StopTrainingOnRewardThreshold)
 
 

tests/test_cnn.py

@@ -3,8 +3,8 @@ import os
 import numpy as np
 import pytest
 
-from torchy_baselines import A2C, PPO, SAC, TD3
-from torchy_baselines.common.identity_env import FakeImageEnv
+from stable_baselines3 import A2C, PPO, SAC, TD3
+from stable_baselines3.common.identity_env import FakeImageEnv
 
 SAVE_PATH = './cnn_model.zip'
 

tests/test_custom_policy.py

@@ -1,7 +1,7 @@
 import pytest
 import torch as th
 
-from torchy_baselines import A2C, PPO, SAC, TD3
+from stable_baselines3 import A2C, PPO, SAC, TD3
 
 
 @pytest.mark.parametrize('net_arch', [

tests/test_distributions.py

@@ -1,11 +1,11 @@
 import pytest
 import torch as th
 
-from torchy_baselines import A2C, PPO
-from torchy_baselines.common.distributions import (DiagGaussianDistribution, TanhBijector,
+from stable_baselines3 import A2C, PPO
+from stable_baselines3.common.distributions import (DiagGaussianDistribution, TanhBijector,
                                                    StateDependentNoiseDistribution,
                                                    CategoricalDistribution, SquashedDiagGaussianDistribution)
-from torchy_baselines.common.utils import set_random_seed
+from stable_baselines3.common.utils import set_random_seed
 
 
 N_ACTIONS = 2

tests/test_identity.py

@@ -1,10 +1,10 @@
 import numpy as np
 import pytest
 
-from torchy_baselines import A2C, PPO, SAC, TD3
-from torchy_baselines.common.identity_env import IdentityEnvBox, IdentityEnv
-from torchy_baselines.common.evaluation import evaluate_policy
-from torchy_baselines.common.noise import NormalActionNoise
+from stable_baselines3 import A2C, PPO, SAC, TD3
+from stable_baselines3.common.identity_env import IdentityEnvBox, IdentityEnv
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.noise import NormalActionNoise
 
 
 @pytest.mark.parametrize("model_class", [A2C, PPO])

tests/test_logger.py

@@ -4,7 +4,7 @@ import shutil
 import pytest
 import numpy as np
 
-from torchy_baselines.common.logger import (make_output_format, read_csv, read_json, DEBUG, ScopedConfigure,
+from stable_baselines3.common.logger import (make_output_format, read_csv, read_json, DEBUG, ScopedConfigure,
                                             info, debug, set_level, configure, logkv, logkvs,
                                             dumpkvs, logkv_mean, warn, error, reset)
 
@@ -18,7 +18,7 @@ KEY_VALUES = {
     "g": np.array([[[1]]]),
 }
 
-LOG_DIR = '/tmp/torchy_baselines/'
+LOG_DIR = '/tmp/stable_baselines3/'
 
 
 def test_main():

tests/test_monitor.py

@@ -5,7 +5,7 @@ import os
 import pandas
 import gym
 
-from torchy_baselines.common.monitor import Monitor, get_monitor_files, load_results
+from stable_baselines3.common.monitor import Monitor, get_monitor_files, load_results
 
 
 def test_monitor(tmp_path):

tests/test_predict.py

@@ -1,8 +1,8 @@
 import gym
 import pytest
 
-from torchy_baselines import A2C, PPO, SAC, TD3
-from torchy_baselines.common.vec_env import DummyVecEnv
+from stable_baselines3 import A2C, PPO, SAC, TD3
+from stable_baselines3.common.vec_env import DummyVecEnv
 
 MODEL_LIST = [
     PPO,

tests/test_run.py

@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 
-from torchy_baselines import A2C, PPO, SAC, TD3
-from torchy_baselines.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
+from stable_baselines3 import A2C, PPO, SAC, TD3
+from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
 
 normal_action_noise = NormalActionNoise(np.zeros(1), 0.1 * np.ones(1))
 

tests/test_save_load.py

@@ -5,10 +5,10 @@ import pytest
 import numpy as np
 import torch as th
 
-from torchy_baselines import A2C, PPO, SAC, TD3
-from torchy_baselines.common.identity_env import IdentityEnvBox
-from torchy_baselines.common.vec_env import DummyVecEnv
-from torchy_baselines.common.identity_env import FakeImageEnv
+from stable_baselines3 import A2C, PPO, SAC, TD3
+from stable_baselines3.common.identity_env import IdentityEnvBox
+from stable_baselines3.common.vec_env import DummyVecEnv
+from stable_baselines3.common.identity_env import FakeImageEnv
 
 
 MODEL_LIST = [

tests/test_sde.py

@@ -2,7 +2,7 @@ import pytest
 import torch as th
 from torch.distributions import Normal
 
-from torchy_baselines import A2C, TD3, SAC, PPO
+from stable_baselines3 import A2C, TD3, SAC, PPO
 
 
 def test_state_dependent_exploration_grad():

tests/test_vec_envs.py

@@ -7,7 +7,7 @@ import pytest
 import gym
 import numpy as np
 
-from torchy_baselines.common.vec_env import DummyVecEnv, SubprocVecEnv, VecNormalize, VecFrameStack
+from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv, VecNormalize, VecFrameStack
 
 N_ENVS = 3
 VEC_ENV_CLASSES = [DummyVecEnv, SubprocVecEnv]

tests/test_vec_normalize.py

@@ -2,9 +2,9 @@ import gym
 import pytest
 import numpy as np
 
-from torchy_baselines.common.running_mean_std import RunningMeanStd
-from torchy_baselines.common.vec_env import DummyVecEnv, VecNormalize, VecFrameStack, sync_envs_normalization, unwrap_vec_normalize
-from torchy_baselines import SAC, TD3
+from stable_baselines3.common.running_mean_std import RunningMeanStd
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize, VecFrameStack, sync_envs_normalization, unwrap_vec_normalize
+from stable_baselines3 import SAC, TD3
 
 ENV_ID = 'Pendulum-v0'
 

torchy_baselines/a2c/__init__.py

@@ -1,2 +0,0 @@
-from torchy_baselines.a2c.a2c import A2C
-from torchy_baselines.ppo.policies import MlpPolicy

torchy_baselines/ppo/__init__.py

@@ -1,2 +0,0 @@
-from torchy_baselines.ppo.ppo import PPO
-from torchy_baselines.ppo.policies import MlpPolicy

torchy_baselines/sac/__init__.py

@@ -1,2 +0,0 @@
-from torchy_baselines.sac.sac import SAC
-from torchy_baselines.sac.policies import MlpPolicy

torchy_baselines/td3/__init__.py

@@ -1,2 +0,0 @@
-from torchy_baselines.td3.td3 import TD3
-from torchy_baselines.td3.policies import MlpPolicy