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values | repo_created_at timestamp[s]date 2012-07-24 23:12:50 2025-06-16 08:07:28 ⌀ | repo_updated_at timestamp[s]date 2026-02-23 15:23:15 2026-05-03 18:52:12 ⌀ | repo_topics listlengths 0 13 ⌀ | repo_languages unknown | is_fork bool 1
class | open_issues int64 3 104 ⌀ | file_path stringlengths 3 208 | file_name stringclasses 509
values | file_extension stringclasses 1
value | file_size_bytes int64 101 84k ⌀ | file_url stringclasses 627
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values | parsed_at stringdate 2026-05-04 01:12:36 2026-05-04 19:41:55 | text stringlengths 100 102k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sloria/TextBlob | https://github.com/sloria/TextBlob | null | null | null | null | 9,518 | null | null | mit | null | null | null | null | null | null | null | tests/test_np_extractor.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:04.257408 | import unittest
import nltk
import pytest
from textblob.base import BaseNPExtractor
from textblob.np_extractors import ConllExtractor
from textblob.utils import filter_insignificant
class TestConllExtractor(unittest.TestCase):
def setUp(self):
self.extractor = ConllExtractor()
self.text = """
Py... |
sloria/TextBlob | https://github.com/sloria/TextBlob | null | null | null | null | 9,518 | null | null | mit | null | null | null | null | null | null | null | tests/test_parsers.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:04.278789 | import unittest
from textblob.en import parse as pattern_parse
from textblob.parsers import PatternParser
class TestPatternParser(unittest.TestCase):
def setUp(self):
self.parser = PatternParser()
self.text = "And now for something completely different."
def test_parse(self):
assert ... |
sloria/TextBlob | https://github.com/sloria/TextBlob | null | null | null | null | 9,518 | null | null | mit | null | null | null | null | null | null | null | tests/test_decorators.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:04.343307 | import unittest
import pytest
from textblob.decorators import requires_nltk_corpus
from textblob.exceptions import MissingCorpusError
class Tokenizer:
@requires_nltk_corpus
def tag(self, text):
raise LookupError
def test_decorator_raises_missing_corpus_exception():
t = Tokenizer()
with pyt... |
sloria/TextBlob | https://github.com/sloria/TextBlob | null | null | null | null | 9,518 | null | null | mit | null | null | null | null | null | null | null | tests/test_sentiments.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:04.343937 | import unittest
import pytest
from textblob.sentiments import (
CONTINUOUS,
DISCRETE,
NaiveBayesAnalyzer,
PatternAnalyzer,
)
class TestPatternSentiment(unittest.TestCase):
def setUp(self):
self.analyzer = PatternAnalyzer()
def test_kind(self):
assert self.analyzer.kind == CO... |
sloria/TextBlob | https://github.com/sloria/TextBlob | null | null | null | null | 9,518 | null | null | mit | null | null | null | null | null | null | null | tests/test_taggers.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:04.387765 | import os
import unittest
import pytest
import textblob.taggers
from textblob.base import BaseTagger
HERE = os.path.abspath(os.path.dirname(__file__))
AP_MODEL_LOC = os.path.join(HERE, "trontagger.pickle")
class TestPatternTagger(unittest.TestCase):
def setUp(self):
self.text = "Simple is better than c... |
sloria/TextBlob | https://github.com/sloria/TextBlob | null | null | null | null | 9,518 | null | null | mit | null | null | null | null | null | null | null | tests/test_utils.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:04.789558 | import os
from unittest import TestCase
from textblob.utils import is_filelike, lowerstrip, strip_punc
HERE = os.path.abspath(os.path.dirname(__file__))
CSV_FILE = os.path.join(HERE, "data.csv")
class UtilsTests(TestCase):
def setUp(self):
self.text = "this. Has. Punctuation?! "
def test_strip_punc... |
sloria/TextBlob | https://github.com/sloria/TextBlob | null | null | null | null | 9,518 | null | null | mit | null | null | null | null | null | null | null | tests/test_tokenizers.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:04.932749 | import unittest
import pytest
from textblob.tokenizers import (
SentenceTokenizer,
WordTokenizer,
sent_tokenize,
word_tokenize,
)
def is_generator(obj):
return hasattr(obj, "__next__")
class TestWordTokenizer(unittest.TestCase):
def setUp(self):
self.tokenizer = WordTokenizer()
... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/_torch_specific.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.384366 | """
Specialization of einops for torch.
Unfortunately, torch's jit scripting mechanism isn't strong enough,
and to have scripting supported at least for layers,
a number of additional moves is needed.
Design of main operations (dynamic resolution by lookup) is unlikely
to be implemented by torch.jit.script,
but torch... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/experimental/indexing.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.386073 | """
This file contained some thoughts on indexing.
These ideas were developed further in eindex (separate package).
"""
|
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/_einmix.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.387652 | import string
import warnings
from typing import Any
from einops import EinopsError
from einops.einops import _product
from einops.parsing import ParsedExpression, _ellipsis
def _report_axes(axes: set, report_message: str):
if len(axes) > 0:
raise EinopsError(report_message.format(axes))
class _EinmixM... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/array_api.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.389060 | from collections.abc import Sequence
from types import ModuleType
from typing import TYPE_CHECKING, Protocol, TypeAlias, TypeVar, cast
from .einops import EinopsError, Reduction, _apply_recipe_array_api, _prepare_transformation_recipe
from .packing import analyze_pattern, prod
if TYPE_CHECKING:
from typing_extens... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/_backends.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.390147 | """
Backends in `einops` are organized to meet the following requirements
- backends are not imported unless those are actually needed, because
- backends may not be installed
- importing all available backends will drive to significant memory footprint
- backends may be present but installed with errors (b... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.391799 | # imports can use EinopsError class
# ruff: noqa: E402
__author__ = "Alex Rogozhnikov"
__version__ = "0.9.0dev"
class EinopsError(RuntimeError):
"""Runtime error thrown by einops"""
pass # noqa: PIE790
__all__ = ["EinopsError", "asnumpy", "einsum", "pack", "parse_shape", "rearrange", "reduce", "repeat", ... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.392732 | __author__ = "Alex Rogozhnikov"
from typing import Any
from einops import EinopsError
from einops.einops import TransformRecipe, _apply_recipe, _prepare_recipes_for_all_dims, get_backend
class RearrangeMixin:
"""
Rearrange layer behaves identically to einops.rearrange operation.
:param pattern: str, re... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/einops.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.409823 | import functools
import itertools
import string
import typing
from collections import OrderedDict
from typing import Any, Protocol, TypeAlias, TypeVar, cast, overload
from . import EinopsError
from ._backends import get_backend
from .parsing import AnonymousAxis, ParsedExpression, _ellipsis
Tensor = TypeVar("Tensor")... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | docs/utils/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:07.443535 | import numpy as np
from IPython import get_ipython
from IPython.display import display_html
from PIL.Image import fromarray
def display_np_arrays_as_images():
def np_to_png(a):
if 2 <= len(a.shape) <= 3:
return fromarray(np.array(np.clip(a, 0, 1) * 255, dtype="uint8"))._repr_png_()
els... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/flax.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.003543 | from dataclasses import field
from typing import cast
import flax.linen as nn
import jax
import jax.numpy as jnp
from . import RearrangeMixin, ReduceMixin
from ._einmix import _EinmixMixin
__author__ = "Alex Rogozhnikov"
class Reduce(nn.Module):
pattern: str
reduction: str
sizes: dict = field(default_f... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/tensorflow.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.012661 | """
Comment about tensorflow layers:
unfortunately instructions on creation of TF layers change constantly,
and changed way too many times at this point to remember what-compatible-where.
Layers in einops==0.7.0 (and several prior versions)
are compatible with TF 2.13
Layers in einops==0.8.0 were re-implemented
acc... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/packing.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.031587 | from collections.abc import Sequence
from functools import lru_cache
from typing import TypeAlias
from einops import EinopsError
from einops._backends import get_backend
from einops.einops import Tensor
from einops.parsing import ParsedExpression
Shape: TypeAlias = Sequence[int]
@lru_cache(maxsize=128)
def analyze_... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/paddle.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.041491 | from typing import cast
import paddle
from . import RearrangeMixin, ReduceMixin
from ._einmix import _EinmixMixin
__author__ = "PaddlePaddle"
class Rearrange(RearrangeMixin, paddle.nn.Layer):
def forward(self, input):
return self._apply_recipe(input)
class Reduce(ReduceMixin, paddle.nn.Layer):
de... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/keras.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.054340 | __author__ = "Alex Rogozhnikov"
from einops.layers.tensorflow import EinMix, Rearrange, Reduce
keras_custom_objects = {
Rearrange.__name__: Rearrange,
Reduce.__name__: Reduce,
EinMix.__name__: EinMix,
}
|
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/oneflow.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.058152 | from typing import cast
import oneflow as flow
from . import RearrangeMixin, ReduceMixin
from ._einmix import _EinmixMixin
__author__ = "Tianhe Ren & Depeng Liang"
class Rearrange(RearrangeMixin, flow.nn.Module):
def forward(self, input):
return self._apply_recipe(input)
class Reduce(ReduceMixin, flo... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/layers/torch.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.059501 | from typing import cast
import torch
from einops._torch_specific import apply_for_scriptable_torch
from . import RearrangeMixin, ReduceMixin
from ._einmix import _EinmixMixin
__author__ = "Alex Rogozhnikov"
class Rearrange(RearrangeMixin, torch.nn.Module):
def forward(self, input):
recipe = self._mult... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/parsing.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.083436 | import keyword
import warnings
from einops import EinopsError
_ellipsis: str = "…" # NB, this is a single unicode symbol. String is used as it is not a list, but can be iterated
class AnonymousAxis:
"""Important thing: all instances of this class are not equal to each other"""
def __init__(self, value: st... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.095997 | """
Common utils for testing.
These functions allow testing only some frameworks, not all.
"""
import logging
import os
import warnings
from functools import lru_cache
from einops import _backends
__author__ = "Alex Rogozhnikov"
# minimize noise in tests logging
logging.getLogger("tensorflow").disabled = True
logg... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/run_tests.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.117519 | """
Runs tests that are appropriate for framework.
"""
import os
import sys
from pathlib import Path
from subprocess import Popen
__author__ = "Alex Rogozhnikov"
def run(cmd, **env):
# keeps printing output when testing
cmd = cmd.split(" ") if isinstance(cmd, str) else cmd
print("running:", cmd)
p =... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_array_api.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.607390 | import itertools
import numpy as np
import pytest
from einops import rearrange, reduce, repeat
from .test_ops import equivalent_rearrange_patterns, equivalent_reduction_patterns, identity_patterns, repeat_test_cases
def test_rearrange_array_api():
import numpy as xp
from einops import array_api as AA
... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_examples.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.642302 | import numpy as np
import pytest
from einops import parse_shape, rearrange, reduce
from einops.tests import is_backend_tested
from einops.tests.test_ops import imp_op_backends
def test_rearrange_examples():
def test1(x):
# transpose
y = rearrange(x, "b c h w -> b h w c")
assert tuple(y.sh... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_layers.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:08.657365 | import pickle
from collections import namedtuple
import numpy as np
import pytest
from einops import EinopsError, rearrange, reduce
from einops.tests import FLOAT_REDUCTIONS as REDUCTIONS
from einops.tests import collect_test_backends, is_backend_tested
__author__ = "Alex Rogozhnikov"
testcase = namedtuple("testcas... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | scripts/convert_readme.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.028110 | """
Converts readme from github repo page to mkdocs-friendly
"""
from pathlib import Path
original_text = Path(__file__).parent.parent.joinpath("README.md").read_text(encoding="utf-8")
def replace_with_video_tag(line: str):
if line.startswith("https://") and line.endswith(".mp4") and " " not in line:
# ... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_einsum.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.029085 | import string
from collections.abc import Callable
from typing import Any
import numpy as np
import pytest
from einops.einops import EinopsError, _compactify_pattern_for_einsum, einsum
from einops.tests import collect_test_backends
class Arguments:
def __init__(self, *args: Any, **kargs: Any):
self.args... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_parsing.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.029823 | import pytest
from einops import EinopsError
from einops.parsing import AnonymousAxis, ParsedExpression, _ellipsis
__author__ = "Alex Rogozhnikov"
class AnonymousAxisPlaceholder:
def __init__(self, value: int):
self.value = value
assert isinstance(self.value, int)
def __eq__(self, other):
... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | scripts/pytorch_examples_source/converter.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.030321 | # type: ignore
"""
just run this script with python converter.py .
It will convert pytorch.ipynb to html page docs/pytorch-examples.html
"""
from pathlib import Path
import markdown
import nbformat
from pygments import highlight
from pygments.formatters import HtmlFormatter
from pygments.lexers import PythonLexer
n... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_ops.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.030961 | import itertools
import numpy as np
import pytest
from einops import EinopsError
from einops.einops import _enumerate_directions, rearrange, reduce, repeat
from einops.tests import FLOAT_REDUCTIONS as REDUCTIONS
from einops.tests import collect_test_backends, is_backend_tested
imp_op_backends = collect_test_backends... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_other.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.031517 | import subprocess
import tempfile
from doctest import testmod
from pathlib import Path
import numpy as np
import pytest
import einops
import einops.layers
from einops._backends import AbstractBackend
from einops.einops import _optimize_transformation, parse_shape, rearrange
from einops.tests import collect_test_backe... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | einops/tests/test_packing.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.172795 | import dataclasses
import numpy as np
import pytest
from einops import EinopsError, asnumpy, pack, unpack
from einops.tests import collect_test_backends
rng = np.random.default_rng()
def pack_unpack(xs, pattern):
x, ps = pack(xs, pattern)
unpacked = unpack(xs, ps, pattern)
assert len(unpacked) == len(x... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | scripts/test_notebooks.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.838054 | """
Script assumes torch, tf and numpy are already installed.
Also needs:
"nbformat",
"nbconvert",
"jupyter",
"pillow",
"""
from io import StringIO
__author__ = "Alex Rogozhnikov"
from pathlib import Path
import nbformat
from nbconvert.preprocessors import ExecutePreprocessor
def render_notebook(filename... |
arogozhnikov/einops | https://github.com/arogozhnikov/einops | null | null | null | null | 9,476 | null | null | mit | null | null | null | null | null | null | null | scripts/setup.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:09.867680 | """
This is a fake script, it is not used.
Github does not count dependent python projects unless you have a setup.py
"""
__author__ = "Alex Rogozhnikov"
from pathlib import Path
from setuptools import setup
setup(
name="einops",
version="0.7.0",
description="A new flavour of deep learning operations",
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/11_Dyna_Q/maze_env.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.379276 | """
Reinforcement learning maze example.
Red rectangle: explorer.
Black rectangles: hells [reward = -1].
Yellow bin circle: paradise [reward = +1].
All other states: ground [reward = 0].
This script is the environment part of this example. The RL is in RL_brain.py.
View more o... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/10_A3C/A3C_discrete_action.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.382847 | """
Asynchronous Advantage Actor Critic (A3C) with discrete action space, Reinforcement Learning.
The Cartpole example.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
tensorflow 1.8.0
gym 0.10.5
"""
import multiprocessing
import threading
import tensorflow as tf
import numpy as np
imp... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/12_Proximal_Policy_Optimization/simply_PPO.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.384838 | """
A simple version of Proximal Policy Optimization (PPO) using single thread.
Based on:
1. Emergence of Locomotion Behaviours in Rich Environments (Google Deepmind): [https://arxiv.org/abs/1707.02286]
2. Proximal Policy Optimization Algorithms (OpenAI): [https://arxiv.org/abs/1707.06347]
View more on my tutorial we... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/12_Proximal_Policy_Optimization/discrete_DPPO.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.386013 | """
A simple version of OpenAI's Proximal Policy Optimization (PPO). [https://arxiv.org/abs/1707.06347]
Distributing workers in parallel to collect data, then stop worker's roll-out and train PPO on collected data.
Restart workers once PPO is updated.
The global PPO updating rule is adopted from DeepMind's paper (DPP... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/12_Proximal_Policy_Optimization/DPPO.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.391013 | """
A simple version of OpenAI's Proximal Policy Optimization (PPO). [https://arxiv.org/abs/1707.06347]
Distributing workers in parallel to collect data, then stop worker's roll-out and train PPO on collected data.
Restart workers once PPO is updated.
The global PPO updating rule is adopted from DeepMind's paper (DPP... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/10_A3C/A3C_distributed_tf.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.392569 | """
Asynchronous Advantage Actor Critic (A3C) with discrete action space, Reinforcement Learning.
The Cartpole example using distributed tensorflow + multiprocessing.
View more on my tutorial page: https://morvanzhou.github.io/
"""
import multiprocessing as mp
import tensorflow as tf
import numpy as np
import gym, ... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/11_Dyna_Q/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.394465 | """
This part of code is the Dyna-Q learning brain, which is a brain of the agent.
All decisions and learning processes are made in here.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
"""
import numpy as np
import pandas as pd
class QLearningTable:
def __init__(self, actions, learning_r... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/10_A3C/A3C_continuous_action.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.395754 | """
Asynchronous Advantage Actor Critic (A3C) with continuous action space, Reinforcement Learning.
The Pendulum example.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
tensorflow 1.8.0
gym 0.10.5
"""
import multiprocessing
import threading
import tensorflow as tf
import numpy as np
i... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/11_Dyna_Q/run_this.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.398558 | """
Simplest model-based RL, Dyna-Q.
Red rectangle: explorer.
Black rectangles: hells [reward = -1].
Yellow bin circle: paradise [reward = +1].
All other states: ground [reward = 0].
This script is the main part which controls the update method of this example.
The RL is in RL_... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/10_A3C/A3C_RNN.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:12.403549 | """
Asynchronous Advantage Actor Critic (A3C) + RNN with continuous action space, Reinforcement Learning.
The Pendulum example.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
tensorflow 1.8.0
gym 0.10.5
"""
import multiprocessing
import threading
import tensorflow as tf
import numpy a... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/2_Q_Learning_maze/run_this.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:13.218338 | """
Reinforcement learning maze example.
Red rectangle: explorer.
Black rectangles: hells [reward = -1].
Yellow bin circle: paradise [reward = +1].
All other states: ground [reward = 0].
This script is the main part which controls the update method of this example.
The RL is in... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/4_Sarsa_lambda_maze/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:13.385270 | """
This part of code is the Q learning brain, which is a brain of the agent.
All decisions are made in here.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
"""
import numpy as np
import pandas as pd
class RL(object):
def __init__(self, action_space, learning_rate=0.01, reward_decay=0.9,... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/1_command_line_reinforcement_learning/treasure_on_right.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:13.388170 | """
A simple example for Reinforcement Learning using table lookup Q-learning method.
An agent "o" is on the left of a 1 dimensional world, the treasure is on the rightmost location.
Run this program and to see how the agent will improve its strategy of finding the treasure.
View more on my tutorial page: https://morv... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/4_Sarsa_lambda_maze/run_this.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:13.389862 | """
Sarsa is a online updating method for Reinforcement learning.
Unlike Q learning which is a offline updating method, Sarsa is updating while in the current trajectory.
You will see the sarsa is more coward when punishment is close because it cares about all behaviours,
while q learning is more brave because it onl... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/4_Sarsa_lambda_maze/maze_env.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:13.399443 | """
Reinforcement learning maze example.
Red rectangle: explorer.
Black rectangles: hells [reward = -1].
Yellow bin circle: paradise [reward = +1].
All other states: ground [reward = 0].
This script is the environment part of this example.
The RL is in RL_brain.py.
View more o... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/3_Sarsa_maze/maze_env.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:13.402188 | """
Reinforcement learning maze example.
Red rectangle: explorer.
Black rectangles: hells [reward = -1].
Yellow bin circle: paradise [reward = +1].
All other states: ground [reward = 0].
This script is the environment part of this example.
The RL is in RL_brain.py.
View more o... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/2_Q_Learning_maze/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.068583 | """
This part of code is the Q learning brain, which is a brain of the agent.
All decisions are made in here.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
"""
import numpy as np
import pandas as pd
class QLearningTable:
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, ... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/2_Q_Learning_maze/maze_env.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.146520 | """
Reinforcement learning maze example.
Red rectangle: explorer.
Black rectangles: hells [reward = -1].
Yellow bin circle: paradise [reward = +1].
All other states: ground [reward = 0].
This script is the environment part of this example. The RL is in RL_brain.py.
View more o... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/3_Sarsa_maze/run_this.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.151140 | """
Sarsa is a online updating method for Reinforcement learning.
Unlike Q learning which is a offline updating method, Sarsa is updating while in the current trajectory.
You will see the sarsa is more coward when punishment is close because it cares about all behaviours,
while q learning is more brave because it onl... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5.1_Double_DQN/run_Pendulum.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.657856 | """
Double DQN & Natural DQN comparison,
The Pendulum example.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import gym
from RL_brain import DoubleDQN
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
env = gym.make('Pendulum-... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5.3_Dueling_DQN/run_Pendulum.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.659728 | """
Dueling DQN & Natural DQN comparison
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import gym
from RL_brain import DuelingDQN
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
env = gym.make('Pendulum-v0')
env = env.unwrap... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5.3_Dueling_DQN/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.661531 | """
The Dueling DQN based on this paper: https://arxiv.org/abs/1511.06581
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
import tensorflow as tf
np.random.seed(1)
tf.set_random_seed(1)
class DuelingDQN:
def __init__(
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5.2_Prioritized_Replay_DQN/run_MountainCar.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.662622 | """
The DQN improvement: Prioritized Experience Replay (based on https://arxiv.org/abs/1511.05952)
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import gym
from RL_brain import DQNPrioritizedReplay
import matplotlib.pyplot as plt
import tensorflow as tf... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5.2_Prioritized_Replay_DQN/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:14.664221 | """
The DQN improvement: Prioritized Experience Replay (based on https://arxiv.org/abs/1511.05952)
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
import tensorflow as tf
np.random.seed(1)
tf.set_random_seed(1)
class SumTree(object):
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5.1_Double_DQN/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:15.487450 | """
The double DQN based on this paper: https://arxiv.org/abs/1509.06461
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
import tensorflow as tf
np.random.seed(1)
tf.set_random_seed(1)
class DoubleDQN:
def __init__(
se... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/3_Sarsa_maze/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:15.488553 | """
This part of code is the Q learning brain, which is a brain of the agent.
All decisions are made in here.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
"""
import numpy as np
import pandas as pd
class RL(object):
def __init__(self, action_space, learning_rate=0.01, reward_decay=0.9,... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5_Deep_Q_Network/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:15.489853 | """
This part of code is the DQN brain, which is a brain of the agent.
All decisions are made in here.
Using Tensorflow to build the neural network.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.7.3
"""
import numpy as np
import pandas as pd
import tensorflow as... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5_Deep_Q_Network/DQN_modified.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:15.795488 | """
This part of code is the Deep Q Network (DQN) brain.
view the tensorboard picture about this DQN structure on: https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/#modification
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: r1.2
"""
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/7_Policy_gradient_softmax/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:15.827557 | """
This part of code is the reinforcement learning brain, which is a brain of the agent.
All decisions are made in here.
Policy Gradient, Reinforcement Learning.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
import tensorflow as tf
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/8_Actor_Critic_Advantage/AC_CartPole.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:16.697938 | """
Actor-Critic using TD-error as the Advantage, Reinforcement Learning.
The cart pole example. Policy is oscillated.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
tensorflow 1.0
gym 0.8.0
"""
import numpy as np
import tensorflow as tf
import gym
np.random.seed(2)
tf.set_random_see... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/7_Policy_gradient_softmax/run_CartPole.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:16.698498 | """
Policy Gradient, Reinforcement Learning.
The cart pole example
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import gym
from RL_brain import PolicyGradient
import matplotlib.pyplot as plt
DISPLAY_REWARD_THRESHOLD = 400 # renders environment if tot... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/9_Deep_Deterministic_Policy_Gradient_DDPG/DDPG_update2.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:17.276234 | """
Note: This is a updated version from my previous code,
for the target network, I use moving average to soft replace target parameters instead using assign function.
By doing this, it has 20% speed up on my machine (CPU).
Deep Deterministic Policy Gradient (DDPG), Reinforcement Learning.
DDPG is Actor Critic based ... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/9_Deep_Deterministic_Policy_Gradient_DDPG/DDPG_update.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:17.277296 | """
Deep Deterministic Policy Gradient (DDPG), Reinforcement Learning.
DDPG is Actor Critic based algorithm.
Pendulum example.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
tensorflow 1.0
gym 0.8.0
"""
import tensorflow as tf
import numpy as np
import gym
import time
###############... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/7_Policy_gradient_softmax/run_MountainCar.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:17.506828 | """
Policy Gradient, Reinforcement Learning.
The cart pole example
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import gym
from RL_brain import PolicyGradient
import matplotlib.pyplot as plt
DISPLAY_REWARD_THRESHOLD = -2000 # renders environment if t... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/Curiosity_Model/Curiosity.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:17.857201 | """This is a simple implementation of [Large-Scale Study of Curiosity-Driven Learning](https://arxiv.org/abs/1808.04355)"""
import numpy as np
import tensorflow as tf
import gym
import matplotlib.pyplot as plt
class CuriosityNet:
def __init__(
self,
n_a,
n_s,
lr=0.... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/Curiosity_Model/Random_Network_Distillation.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:17.858381 | """This is a simple implementation of [Exploration by Random Network Distillation](https://arxiv.org/abs/1810.12894)"""
import numpy as np
import tensorflow as tf
import gym
import matplotlib.pyplot as plt
class CuriosityNet:
def __init__(
self,
n_a,
n_s,
lr=0.01,
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | experiments/2D_car/DDPG.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:18.147349 | """
Environment is a 2D car.
Car has 5 sensors to obtain distance information.
Car collision => reward = -1, otherwise => reward = 0.
You can train this RL by using LOAD = False, after training, this model will be store in the a local folder.
Using LOAD = True to reload the trained model for playing.
You can custom... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | experiments/2D_car/car_env.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:18.419924 | """
Environment for 2D car driving.
You can customize this script in a way you want.
View more on [莫烦Python] : https://morvanzhou.github.io/tutorials/
Requirement:
pyglet >= 1.2.4
numpy >= 1.12.1
"""
import numpy as np
import pyglet
pyglet.clock.set_fps_limit(10000)
class CarEnv(object):
n_sensor = 5
act... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | experiments/2D_car/collision.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:18.438979 | import numpy as np
def intersection():
p = np.array([0, 0])
r = np.array([1, 1])
q = np.array([0.1, 0.1])
s = np.array([.1, .1])
if np.cross(r, s) == 0 and np.cross((q-p), r) == 0: # collinear
# t0 = (q − p) · r / (r · r)
# t1 = (q + s − p) · r / (r · r) = t0 + s · r / (r · r)
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | experiments/Robot_arm/A3C.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:18.736113 | """
Environment is a Robot Arm. The arm tries to get to the blue point.
The environment will return a geographic (distance) information for the arm to learn.
The far away from blue point the less reward; touch blue r+=1; stop at blue for a while then get r=+10.
You can train this RL by using LOAD = False, after trai... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | experiments/Robot_arm/DDPG.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:18.972194 | """
Environment is a Robot Arm. The arm tries to get to the blue point.
The environment will return a geographic (distance) information for the arm to learn.
The far away from blue point the less reward; touch blue r+=1; stop at blue for a while then get r=+10.
You can train this RL by using LOAD = False, after trai... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | experiments/Robot_arm/DPPO.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:19.008245 | """
A simple version of OpenAI's Proximal Policy Optimization (PPO). [http://adsabs.harvard.edu/abs/2017arXiv170706347S]
Distributing workers in parallel to collect data, then stop worker's roll-out and train PPO on collected data.
Restart workers once PPO is updated.
The global PPO updating rule is adopted from Deep... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | experiments/Robot_arm/arm_env.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:19.357499 | """
Environment for Robot Arm.
You can customize this script in a way you want.
View more on [莫烦Python] : https://morvanzhou.github.io/tutorials/
Requirement:
pyglet >= 1.2.4
numpy >= 1.12.1
"""
import numpy as np
import pyglet
pyglet.clock.set_fps_limit(10000)
class ArmEnv(object):
action_bound = [-1, 1]
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5_Deep_Q_Network/maze_env.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:20.097647 | """
Reinforcement learning maze example.
Red rectangle: explorer.
Black rectangles: hells [reward = -1].
Yellow bin circle: paradise [reward = +1].
All other states: ground [reward = 0].
This script is the environment part of this example.
The RL is in RL_brain.py.
View more o... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/9_Deep_Deterministic_Policy_Gradient_DDPG/DDPG.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:21.601477 | """
Deep Deterministic Policy Gradient (DDPG), Reinforcement Learning.
DDPG is Actor Critic based algorithm.
Pendulum example.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
tensorflow 1.0
gym 0.8.0
"""
import tensorflow as tf
import numpy as np
import gym
import time
np.random.seed(... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/8_Actor_Critic_Advantage/AC_continue_Pendulum.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:21.601947 | """
Actor-Critic with continuous action using TD-error as the Advantage, Reinforcement Learning.
The Pendulum example (based on https://github.com/dennybritz/reinforcement-learning/blob/master/PolicyGradient/Continuous%20MountainCar%20Actor%20Critic%20Solution.ipynb)
Cannot converge!!! oscillate!!!
View more on my t... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/5_Deep_Q_Network/run_this.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:25.389890 | from maze_env import Maze
from RL_brain import DeepQNetwork
def run_maze():
step = 0
for episode in range(300):
# initial observation
observation = env.reset()
while True:
# fresh env
env.render()
# RL choose action based on observation
... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/6_OpenAI_gym/run_CartPole.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:25.391364 | """
Deep Q network,
Using:
Tensorflow: 1.0
gym: 0.7.3
"""
import gym
from RL_brain import DeepQNetwork
env = gym.make('CartPole-v0')
env = env.unwrapped
print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)
RL = DeepQNetwork(n_actions=env.action_sp... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/6_OpenAI_gym/run_MountainCar.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:25.392136 | """
Deep Q network,
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import gym
from RL_brain import DeepQNetwork
env = gym.make('MountainCar-v0')
env = env.unwrapped
print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)
RL = DeepQNetwork(n_actions=3, n_featu... |
MorvanZhou/Reinforcement-learning-with-tensorflow | https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow | null | null | null | null | 9,455 | null | null | mit | null | null | null | null | null | null | null | contents/6_OpenAI_gym/RL_brain.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:25.392796 | """
This part of code is the DQN brain, which is a brain of the agent.
All decisions are made in here.
Using Tensorflow to build the neural network.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
import pandas as pd
import tensorflow as... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/config/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.043292 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .defaults import _C as cfg
|
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | demo/webcam.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.054201 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import argparse
import cv2
from maskrcnn_benchmark.config import cfg
from predictor import COCODemo
import time
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection Webcam Demo")
parser.add_argument(
... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | demo/predictor.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.054804 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import cv2
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import DetectronCheck... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/config/paths_catalog.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.057028 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Centralized catalog of paths."""
import os
from copy import deepcopy
class DatasetCatalog(object):
DATA_DIR = "datasets"
DATASETS = {
"coco_2017_train": {
"img_dir": "coco/train2017",
"ann_file": "coco/a... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/data/build.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.059371 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
import copy
import logging
import torch.utils.data
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.imports import import_file
from maskrcnn_benchmark.utils.miscellaneous import save_labels
from... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/data/collate_batch.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.062374 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from maskrcnn_benchmark.structures.image_list import to_image_list
class BatchCollator(object):
"""
From a list of samples from the dataset,
returns the batched images and targets.
This should be passed to the DataLoader
"""
... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/config/defaults.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.063586 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
from yacs.config import CfgNode as CN
# -----------------------------------------------------------------------------
# Convention about Training / Test specific parameters
# ------------------------------------------------------------... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | docker/docker-jupyter/jupyter_notebook_config.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.074891 | import os
from IPython.lib import passwd
# c = c # pylint:disable=undefined-variable
c = get_config()
c.NotebookApp.ip = '0.0.0.0'
c.NotebookApp.port = int(os.getenv('PORT', 8888))
c.NotebookApp.open_browser = False
# sets a password if PASSWORD is set in the environment
if 'PASSWORD' in os.environ:
password = o... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/data/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.077938 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .build import make_data_loader
|
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/data/datasets/__init__.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.622613 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .coco import COCODataset
from .voc import PascalVOCDataset
from .concat_dataset import ConcatDataset
from .abstract import AbstractDataset
from .cityscapes import CityScapesDataset
__all__ = [
"COCODataset",
"ConcatDataset",
"Pas... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/data/datasets/evaluation/cityscapes/cityscapes_eval.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.694683 | import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from copy import deepcopy
import torch
import numpy as np
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.structures.bounding_box import BoxList
from mas... |
facebookresearch/maskrcnn-benchmark | https://github.com/facebookresearch/maskrcnn-benchmark | null | null | null | null | 9,378 | null | null | mit | null | null | null | null | null | null | null | maskrcnn_benchmark/data/datasets/coco.py | null | null | null | null | null | null | Python | 2026-05-04T02:05:28.705972 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torchvision
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
min_ke... |
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