Merge branch 'master' into contrib
This commit is contained in:
Anssi "Miffyli" Kanervisto
commit
8d3570ae5f
|
|
@ -29,15 +29,16 @@ jobs:
|
|||
python -m pip install --upgrade pip
|
||||
# cpu version of pytorch
|
||||
pip install torch==1.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
|
||||
# Install dependencies for docs and tests
|
||||
pip install stable_baselines3[extra,tests,docs]
|
||||
# Install master version
|
||||
pip install git+https://github.com/DLR-RM/stable-baselines3
|
||||
pip install .
|
||||
# Use headless version
|
||||
pip install opencv-python-headless
|
||||
# - name: Build the doc
|
||||
# run: |
|
||||
# make doc
|
||||
- name: Build the doc
|
||||
run: |
|
||||
make doc
|
||||
- name: Type check
|
||||
run: |
|
||||
make type
|
||||
|
|
|
|||
7
Makefile
7
Makefile
|
|
@ -28,6 +28,11 @@ check-codestyle:
|
|||
|
||||
commit-checks: format type lint
|
||||
|
||||
doc:
|
||||
cd docs && make html
|
||||
|
||||
spelling:
|
||||
cd docs && make spelling
|
||||
|
||||
# PyPi package release
|
||||
release:
|
||||
|
|
@ -41,4 +46,4 @@ test-release:
|
|||
python setup.py bdist_wheel
|
||||
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
|
||||
|
||||
.PHONY: lint format check-codestyle commit-checks
|
||||
.PHONY: lint format check-codestyle commit-checks doc spelling
|
||||
|
|
|
|||
16
README.md
16
README.md
|
|
@ -18,3 +18,19 @@ Implemented:
|
|||
```
|
||||
pip install git+https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
|
||||
```
|
||||
|
||||
|
||||
## Citing the Project
|
||||
|
||||
To cite this repository in publications (please cite SB3 directly):
|
||||
|
||||
```
|
||||
@misc{stable-baselines3,
|
||||
author = {Raffin, Antonin and Hill, Ashley and Ernestus, Maximilian and Gleave, Adam and Kanervisto, Anssi and Dormann, Noah},
|
||||
title = {Stable Baselines3},
|
||||
year = {2019},
|
||||
publisher = {GitHub},
|
||||
journal = {GitHub repository},
|
||||
howpublished = {\url{https://github.com/DLR-RM/stable-baselines3}},
|
||||
}
|
||||
```
|
||||
|
|
|
|||
|
|
@ -0,0 +1,20 @@
|
|||
# Minimal makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line.
|
||||
SPHINXOPTS = -W # make warnings fatal
|
||||
SPHINXBUILD = sphinx-build
|
||||
SPHINXPROJ = StableBaselines
|
||||
SOURCEDIR = .
|
||||
BUILDDIR = _build
|
||||
|
||||
# Put it first so that "make" without argument is like "make help".
|
||||
help:
|
||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
.PHONY: help Makefile
|
||||
|
||||
# Catch-all target: route all unknown targets to Sphinx using the new
|
||||
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
## Stable Baselines3 Documentation
|
||||
|
||||
This folder contains documentation for the RL baselines.
|
||||
|
||||
|
||||
### Build the Documentation
|
||||
|
||||
#### Install Sphinx and Theme
|
||||
|
||||
```
|
||||
pip install sphinx sphinx-autobuild sphinx-rtd-theme
|
||||
```
|
||||
|
||||
#### Building the Docs
|
||||
|
||||
In the `docs/` folder:
|
||||
```
|
||||
make html
|
||||
```
|
||||
|
||||
if you want to building each time a file is changed:
|
||||
|
||||
```
|
||||
sphinx-autobuild . _build/html
|
||||
```
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
/* Main colors adapted from pytorch doc */
|
||||
:root{
|
||||
--main-bg-color: #343A40;
|
||||
--link-color: #FD7E14;
|
||||
}
|
||||
|
||||
/* Header fonts y */
|
||||
h1, h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend, p.caption {
|
||||
font-family: "Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;
|
||||
}
|
||||
|
||||
|
||||
/* Docs background */
|
||||
.wy-side-nav-search{
|
||||
background-color: var(--main-bg-color);
|
||||
}
|
||||
|
||||
/* Mobile version */
|
||||
.wy-nav-top{
|
||||
background-color: var(--main-bg-color);
|
||||
}
|
||||
|
||||
/* Change link colors (except for the menu) */
|
||||
a {
|
||||
color: var(--link-color);
|
||||
}
|
||||
|
||||
a:hover {
|
||||
color: #4F778F;
|
||||
}
|
||||
|
||||
.wy-menu a {
|
||||
color: #b3b3b3;
|
||||
}
|
||||
|
||||
.wy-menu a:hover {
|
||||
color: #b3b3b3;
|
||||
}
|
||||
|
||||
a.icon.icon-home {
|
||||
color: #b3b3b3;
|
||||
}
|
||||
|
||||
.version{
|
||||
color: var(--link-color) !important;
|
||||
}
|
||||
|
||||
|
||||
/* Make code blocks have a background */
|
||||
.codeblock,pre.literal-block,.rst-content .literal-block,.rst-content pre.literal-block,div[class^='highlight'] {
|
||||
background: #f8f8f8;;
|
||||
}
|
||||
|
||||
/* Change style of types in the docstrings .rst-content .field-list */
|
||||
.field-list .xref.py.docutils, .field-list code.docutils, .field-list .docutils.literal.notranslate
|
||||
{
|
||||
border: None;
|
||||
padding-left: 0;
|
||||
padding-right: 0;
|
||||
color: #404040;
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="117" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="117" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h30v20H0z"/><path fill="#007ec6" d="M30 0h87v20H30z"/><path fill="url(#b)" d="M0 0h117v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"><svg x="4px" y="0px" width="22px" height="20px" viewBox="-2 0 28 24" style="background-color: #fff;border-radius: 1px;"><path style="fill:#ef9008;" d="M1.977,16.77c-2.667-2.277-2.605-7.079,0-9.357C2.919,8.057,3.522,9.075,4.49,9.691c-1.152,1.6-1.146,3.201-0.004,4.803C3.522,15.111,2.918,16.126,1.977,16.77z"/><path style="fill:#fdba18;" d="M12.257,17.114c-1.767-1.633-2.485-3.658-2.118-6.02c0.451-2.91,2.139-4.893,4.946-5.678c2.565-0.718,4.964-0.217,6.878,1.819c-0.884,0.743-1.707,1.547-2.434,2.446C18.488,8.827,17.319,8.435,16,8.856c-2.404,0.767-3.046,3.241-1.494,5.644c-0.241,0.275-0.493,0.541-0.721,0.826C13.295,15.939,12.511,16.3,12.257,17.114z"/><path style="fill:#ef9008;" d="M19.529,9.682c0.727-0.899,1.55-1.703,2.434-2.446c2.703,2.783,2.701,7.031-0.005,9.764c-2.648,2.674-6.936,2.725-9.701,0.115c0.254-0.814,1.038-1.175,1.528-1.788c0.228-0.285,0.48-0.552,0.721-0.826c1.053,0.916,2.254,1.268,3.6,0.83C20.502,14.551,21.151,11.927,19.529,9.682z"/><path style="fill:#fdba18;" d="M4.49,9.691C3.522,9.075,2.919,8.057,1.977,7.413c2.209-2.398,5.721-2.942,8.476-1.355c0.555,0.32,0.719,0.606,0.285,1.128c-0.157,0.188-0.258,0.422-0.391,0.631c-0.299,0.47-0.509,1.067-0.929,1.371C8.933,9.539,8.523,8.847,8.021,8.746C6.673,8.475,5.509,8.787,4.49,9.691z"/><path style="fill:#fdba18;" d="M1.977,16.77c0.941-0.644,1.545-1.659,2.509-2.277c1.373,1.152,2.85,1.433,4.45,0.499c0.332-0.194,0.503-0.088,0.673,0.19c0.386,0.635,0.753,1.285,1.181,1.89c0.34,0.48,0.222,0.715-0.253,1.006C7.84,19.73,4.205,19.188,1.977,16.77z"/></svg><text x="245" y="140" transform="scale(.1)" textLength="30"> </text><text x="725" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="770">Open in Colab</text><text x="725" y="140" transform="scale(.1)" textLength="770">Open in Colab</text></g> </svg>
|
||||
|
After Width: | Height: | Size: 2.3 KiB |
|
|
@ -0,0 +1,7 @@
|
|||
<svg width="24px" height="15px" viewBox="0 0 24 15" version="1.1" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M1.977,11.77 C-0.69,9.493 -0.628,4.691 1.977,2.413 C2.919,3.057 3.522,4.075 4.49,4.691 C3.338,6.291 3.344,7.892 4.486,9.494 C3.522,10.111 2.918,11.126 1.977,11.77 Z" fill="#FDBA18"/>
|
||||
<path d="M12.257,12.114 C10.49,10.481 9.772,8.456 10.139,6.094 C10.59,3.184 12.278,1.201 15.085,0.416 C17.65,-0.302 20.049,0.199 21.963,2.235 C21.079,2.978 20.256,3.782 19.529,4.681 C18.488,3.827 17.319,3.435 16,3.856 C13.596,4.623 12.954,7.097 14.506,9.5 C14.265,9.775 14.013,10.041 13.785,10.326 C13.295,10.939 12.511,11.3 12.257,12.114 Z" fill="#FCD93D"/>
|
||||
<path d="M19.529,4.682 C20.256,3.783 21.079,2.979 21.963,2.236 C24.666,5.019 24.664,9.267 21.958,12 C19.31,14.674 15.022,14.725 12.257,12.115 C12.511,11.301 13.295,10.94 13.785,10.327 C14.013,10.042 14.265,9.775 14.506,9.501 C15.559,10.417 16.76,10.769 18.106,10.331 C20.502,9.551 21.151,6.927 19.529,4.682 Z" fill="#FDBA18"/>
|
||||
<path d="M4.49,4.691 C3.522,4.075 2.919,3.057 1.977,2.413 C4.186,0.015 7.698,-0.529 10.453,1.058 C11.008,1.378 11.172,1.664 10.738,2.186 C10.581,2.374 10.48,2.608 10.347,2.817 C10.048,3.287 9.838,3.884 9.418,4.188 C8.933,4.539 8.523,3.847 8.021,3.746 C6.673,3.475 5.509,3.787 4.49,4.691 Z" fill="#FCD93D"/>
|
||||
<path d="M1.977,11.77 C2.918,11.126 3.522,10.111 4.486,9.493 C5.859,10.645 7.336,10.926 8.936,9.992 C9.268,9.798 9.439,9.904 9.609,10.182 C9.995,10.817 10.362,11.467 10.79,12.072 C11.13,12.552 11.012,12.787 10.537,13.078 C7.84,14.73 4.205,14.188 1.977,11.77 Z" fill="#FCD93D"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.5 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 156 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 156 KiB |
|
|
@ -0,0 +1,18 @@
|
|||
name: root
|
||||
channels:
|
||||
- pytorch
|
||||
- defaults
|
||||
dependencies:
|
||||
- cpuonly=1.0=0
|
||||
- pip=20.2
|
||||
- python=3.6
|
||||
- pytorch=1.5.0=py3.6_cpu_0
|
||||
- pip:
|
||||
- gym>=0.17.2
|
||||
- cloudpickle
|
||||
- opencv-python-headless
|
||||
- pandas
|
||||
- numpy
|
||||
- matplotlib
|
||||
- sphinx_autodoc_typehints
|
||||
- stable-baselines3>=0.9.0
|
||||
|
|
@ -0,0 +1,219 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# This file does only contain a selection of the most common options. For a
|
||||
# full list see the documentation:
|
||||
# http://www.sphinx-doc.org/en/master/config
|
||||
|
||||
# -- Path setup --------------------------------------------------------------
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
# We CANNOT enable 'sphinxcontrib.spelling' because ReadTheDocs.org does not support
|
||||
# PyEnchant.
|
||||
try:
|
||||
import sphinxcontrib.spelling # noqa: F401
|
||||
|
||||
enable_spell_check = True
|
||||
except ImportError:
|
||||
enable_spell_check = False
|
||||
|
||||
# source code directory, relative to this file, for sphinx-autobuild
|
||||
sys.path.insert(0, os.path.abspath(".."))
|
||||
|
||||
|
||||
class Mock(MagicMock):
|
||||
__subclasses__ = []
|
||||
|
||||
@classmethod
|
||||
def __getattr__(cls, name):
|
||||
return MagicMock()
|
||||
|
||||
|
||||
# Mock modules that requires C modules
|
||||
# Note: because of that we cannot test examples using CI
|
||||
# 'torch', 'torch.nn', 'torch.nn.functional',
|
||||
# DO not mock modules for now, we will need to do that for read the docs later
|
||||
MOCK_MODULES = []
|
||||
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
|
||||
|
||||
# Read version from file
|
||||
version_file = os.path.join(os.path.dirname(__file__), "../sb3_contrib", "version.txt")
|
||||
with open(version_file, "r") as file_handler:
|
||||
__version__ = file_handler.read().strip()
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = "Stable Baselines3 - Contrib"
|
||||
copyright = "2020, Stable Baselines3"
|
||||
author = "Stable Baselines3 Contributors"
|
||||
|
||||
# The short X.Y version
|
||||
version = "master (" + __version__ + " )"
|
||||
# The full version, including alpha/beta/rc tags
|
||||
release = __version__
|
||||
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
||||
# If your documentation needs a minimal Sphinx version, state it here.
|
||||
#
|
||||
# needs_sphinx = '1.0'
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
"sphinx.ext.autodoc",
|
||||
"sphinx_autodoc_typehints",
|
||||
"sphinx.ext.autosummary",
|
||||
"sphinx.ext.mathjax",
|
||||
"sphinx.ext.ifconfig",
|
||||
"sphinx.ext.viewcode",
|
||||
# 'sphinx.ext.intersphinx',
|
||||
# 'sphinx.ext.doctest'
|
||||
]
|
||||
|
||||
if enable_spell_check:
|
||||
extensions.append("sphinxcontrib.spelling")
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ["_templates"]
|
||||
|
||||
# The suffix(es) of source filenames.
|
||||
# You can specify multiple suffix as a list of string:
|
||||
#
|
||||
# source_suffix = ['.rst', '.md']
|
||||
source_suffix = ".rst"
|
||||
|
||||
# The master toctree document.
|
||||
master_doc = "index"
|
||||
|
||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||
# for a list of supported languages.
|
||||
#
|
||||
# This is also used if you do content translation via gettext catalogs.
|
||||
# Usually you set "language" from the command line for these cases.
|
||||
language = None
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path .
|
||||
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
|
||||
|
||||
# The name of the Pygments (syntax highlighting) style to use.
|
||||
pygments_style = "sphinx"
|
||||
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
|
||||
# Fix for read the docs
|
||||
on_rtd = os.environ.get("READTHEDOCS") == "True"
|
||||
if on_rtd:
|
||||
html_theme = "default"
|
||||
else:
|
||||
html_theme = "sphinx_rtd_theme"
|
||||
|
||||
html_logo = "_static/img/logo.png"
|
||||
|
||||
|
||||
def setup(app):
|
||||
app.add_css_file("css/baselines_theme.css")
|
||||
|
||||
|
||||
# Theme options are theme-specific and customize the look and feel of a theme
|
||||
# further. For a list of options available for each theme, see the
|
||||
# documentation.
|
||||
#
|
||||
# html_theme_options = {}
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ["_static"]
|
||||
|
||||
# Custom sidebar templates, must be a dictionary that maps document names
|
||||
# to template names.
|
||||
#
|
||||
# The default sidebars (for documents that don't match any pattern) are
|
||||
# defined by theme itself. Builtin themes are using these templates by
|
||||
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
|
||||
# 'searchbox.html']``.
|
||||
#
|
||||
# html_sidebars = {}
|
||||
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = "StableBaselines3doc"
|
||||
|
||||
|
||||
# -- Options for LaTeX output ------------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
#
|
||||
# 'papersize': 'letterpaper',
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
#
|
||||
# 'pointsize': '10pt',
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
#
|
||||
# 'preamble': '',
|
||||
# Latex figure (float) alignment
|
||||
#
|
||||
# 'figure_align': 'htbp',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, "StableBaselines3.tex", "Stable Baselines3 Documentation", "Stable Baselines3 Contributors", "manual"),
|
||||
]
|
||||
|
||||
|
||||
# -- Options for manual page output ------------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [(master_doc, "stablebaselines3", "Stable Baselines3 Documentation", [author], 1)]
|
||||
|
||||
|
||||
# -- Options for Texinfo output ----------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(
|
||||
master_doc,
|
||||
"StableBaselines3",
|
||||
"Stable Baselines3 Documentation",
|
||||
author,
|
||||
"StableBaselines3",
|
||||
"One line description of project.",
|
||||
"Miscellaneous",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
|
||||
# Example configuration for intersphinx: refer to the Python standard library.
|
||||
# intersphinx_mapping = {
|
||||
# 'python': ('https://docs.python.org/3/', None),
|
||||
# 'numpy': ('http://docs.scipy.org/doc/numpy/', None),
|
||||
# 'torch': ('http://pytorch.org/docs/master/', None),
|
||||
# }
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
RL Algorithms
|
||||
=============
|
||||
|
||||
This table displays the rl algorithms that are implemented in the Stable Baselines3 contrib project,
|
||||
along with some useful characteristics: support for discrete/continuous actions, multiprocessing.
|
||||
|
||||
..
|
||||
.. ============ =========== ============ ================= =============== ================
|
||||
.. Name ``Box`` ``Discrete`` ``MultiDiscrete`` ``MultiBinary`` Multi Processing
|
||||
.. ============ =========== ============ ================= =============== ================
|
||||
.. A2C ✔️ ✔️ ✔️ ✔️ ✔️
|
||||
.. DDPG ✔️ ❌ ❌ ❌ ❌
|
||||
.. DQN ❌ ✔️ ❌ ❌ ❌
|
||||
.. PPO ✔️ ✔️ ✔️ ✔️ ✔️
|
||||
.. SAC ✔️ ❌ ❌ ❌ ❌
|
||||
.. TD3 ✔️ ❌ ❌ ❌ ❌
|
||||
.. ============ =========== ============ ================= =============== ================
|
||||
|
||||
|
||||
.. .. note::
|
||||
.. Non-array spaces such as ``Dict`` or ``Tuple`` are not currently supported by any algorithm.
|
||||
..
|
||||
.. Actions ``gym.spaces``:
|
||||
..
|
||||
.. - ``Box``: A N-dimensional box that contains every point in the action
|
||||
.. space.
|
||||
.. - ``Discrete``: A list of possible actions, where each timestep only
|
||||
.. one of the actions can be used.
|
||||
.. - ``MultiDiscrete``: A list of possible actions, where each timestep only one action of each discrete set can be used.
|
||||
.. - ``MultiBinary``: A list of possible actions, where each timestep any of the actions can be used in any combination.
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
.. _examples:
|
||||
|
||||
Examples
|
||||
========
|
||||
|
||||
WIP
|
||||
|
||||
.. PyBullet: Normalizing input features
|
||||
.. ------------------------------------
|
||||
..
|
||||
.. Normalizing input features may be essential to successful training of an RL agent
|
||||
.. (by default, images are scaled but not other types of input),
|
||||
.. for instance when training on `PyBullet <https://github.com/bulletphysics/bullet3/>`__ environments. For that, a wrapper exists and
|
||||
.. will compute a running average and standard deviation of input features (it can do the same for rewards).
|
||||
..
|
||||
|
||||
.. .. note::
|
||||
..
|
||||
.. you need to install pybullet with ``pip install pybullet``
|
||||
..
|
||||
..
|
||||
.. .. image:: ../_static/img/colab-badge.svg
|
||||
.. :target: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/pybullet.ipynb
|
||||
..
|
||||
..
|
||||
.. .. code-block:: python
|
||||
..
|
||||
.. import gym
|
||||
.. import pybullet_envs
|
||||
..
|
||||
.. from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
|
||||
.. from stable_baselines3 import PPO
|
||||
..
|
||||
.. env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
|
||||
.. # Automatically normalize the input features and reward
|
||||
.. env = VecNormalize(env, norm_obs=True, norm_reward=True,
|
||||
.. clip_obs=10.)
|
||||
..
|
||||
.. model = PPO('MlpPolicy', env)
|
||||
.. model.learn(total_timesteps=2000)
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
.. _install:
|
||||
|
||||
Installation
|
||||
============
|
||||
|
||||
Prerequisites
|
||||
-------------
|
||||
|
||||
Please read `Stable-Baselines3 installation guide <https://stable-baselines3.readthedocs.io/en/master/guide/install.html>`_ first.
|
||||
|
||||
|
||||
Stable Release
|
||||
~~~~~~~~~~~~~~
|
||||
To install Stable Baselines3 contrib with pip, execute:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install sb3-contrib
|
||||
|
||||
|
||||
Bleeding-edge version
|
||||
---------------------
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install git+https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/
|
||||
|
||||
|
||||
Development version
|
||||
-------------------
|
||||
|
||||
To contribute to Stable-Baselines3, with support for running tests and building the documentation.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
git clone https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/ && cd stable-baselines3-contrib
|
||||
pip install -e .
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
.. Stable Baselines3 documentation master file, created by
|
||||
sphinx-quickstart on Thu Sep 26 11:06:54 2019.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to Stable Baselines3 Contrib docs!
|
||||
==========================================
|
||||
|
||||
Contrib package for `Stable Baselines3 <https://github.com/DLR-RM/stable-baselines3>`_ - Experimental code.
|
||||
|
||||
|
||||
Github repository: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
|
||||
|
||||
SB3 repository: https://github.com/DLR-RM/stable-baselines3
|
||||
|
||||
RL Baselines3 Zoo (collection of pre-trained agents): https://github.com/DLR-RM/rl-baselines3-zoo
|
||||
|
||||
RL Baselines3 Zoo also offers a simple interface to train, evaluate agents and do hyperparameter tuning.
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: User Guide
|
||||
|
||||
guide/install
|
||||
guide/algos
|
||||
guide/examples
|
||||
|
||||
|
||||
.. .. toctree::
|
||||
.. :maxdepth: 1
|
||||
.. :caption: RL Algorithms
|
||||
..
|
||||
.. modules/a2c
|
||||
|
||||
.. .. toctree::
|
||||
.. :maxdepth: 1
|
||||
.. :caption: Common
|
||||
..
|
||||
.. common/atari_wrappers
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:caption: Misc
|
||||
|
||||
misc/changelog
|
||||
|
||||
|
||||
Citing Stable Baselines3
|
||||
------------------------
|
||||
To cite this project in publications:
|
||||
|
||||
.. code-block:: bibtex
|
||||
|
||||
@misc{stable-baselines3,
|
||||
author = {Raffin, Antonin and Hill, Ashley and Ernestus, Maximilian and Gleave, Adam and Kanervisto, Anssi and Dormann, Noah},
|
||||
title = {Stable Baselines3},
|
||||
year = {2019},
|
||||
publisher = {GitHub},
|
||||
journal = {GitHub repository},
|
||||
howpublished = {\url{https://github.com/DLR-RM/stable-baselines3}},
|
||||
}
|
||||
|
||||
Contributing
|
||||
------------
|
||||
|
||||
To any interested in making the rl baselines better, there are still some improvements
|
||||
that need to be done.
|
||||
You can check issues in the `repo <https://github.com/DLR-RM/stable-baselines3/issues>`_.
|
||||
|
||||
If you want to contribute, please read `CONTRIBUTING.md <https://github.com/DLR-RM/stable-baselines3/blob/master/CONTRIBUTING.md>`_ first.
|
||||
|
||||
Indices and tables
|
||||
-------------------
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`search`
|
||||
* :ref:`modindex`
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
@ECHO OFF
|
||||
|
||||
pushd %~dp0
|
||||
|
||||
REM Command file for Sphinx documentation
|
||||
|
||||
if "%SPHINXBUILD%" == "" (
|
||||
set SPHINXBUILD=sphinx-build
|
||||
)
|
||||
set SOURCEDIR=.
|
||||
set BUILDDIR=_build
|
||||
set SPHINXPROJ=StableBaselines
|
||||
|
||||
if "%1" == "" goto help
|
||||
|
||||
%SPHINXBUILD% >NUL 2>NUL
|
||||
if errorlevel 9009 (
|
||||
echo.
|
||||
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
||||
echo.installed, then set the SPHINXBUILD environment variable to point
|
||||
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
||||
echo.may add the Sphinx directory to PATH.
|
||||
echo.
|
||||
echo.If you don't have Sphinx installed, grab it from
|
||||
echo.http://sphinx-doc.org/
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
|
||||
goto end
|
||||
|
||||
:help
|
||||
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
|
||||
|
||||
:end
|
||||
popd
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
.. _changelog:
|
||||
|
||||
Changelog
|
||||
==========
|
||||
|
||||
|
||||
Pre-Release 0.10.0a0 (WIP)
|
||||
------------------------------
|
||||
|
||||
Breaking Changes:
|
||||
^^^^^^^^^^^^^^^^^
|
||||
|
||||
New Features:
|
||||
^^^^^^^^^^^^^
|
||||
|
||||
Bug Fixes:
|
||||
^^^^^^^^^^
|
||||
|
||||
Deprecations:
|
||||
^^^^^^^^^^^^^
|
||||
|
||||
Others:
|
||||
^^^^^^^
|
||||
|
||||
Documentation:
|
||||
^^^^^^^^^^^^^^
|
||||
|
||||
|
||||
Maintainers
|
||||
-----------
|
||||
|
||||
Stable-Baselines3 is currently maintained by `Antonin Raffin`_ (aka `@araffin`_), `Ashley Hill`_ (aka @hill-a),
|
||||
`Maximilian Ernestus`_ (aka @erniejunior), `Adam Gleave`_ (`@AdamGleave`_) and `Anssi Kanervisto`_ (aka `@Miffyli`_).
|
||||
|
||||
.. _Ashley Hill: https://github.com/hill-a
|
||||
.. _Antonin Raffin: https://araffin.github.io/
|
||||
.. _Maximilian Ernestus: https://github.com/erniejunior
|
||||
.. _Adam Gleave: https://gleave.me/
|
||||
.. _@araffin: https://github.com/araffin
|
||||
.. _@AdamGleave: https://github.com/adamgleave
|
||||
.. _Anssi Kanervisto: https://github.com/Miffyli
|
||||
.. _@Miffyli: https://github.com/Miffyli
|
||||
|
|
@ -0,0 +1,121 @@
|
|||
py
|
||||
env
|
||||
atari
|
||||
argparse
|
||||
Argparse
|
||||
TensorFlow
|
||||
feedforward
|
||||
envs
|
||||
VecEnv
|
||||
pretrain
|
||||
petrained
|
||||
tf
|
||||
th
|
||||
nn
|
||||
np
|
||||
str
|
||||
mujoco
|
||||
cpu
|
||||
ndarray
|
||||
ndarrays
|
||||
timestep
|
||||
timesteps
|
||||
stepsize
|
||||
dataset
|
||||
adam
|
||||
fn
|
||||
normalisation
|
||||
Kullback
|
||||
Leibler
|
||||
boolean
|
||||
deserialized
|
||||
pretrained
|
||||
minibatch
|
||||
subprocesses
|
||||
ArgumentParser
|
||||
Tensorflow
|
||||
Gaussian
|
||||
approximator
|
||||
minibatches
|
||||
hyperparameters
|
||||
hyperparameter
|
||||
vectorized
|
||||
rl
|
||||
colab
|
||||
dataloader
|
||||
npz
|
||||
datasets
|
||||
vf
|
||||
logits
|
||||
num
|
||||
Utils
|
||||
backpropagate
|
||||
prepend
|
||||
NaN
|
||||
preprocessing
|
||||
Cloudpickle
|
||||
async
|
||||
multiprocess
|
||||
tensorflow
|
||||
mlp
|
||||
cnn
|
||||
neglogp
|
||||
tanh
|
||||
coef
|
||||
repo
|
||||
Huber
|
||||
params
|
||||
ppo
|
||||
arxiv
|
||||
Arxiv
|
||||
func
|
||||
DQN
|
||||
Uhlenbeck
|
||||
Ornstein
|
||||
multithread
|
||||
cancelled
|
||||
Tensorboard
|
||||
parallelize
|
||||
customising
|
||||
serializable
|
||||
Multiprocessed
|
||||
cartpole
|
||||
toolset
|
||||
lstm
|
||||
rescale
|
||||
ffmpeg
|
||||
avconv
|
||||
unnormalized
|
||||
Github
|
||||
pre
|
||||
preprocess
|
||||
backend
|
||||
attr
|
||||
preprocess
|
||||
Antonin
|
||||
Raffin
|
||||
araffin
|
||||
Homebrew
|
||||
Numpy
|
||||
Theano
|
||||
rollout
|
||||
kfac
|
||||
Piecewise
|
||||
csv
|
||||
nvidia
|
||||
visdom
|
||||
tensorboard
|
||||
preprocessed
|
||||
namespace
|
||||
sklearn
|
||||
GoalEnv
|
||||
Torchy
|
||||
pytorch
|
||||
dicts
|
||||
optimizers
|
||||
Deprecations
|
||||
forkserver
|
||||
cuda
|
||||
Polyak
|
||||
gSDE
|
||||
rollouts
|
||||
|
|
@ -17,25 +17,25 @@ class Actor(BasePolicy):
|
|||
"""
|
||||
Actor network (policy) for TQC.
|
||||
|
||||
:param observation_space: (gym.spaces.Space) Obervation space
|
||||
:param action_space: (gym.spaces.Space) Action space
|
||||
:param net_arch: ([int]) Network architecture
|
||||
:param features_extractor: (nn.Module) Network to extract features
|
||||
:param observation_space: Obervation space
|
||||
:param action_space: Action space
|
||||
:param net_arch: Network architecture
|
||||
:param features_extractor: Network to extract features
|
||||
(a CNN when using images, a nn.Flatten() layer otherwise)
|
||||
:param features_dim: (int) Number of features
|
||||
:param activation_fn: (Type[nn.Module]) Activation function
|
||||
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: (float) Initial value for the log standard deviation
|
||||
:param full_std: (bool) Whether to use (n_features x n_actions) parameters
|
||||
:param features_dim: Number of features
|
||||
:param activation_fn: Activation function
|
||||
:param use_sde: Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: Initial value for the log standard deviation
|
||||
:param full_std: Whether to use (n_features x n_actions) parameters
|
||||
for the std instead of only (n_features,) when using gSDE.
|
||||
:param sde_net_arch: ([int]) Network architecture for extracting features
|
||||
:param sde_net_arch: Network architecture for extracting features
|
||||
when using gSDE. If None, the latent features from the policy will be used.
|
||||
Pass an empty list to use the states as features.
|
||||
:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
|
||||
:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
|
||||
a positive standard deviation (cf paper). It allows to keep variance
|
||||
above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
|
||||
:param clip_mean: (float) Clip the mean output when using gSDE to avoid numerical instability.
|
||||
:param normalize_images: (bool) Whether to normalize images or not,
|
||||
:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
|
||||
:param normalize_images: Whether to normalize images or not,
|
||||
dividing by 255.0 (True by default)
|
||||
"""
|
||||
|
||||
|
|
@ -131,7 +131,7 @@ class Actor(BasePolicy):
|
|||
but is slightly different when using ``expln`` function
|
||||
(cf StateDependentNoiseDistribution doc).
|
||||
|
||||
:return: (th.Tensor)
|
||||
:return:
|
||||
"""
|
||||
msg = "get_std() is only available when using gSDE"
|
||||
assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
|
||||
|
|
@ -141,7 +141,7 @@ class Actor(BasePolicy):
|
|||
"""
|
||||
Sample new weights for the exploration matrix, when using gSDE.
|
||||
|
||||
:param batch_size: (int)
|
||||
:param batch_size:
|
||||
"""
|
||||
msg = "reset_noise() is only available when using gSDE"
|
||||
assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
|
||||
|
|
@ -151,8 +151,8 @@ class Actor(BasePolicy):
|
|||
"""
|
||||
Get the parameters for the action distribution.
|
||||
|
||||
:param obs: (th.Tensor)
|
||||
:return: (Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]])
|
||||
:param obs:
|
||||
:return:
|
||||
Mean, standard deviation and optional keyword arguments.
|
||||
"""
|
||||
features = self.extract_features(obs)
|
||||
|
|
@ -183,31 +183,19 @@ class Actor(BasePolicy):
|
|||
def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
|
||||
return self.forward(observation, deterministic)
|
||||
|
||||
def evaluate_actions(self, obs: th.Tensor, actions: th.Tensor) -> th.Tensor:
|
||||
"""
|
||||
Evaluate actions according to the current policy,
|
||||
given the observations. Only useful when using SDE.
|
||||
:param obs: (th.Tensor)
|
||||
:param actions: (th.Tensor)
|
||||
:return: (th.Tensor) log likelihood of taking those actions
|
||||
"""
|
||||
mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
|
||||
self.action_dist.proba_distribution(mean_actions, log_std, **kwargs)
|
||||
return self.action_dist.log_prob(actions)
|
||||
|
||||
|
||||
class Critic(BaseModel):
|
||||
"""
|
||||
Critic network (q-value function) for TQC.
|
||||
|
||||
:param observation_space: (gym.spaces.Space) Obervation space
|
||||
:param action_space: (gym.spaces.Space) Action space
|
||||
:param net_arch: ([int]) Network architecture
|
||||
:param features_extractor: (nn.Module) Network to extract features
|
||||
:param observation_space: Obervation space
|
||||
:param action_space: Action space
|
||||
:param net_arch: Network architecture
|
||||
:param features_extractor: Network to extract features
|
||||
(a CNN when using images, a nn.Flatten() layer otherwise)
|
||||
:param features_dim: (int) Number of features
|
||||
:param activation_fn: (Type[nn.Module]) Activation function
|
||||
:param normalize_images: (bool) Whether to normalize images or not,
|
||||
:param features_dim: Number of features
|
||||
:param activation_fn: Activation function
|
||||
:param normalize_images: Whether to normalize images or not,
|
||||
dividing by 255.0 (True by default)
|
||||
"""
|
||||
|
||||
|
|
@ -257,28 +245,28 @@ class TQCPolicy(BasePolicy):
|
|||
"""
|
||||
Policy class (with both actor and critic) for TQC.
|
||||
|
||||
:param observation_space: (gym.spaces.Space) Observation space
|
||||
:param action_space: (gym.spaces.Space) Action space
|
||||
:param lr_schedule: (callable) Learning rate schedule (could be constant)
|
||||
:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
|
||||
:param activation_fn: (Type[nn.Module]) Activation function
|
||||
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: (float) Initial value for the log standard deviation
|
||||
:param sde_net_arch: ([int]) Network architecture for extracting features
|
||||
:param observation_space: Observation space
|
||||
:param action_space: Action space
|
||||
:param lr_schedule: Learning rate schedule (could be constant)
|
||||
:param net_arch: The specification of the policy and value networks.
|
||||
:param activation_fn: Activation function
|
||||
:param use_sde: Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: Initial value for the log standard deviation
|
||||
:param sde_net_arch: Network architecture for extracting features
|
||||
when using gSDE. If None, the latent features from the policy will be used.
|
||||
Pass an empty list to use the states as features.
|
||||
:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
|
||||
:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
|
||||
a positive standard deviation (cf paper). It allows to keep variance
|
||||
above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
|
||||
:param clip_mean: (float) Clip the mean output when using gSDE to avoid numerical instability.
|
||||
:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
|
||||
:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
|
||||
:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
|
||||
:param features_extractor_class: Features extractor to use.
|
||||
:param features_extractor_kwargs: Keyword arguments
|
||||
to pass to the feature extractor.
|
||||
:param normalize_images: (bool) Whether to normalize images or not,
|
||||
:param normalize_images: Whether to normalize images or not,
|
||||
dividing by 255.0 (True by default)
|
||||
:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
|
||||
:param optimizer_class: The optimizer to use,
|
||||
``th.optim.Adam`` by default
|
||||
:param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
|
||||
:param optimizer_kwargs: Additional keyword arguments,
|
||||
excluding the learning rate, to pass to the optimizer
|
||||
"""
|
||||
|
||||
|
|
@ -388,7 +376,7 @@ class TQCPolicy(BasePolicy):
|
|||
"""
|
||||
Sample new weights for the exploration matrix, when using gSDE.
|
||||
|
||||
:param batch_size: (int)
|
||||
:param batch_size:
|
||||
"""
|
||||
self.actor.reset_noise(batch_size=batch_size)
|
||||
|
||||
|
|
@ -412,26 +400,26 @@ class CnnPolicy(TQCPolicy):
|
|||
"""
|
||||
Policy class (with both actor and critic) for TQC.
|
||||
|
||||
:param observation_space: (gym.spaces.Space) Observation space
|
||||
:param action_space: (gym.spaces.Space) Action space
|
||||
:param lr_schedule: (callable) Learning rate schedule (could be constant)
|
||||
:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
|
||||
:param activation_fn: (Type[nn.Module]) Activation function
|
||||
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: (float) Initial value for the log standard deviation
|
||||
:param sde_net_arch: ([int]) Network architecture for extracting features
|
||||
:param observation_space: Observation space
|
||||
:param action_space: Action space
|
||||
:param lr_schedule: Learning rate schedule (could be constant)
|
||||
:param net_arch: The specification of the policy and value networks.
|
||||
:param activation_fn: Activation function
|
||||
:param use_sde: Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: Initial value for the log standard deviation
|
||||
:param sde_net_arch: Network architecture for extracting features
|
||||
when using gSDE. If None, the latent features from the policy will be used.
|
||||
Pass an empty list to use the states as features.
|
||||
:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
|
||||
:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
|
||||
a positive standard deviation (cf paper). It allows to keep variance
|
||||
above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
|
||||
:param clip_mean: (float) Clip the mean output when using gSDE to avoid numerical instability.
|
||||
:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
|
||||
:param normalize_images: (bool) Whether to normalize images or not,
|
||||
:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
|
||||
:param features_extractor_class: Features extractor to use.
|
||||
:param normalize_images: Whether to normalize images or not,
|
||||
dividing by 255.0 (True by default)
|
||||
:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
|
||||
:param optimizer_class: The optimizer to use,
|
||||
``th.optim.Adam`` by default
|
||||
:param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
|
||||
:param optimizer_kwargs: Additional keyword arguments,
|
||||
excluding the learning rate, to pass to the optimizer
|
||||
"""
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,6 @@ from stable_baselines3.common.noise import ActionNoise
|
|||
from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
|
||||
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
|
||||
from stable_baselines3.common.utils import polyak_update
|
||||
from tqdm import tqdm
|
||||
|
||||
from sb3_contrib.tqc.policies import TQCPolicy
|
||||
|
||||
|
|
@ -15,48 +14,48 @@ from sb3_contrib.tqc.policies import TQCPolicy
|
|||
class TQC(OffPolicyAlgorithm):
|
||||
"""
|
||||
|
||||
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
|
||||
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics.
|
||||
Paper: https://arxiv.org/abs/2005.04269
|
||||
|
||||
:param policy: (TQCPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, ...)
|
||||
:param env: (GymEnv or str) The environment to learn from (if registered in Gym, can be str)
|
||||
:param learning_rate: (float or callable) learning rate for adam optimizer,
|
||||
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
|
||||
:param env: The environment to learn from (if registered in Gym, can be str)
|
||||
:param learning_rate: learning rate for adam optimizer,
|
||||
the same learning rate will be used for all networks (Q-Values, Actor and Value function)
|
||||
it can be a function of the current progress remaining (from 1 to 0)
|
||||
:param buffer_size: (int) size of the replay buffer
|
||||
:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
|
||||
:param batch_size: (int) Minibatch size for each gradient update
|
||||
:param tau: (float) the soft update coefficient ("Polyak update", between 0 and 1)
|
||||
:param gamma: (float) the discount factor
|
||||
:param train_freq: (int) Update the model every ``train_freq`` steps.
|
||||
:param gradient_steps: (int) How many gradient update after each step
|
||||
:param n_episodes_rollout: (int) Update the model every ``n_episodes_rollout`` episodes.
|
||||
:param buffer_size: size of the replay buffer
|
||||
:param learning_starts: how many steps of the model to collect transitions for before learning starts
|
||||
:param batch_size: Minibatch size for each gradient update
|
||||
:param tau: the soft update coefficient ("Polyak update", between 0 and 1)
|
||||
:param gamma: the discount factor
|
||||
:param train_freq: Update the model every ``train_freq`` steps.
|
||||
:param gradient_steps: How many gradient update after each step
|
||||
:param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
|
||||
Note that this cannot be used at the same time as ``train_freq``
|
||||
:param action_noise: (ActionNoise) the action noise type (None by default), this can help
|
||||
:param action_noise: the action noise type (None by default), this can help
|
||||
for hard exploration problem. Cf common.noise for the different action noise type.
|
||||
:param optimize_memory_usage: (bool) Enable a memory efficient variant of the replay buffer
|
||||
:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
|
||||
at a cost of more complexity.
|
||||
See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
|
||||
:param ent_coef: (str or float) Entropy regularization coefficient. (Equivalent to
|
||||
:param ent_coef: Entropy regularization coefficient. (Equivalent to
|
||||
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
|
||||
Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
|
||||
:param target_update_interval: (int) update the target network every ``target_network_update_freq``
|
||||
:param target_update_interval: update the target network every ``target_network_update_freq``
|
||||
gradient steps.
|
||||
:param target_entropy: (str or float) target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
|
||||
:param use_sde: (bool) Whether to use generalized State Dependent Exploration (gSDE)
|
||||
:param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
|
||||
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
|
||||
instead of action noise exploration (default: False)
|
||||
:param sde_sample_freq: (int) Sample a new noise matrix every n steps when using gSDE
|
||||
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
|
||||
Default: -1 (only sample at the beginning of the rollout)
|
||||
:param use_sde_at_warmup: (bool) Whether to use gSDE instead of uniform sampling
|
||||
:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
|
||||
during the warm up phase (before learning starts)
|
||||
:param create_eval_env: (bool) Whether to create a second environment that will be
|
||||
:param create_eval_env: Whether to create a second environment that will be
|
||||
used for evaluating the agent periodically. (Only available when passing string for the environment)
|
||||
:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
|
||||
:param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug
|
||||
:param seed: (int) Seed for the pseudo random generators
|
||||
:param device: (str or th.device) Device (cpu, cuda, ...) on which the code should be run.
|
||||
:param policy_kwargs: additional arguments to be passed to the policy on creation
|
||||
:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
|
||||
:param seed: Seed for the pseudo random generators
|
||||
:param device: Device (cpu, cuda, ...) on which the code should be run.
|
||||
Setting it to auto, the code will be run on the GPU if possible.
|
||||
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
|
||||
:param _init_setup_model: Whether or not to build the network at the creation of the instance
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
|
|
@ -274,171 +273,6 @@ class TQC(OffPolicyAlgorithm):
|
|||
if len(ent_coef_losses) > 0:
|
||||
logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
|
||||
|
||||
def pretrain(
|
||||
self,
|
||||
gradient_steps: int,
|
||||
batch_size: int = 64,
|
||||
n_action_samples: int = -1,
|
||||
target_update_interval: int = 1,
|
||||
tau: float = 0.005,
|
||||
strategy: str = "exp",
|
||||
reduce: str = "mean",
|
||||
exp_temperature: float = 1.0,
|
||||
off_policy_update_freq: int = -1,
|
||||
) -> None:
|
||||
"""
|
||||
Pretrain with Critic Regularized Regression (CRR)
|
||||
Paper: https://arxiv.org/abs/2006.15134
|
||||
"""
|
||||
# Update optimizers learning rate
|
||||
optimizers = [self.actor.optimizer, self.critic.optimizer]
|
||||
if self.ent_coef_optimizer is not None:
|
||||
optimizers += [self.ent_coef_optimizer]
|
||||
|
||||
# Update learning rate according to lr schedule
|
||||
self._update_learning_rate(optimizers)
|
||||
|
||||
actor_losses, critic_losses = [], []
|
||||
|
||||
for gradient_step in tqdm(range(gradient_steps)):
|
||||
|
||||
if off_policy_update_freq > 0 and gradient_step % off_policy_update_freq == 0:
|
||||
self.train(gradient_steps=1, batch_size=batch_size)
|
||||
continue
|
||||
|
||||
# Sample replay buffer
|
||||
replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
|
||||
|
||||
# We need to sample because `log_std` may have changed between two gradient steps
|
||||
if self.use_sde:
|
||||
self.actor.reset_noise()
|
||||
|
||||
# Action by the current actor for the sampled state
|
||||
_, log_prob = self.actor.action_log_prob(replay_data.observations)
|
||||
log_prob = log_prob.reshape(-1, 1)
|
||||
|
||||
ent_coef_loss = None
|
||||
if self.ent_coef_optimizer is not None:
|
||||
# Important: detach the variable from the graph
|
||||
# so we don't change it with other losses
|
||||
# see https://github.com/rail-berkeley/softlearning/issues/60
|
||||
ent_coef = th.exp(self.log_ent_coef.detach())
|
||||
ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
|
||||
else:
|
||||
ent_coef = self.ent_coef_tensor
|
||||
|
||||
self.replay_buffer.ent_coef = ent_coef.item()
|
||||
|
||||
# Optimize entropy coefficient, also called
|
||||
# entropy temperature or alpha in the paper
|
||||
if ent_coef_loss is not None:
|
||||
self.ent_coef_optimizer.zero_grad()
|
||||
ent_coef_loss.backward()
|
||||
self.ent_coef_optimizer.step()
|
||||
|
||||
with th.no_grad():
|
||||
top_quantiles_to_drop = self.top_quantiles_to_drop_per_net * self.critic.n_critics
|
||||
# Select action according to policy
|
||||
next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
|
||||
# Compute and cut quantiles at the next state
|
||||
# batch x nets x quantiles
|
||||
next_z = self.critic_target(replay_data.next_observations, next_actions)
|
||||
sorted_z, _ = th.sort(next_z.reshape(batch_size, -1))
|
||||
sorted_z_part = sorted_z[:, : self.critic.quantiles_total - top_quantiles_to_drop]
|
||||
|
||||
target_q = sorted_z_part - ent_coef * next_log_prob.reshape(-1, 1)
|
||||
# td error + entropy term
|
||||
q_backup = replay_data.rewards + (1 - replay_data.dones) * self.gamma * target_q
|
||||
|
||||
# Get current Q estimates
|
||||
# using action from the replay buffer
|
||||
current_z = self.critic(replay_data.observations, replay_data.actions)
|
||||
# Compute critic loss
|
||||
critic_loss = self.quantile_huber_loss(current_z, q_backup)
|
||||
critic_losses.append(critic_loss.item())
|
||||
|
||||
# Optimize the critic
|
||||
self.critic.optimizer.zero_grad()
|
||||
critic_loss.backward()
|
||||
self.critic.optimizer.step()
|
||||
|
||||
if strategy == "bc":
|
||||
# Behavior cloning
|
||||
weight = 1
|
||||
else:
|
||||
# Tensor version: TODO: check that the reshape works as expected
|
||||
# cleaner but not faster on cpu for large batch size
|
||||
# with th.no_grad():
|
||||
# # Q-value for the action in the buffer
|
||||
# qf_buffer = self.critic(replay_data.observations, replay_data.actions).mean(2).mean(1, keepdim=True)
|
||||
# # Create tensor to avoid loop
|
||||
# # Note: For SDE, we need to sample several matrices
|
||||
# obs_ = replay_data.observations.repeat(n_action_samples, 1)
|
||||
# if self.use_sde:
|
||||
# self.actor.reset_noise(batch_size * n_action_samples)
|
||||
# actions_pi, _ = self.actor.action_log_prob(obs_)
|
||||
# qf_pi = self.critic(obs_, actions_pi.detach()).mean(2).mean(1, keepdim=True)
|
||||
# # Agregate: reduce mean or reduce max
|
||||
# if reduce == "max":
|
||||
# _, qf_agg = qf_pi.reshape(n_action_samples, batch_size, 1).max(axis=0)
|
||||
# else:
|
||||
# qf_agg = qf_pi.reshape(n_action_samples, batch_size, 1).mean(axis=0)
|
||||
with th.no_grad():
|
||||
qf_buffer = self.critic(replay_data.observations, replay_data.actions).mean(2).mean(1, keepdim=True)
|
||||
|
||||
# Use the mean (as done in AWAC, cf rlkit)
|
||||
if n_action_samples == -1:
|
||||
actions_pi = self.actor.forward(replay_data.observations, deterministic=True)
|
||||
qf_agg = self.critic(replay_data.observations, actions_pi).mean(2).mean(1, keepdim=True)
|
||||
else:
|
||||
qf_agg = None
|
||||
for _ in range(n_action_samples):
|
||||
if self.use_sde:
|
||||
self.actor.reset_noise()
|
||||
actions_pi, _ = self.actor.action_log_prob(replay_data.observations)
|
||||
|
||||
qf_pi = self.critic(replay_data.observations, actions_pi.detach()).mean(2).mean(1, keepdim=True)
|
||||
if qf_agg is None:
|
||||
if reduce == "max":
|
||||
qf_agg = qf_pi
|
||||
else:
|
||||
qf_agg = qf_pi / n_action_samples
|
||||
else:
|
||||
if reduce == "max":
|
||||
qf_agg = th.max(qf_pi, qf_agg)
|
||||
else:
|
||||
qf_agg += qf_pi / n_action_samples
|
||||
|
||||
advantage = qf_buffer - qf_agg
|
||||
if strategy == "binary":
|
||||
# binary advantage
|
||||
weight = advantage > 0
|
||||
else:
|
||||
# exp advantage
|
||||
exp_clip = 20.0
|
||||
weight = th.clamp(th.exp(advantage / exp_temperature), 0.0, exp_clip)
|
||||
|
||||
# Log prob by the current actor for the sampled state and action
|
||||
log_prob = self.actor.evaluate_actions(replay_data.observations, replay_data.actions)
|
||||
log_prob = log_prob.reshape(-1, 1)
|
||||
|
||||
# weigthed regression loss (close to policy gradient loss)
|
||||
actor_loss = (-log_prob * weight).mean()
|
||||
# actor_loss = ((actions_pi - replay_data.actions * weight) ** 2).mean()
|
||||
actor_losses.append(actor_loss.item())
|
||||
|
||||
# Optimize the actor
|
||||
self.actor.optimizer.zero_grad()
|
||||
actor_loss.backward()
|
||||
self.actor.optimizer.step()
|
||||
|
||||
# Update target networks
|
||||
if gradient_step % target_update_interval == 0:
|
||||
polyak_update(self.critic.parameters(), self.critic_target.parameters(), tau)
|
||||
|
||||
if self.use_sde:
|
||||
print(f"std={(self.actor.get_std()).mean().item()}")
|
||||
|
||||
def learn(
|
||||
self,
|
||||
total_timesteps: int,
|
||||
|
|
@ -469,7 +303,7 @@ class TQC(OffPolicyAlgorithm):
|
|||
Returns the names of the parameters that should be excluded by default
|
||||
when saving the model.
|
||||
|
||||
:return: (List[str]) List of parameters that should be excluded from save
|
||||
:return: List of parameters that should be excluded from save
|
||||
"""
|
||||
# Exclude aliases
|
||||
return super(TQC, self)._excluded_save_params() + ["actor", "critic", "critic_target"]
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
0.9.0a2
|
||||
0.10.0a0
|
||||
|
|
|
|||
6
setup.py
6
setup.py
|
|
@ -23,11 +23,7 @@ setup(
|
|||
packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
|
||||
package_data={"sb3_contrib": ["py.typed", "version.txt"]},
|
||||
install_requires=[
|
||||
"stable_baselines3[tests,docs]>=0.9.0a0",
|
||||
# For progress bar when using CRR
|
||||
"tqdm"
|
||||
# Enable CMA
|
||||
# "cma",
|
||||
"stable_baselines3[tests,docs]>=0.9.0",
|
||||
],
|
||||
description="Contrib package of Stable Baselines3, experimental code.",
|
||||
author="Antonin Raffin",
|
||||
|
|
|
|||
|
|
@ -14,41 +14,27 @@ def test_tqc(ent_coef):
|
|||
create_eval_env=True,
|
||||
ent_coef=ent_coef,
|
||||
)
|
||||
model.learn(total_timesteps=1000, eval_freq=500)
|
||||
model.learn(total_timesteps=500, eval_freq=250)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("n_critics", [1, 3])
|
||||
def test_n_critics(n_critics):
|
||||
# Test TQC with different number of critics
|
||||
model = TQC(
|
||||
"MlpPolicy", "Pendulum-v0", policy_kwargs=dict(net_arch=[64, 64], n_critics=n_critics), learning_starts=100, verbose=1
|
||||
"MlpPolicy", "Pendulum-v0", policy_kwargs=dict(net_arch=[64], n_critics=n_critics), learning_starts=100, verbose=1
|
||||
)
|
||||
model.learn(total_timesteps=1000)
|
||||
model.learn(total_timesteps=500)
|
||||
|
||||
|
||||
# "CartPole-v1"
|
||||
# @pytest.mark.parametrize("env_id", ["MountainCarContinuous-v0"])
|
||||
# def test_cmaes(env_id):
|
||||
# if CMAES is None:
|
||||
# return
|
||||
# model = CMAES("MlpPolicy", env_id, seed=0, policy_kwargs=dict(net_arch=[64]), verbose=1, create_eval_env=True)
|
||||
# model.learn(total_timesteps=50000, eval_freq=10000)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("strategy", ["exp", "bc", "binary"])
|
||||
@pytest.mark.parametrize("reduce", ["mean", "max"])
|
||||
def test_crr(tmp_path, strategy, reduce):
|
||||
def test_sde():
|
||||
model = TQC(
|
||||
"MlpPolicy",
|
||||
"Pendulum-v0",
|
||||
policy_kwargs=dict(net_arch=[64]),
|
||||
learning_starts=1000,
|
||||
policy_kwargs=dict(net_arch=[64], sde_net_arch=[8]),
|
||||
use_sde=True,
|
||||
learning_starts=100,
|
||||
verbose=1,
|
||||
create_eval_env=True,
|
||||
action_noise=None,
|
||||
use_sde=False,
|
||||
)
|
||||
|
||||
model.learn(total_timesteps=1000, eval_freq=0)
|
||||
for n_action_samples in [1, 2, -1]:
|
||||
model.pretrain(gradient_steps=32, batch_size=32, n_action_samples=n_action_samples, strategy=strategy, reduce=reduce)
|
||||
model.learn(total_timesteps=500)
|
||||
model.policy.reset_noise()
|
||||
model.policy.actor.get_std()
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
import os
|
||||
import pathlib
|
||||
import warnings
|
||||
from collections import OrderedDict
|
||||
from copy import deepcopy
|
||||
|
||||
|
|
@ -11,7 +10,6 @@ import torch as th
|
|||
from stable_baselines3 import DQN
|
||||
from stable_baselines3.common.base_class import BaseAlgorithm
|
||||
from stable_baselines3.common.identity_env import FakeImageEnv, IdentityEnv, IdentityEnvBox
|
||||
from stable_baselines3.common.save_util import load_from_pkl, open_path, save_to_pkl
|
||||
from stable_baselines3.common.utils import get_device
|
||||
from stable_baselines3.common.vec_env import DummyVecEnv
|
||||
|
||||
|
|
@ -45,7 +43,7 @@ def test_save_load(tmp_path, model_class):
|
|||
|
||||
# create model
|
||||
model = model_class("MlpPolicy", env, policy_kwargs=dict(net_arch=[16]), verbose=1)
|
||||
model.learn(total_timesteps=500, eval_freq=250)
|
||||
model.learn(total_timesteps=500)
|
||||
|
||||
env.reset()
|
||||
observations = np.concatenate([env.step([env.action_space.sample()])[0] for _ in range(10)], axis=0)
|
||||
|
|
@ -154,7 +152,7 @@ def test_save_load(tmp_path, model_class):
|
|||
assert np.allclose(selected_actions, new_selected_actions, 1e-4)
|
||||
|
||||
# check if learn still works
|
||||
model.learn(total_timesteps=1000, eval_freq=500)
|
||||
model.learn(total_timesteps=500)
|
||||
|
||||
del model
|
||||
|
||||
|
|
@ -177,17 +175,17 @@ def test_set_env(model_class):
|
|||
# create model
|
||||
model = model_class("MlpPolicy", env, policy_kwargs=dict(net_arch=[16]))
|
||||
# learn
|
||||
model.learn(total_timesteps=1000, eval_freq=500)
|
||||
model.learn(total_timesteps=300)
|
||||
|
||||
# change env
|
||||
model.set_env(env2)
|
||||
# learn again
|
||||
model.learn(total_timesteps=1000, eval_freq=500)
|
||||
model.learn(total_timesteps=300)
|
||||
|
||||
# change env test wrapping
|
||||
model.set_env(env3)
|
||||
# learn again
|
||||
model.learn(total_timesteps=1000, eval_freq=500)
|
||||
model.learn(total_timesteps=300)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model_class", MODEL_LIST)
|
||||
|
|
@ -247,45 +245,6 @@ def test_save_load_replay_buffer(tmp_path, model_class):
|
|||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model_class", [TQC])
|
||||
@pytest.mark.parametrize("optimize_memory_usage", [False, True])
|
||||
def test_warn_buffer(recwarn, model_class, optimize_memory_usage):
|
||||
"""
|
||||
When using memory efficient replay buffer,
|
||||
a warning must be emitted when calling `.learn()`
|
||||
multiple times.
|
||||
See https://github.com/DLR-RM/stable-baselines3/issues/46
|
||||
"""
|
||||
# remove gym warnings
|
||||
warnings.filterwarnings(action="ignore", category=DeprecationWarning)
|
||||
warnings.filterwarnings(action="ignore", category=UserWarning, module="gym")
|
||||
|
||||
model = model_class(
|
||||
"MlpPolicy",
|
||||
select_env(model_class),
|
||||
buffer_size=100,
|
||||
optimize_memory_usage=optimize_memory_usage,
|
||||
policy_kwargs=dict(net_arch=[64]),
|
||||
learning_starts=10,
|
||||
)
|
||||
|
||||
model.learn(150)
|
||||
|
||||
model.learn(150, reset_num_timesteps=False)
|
||||
|
||||
# Check that there is no warning
|
||||
assert len(recwarn) == 0
|
||||
|
||||
model.learn(150)
|
||||
|
||||
if optimize_memory_usage:
|
||||
assert len(recwarn) == 1
|
||||
warning = recwarn.pop(UserWarning)
|
||||
assert "The last trajectory in the replay buffer will be truncated" in str(warning.message)
|
||||
else:
|
||||
assert len(recwarn) == 0
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model_class", MODEL_LIST)
|
||||
@pytest.mark.parametrize("policy_str", ["MlpPolicy", "CnnPolicy"])
|
||||
def test_save_load_policy(tmp_path, model_class, policy_str):
|
||||
|
|
@ -309,7 +268,7 @@ def test_save_load_policy(tmp_path, model_class, policy_str):
|
|||
|
||||
# create model
|
||||
model = model_class(policy_str, env, policy_kwargs=dict(net_arch=[16]), verbose=1, **kwargs)
|
||||
model.learn(total_timesteps=500, eval_freq=250)
|
||||
model.learn(total_timesteps=500)
|
||||
|
||||
env.reset()
|
||||
observations = np.concatenate([env.step([env.action_space.sample()])[0] for _ in range(10)], axis=0)
|
||||
|
|
@ -375,52 +334,3 @@ def test_save_load_policy(tmp_path, model_class, policy_str):
|
|||
os.remove(tmp_path / "policy.pkl")
|
||||
if actor_class is not None:
|
||||
os.remove(tmp_path / "actor.pkl")
|
||||
|
||||
|
||||
@pytest.mark.parametrize("pathtype", [str, pathlib.Path])
|
||||
def test_open_file_str_pathlib(tmp_path, pathtype):
|
||||
# check that suffix isn't added because we used open_path first
|
||||
with open_path(pathtype(f"{tmp_path}/t1"), "w") as fp1:
|
||||
save_to_pkl(fp1, "foo")
|
||||
assert fp1.closed
|
||||
with pytest.warns(None) as record:
|
||||
assert load_from_pkl(pathtype(f"{tmp_path}/t1")) == "foo"
|
||||
assert not record
|
||||
|
||||
# test custom suffix
|
||||
with open_path(pathtype(f"{tmp_path}/t1.custom_ext"), "w") as fp1:
|
||||
save_to_pkl(fp1, "foo")
|
||||
assert fp1.closed
|
||||
with pytest.warns(None) as record:
|
||||
assert load_from_pkl(pathtype(f"{tmp_path}/t1.custom_ext")) == "foo"
|
||||
assert not record
|
||||
|
||||
# test without suffix
|
||||
with open_path(pathtype(f"{tmp_path}/t1"), "w", suffix="pkl") as fp1:
|
||||
save_to_pkl(fp1, "foo")
|
||||
assert fp1.closed
|
||||
with pytest.warns(None) as record:
|
||||
assert load_from_pkl(pathtype(f"{tmp_path}/t1.pkl")) == "foo"
|
||||
assert not record
|
||||
|
||||
# test that a warning is raised when the path doesn't exist
|
||||
with open_path(pathtype(f"{tmp_path}/t2.pkl"), "w") as fp1:
|
||||
save_to_pkl(fp1, "foo")
|
||||
assert fp1.closed
|
||||
with pytest.warns(None) as record:
|
||||
assert load_from_pkl(open_path(pathtype(f"{tmp_path}/t2"), "r", suffix="pkl")) == "foo"
|
||||
assert len(record) == 0
|
||||
|
||||
with pytest.warns(None) as record:
|
||||
assert load_from_pkl(open_path(pathtype(f"{tmp_path}/t2"), "r", suffix="pkl", verbose=2)) == "foo"
|
||||
assert len(record) == 1
|
||||
|
||||
fp = pathlib.Path(f"{tmp_path}/t2").open("w")
|
||||
fp.write("rubbish")
|
||||
fp.close()
|
||||
# test that a warning is only raised when verbose = 0
|
||||
with pytest.warns(None) as record:
|
||||
open_path(pathtype(f"{tmp_path}/t2"), "w", suffix="pkl", verbose=0).close()
|
||||
open_path(pathtype(f"{tmp_path}/t2"), "w", suffix="pkl", verbose=1).close()
|
||||
open_path(pathtype(f"{tmp_path}/t2"), "w", suffix="pkl", verbose=2).close()
|
||||
assert len(record) == 1
|
||||
|
|
|
|||
Loading…
Reference in New Issue