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2,289,300 | test_envs.py | WindyLab_Gym-PPS/gym/envs/tests/test_envs.py | import pytest
import numpy as np
from gym import envs
from gym.envs.tests.spec_list import spec_list
from gym.spaces import Box
from gym.utils.env_checker import check_env
# This runs a smoketest on each official registered env. We may want
# to try also running environments which are not officially registered
# env... | 2,735 | Python | .py | 68 | 33.647059 | 88 | 0.656109 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,301 | test_envs_semantics.py | WindyLab_Gym-PPS/gym/envs/tests/test_envs_semantics.py | """
Currently disabled since this was done in a very poor way
Hashed str representation of objects
"""
import json
import hashlib
import os
import pytest
from gym import spaces, logger
from gym.envs.tests.spec_list import spec_list
DATA_DIR = os.path.dirname(__file__)
ROLLOUT_STEPS = 100
episodes = ROLLOUT_STEPS
st... | 3,577 | Python | .py | 98 | 28.479592 | 111 | 0.614182 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,302 | test_determinism.py | WindyLab_Gym-PPS/gym/envs/tests/test_determinism.py | import numpy as np
import pytest
from gym.envs.tests.spec_list import spec_list
@pytest.mark.parametrize("spec", spec_list)
def test_env(spec):
# Note that this precludes running this test in multiple
# threads. However, we probably already can't do multithreading
# due to some environments.
env1 = s... | 2,863 | Python | .py | 71 | 32.338028 | 87 | 0.6 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,303 | test_registration.py | WindyLab_Gym-PPS/gym/envs/tests/test_registration.py | # -*- coding: utf-8 -*-
import gym
from gym import error, envs
from gym.envs import registration
from gym.envs.classic_control import cartpole
class ArgumentEnv(gym.Env):
def __init__(self, arg1, arg2, arg3):
self.arg1 = arg1
self.arg2 = arg2
self.arg3 = arg3
gym.register(
id="test.A... | 2,244 | Python | .py | 71 | 26 | 86 | 0.657674 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,304 | test_mujoco_v2_to_v3_conversion.py | WindyLab_Gym-PPS/gym/envs/tests/test_mujoco_v2_to_v3_conversion.py | import unittest
import numpy as np
from gym import envs
from gym.envs.tests.spec_list import skip_mujoco, SKIP_MUJOCO_WARNING_MESSAGE
def verify_environments_match(
old_environment_id, new_environment_id, seed=1, num_actions=1000
):
old_environment = envs.make(old_environment_id)
new_environment = envs.ma... | 2,439 | Python | .py | 48 | 42.979167 | 86 | 0.665684 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,305 | spec_list.py | WindyLab_Gym-PPS/gym/envs/tests/spec_list.py | from gym import envs, logger
import os
SKIP_MUJOCO_WARNING_MESSAGE = (
"Cannot run mujoco test (either license key not found or mujoco not"
"installed properly)."
)
skip_mujoco = not (os.environ.get("MUJOCO_KEY"))
if not skip_mujoco:
try:
import mujoco_py
except ImportError:
skip_muj... | 1,431 | Python | .py | 48 | 23.375 | 80 | 0.637555 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,306 | test_frozenlake_dfs.py | WindyLab_Gym-PPS/gym/envs/tests/test_frozenlake_dfs.py | import pytest
import numpy as np
from gym.envs.toy_text.frozen_lake import generate_random_map
# Test that FrozenLake map generation creates valid maps of various sizes.
def test_frozenlake_dfs_map_generation():
def frozenlake_dfs_path_exists(res):
frontier, discovered = [], set()
frontier.append... | 1,224 | Python | .py | 29 | 30.034483 | 80 | 0.52309 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,307 | test_kellycoinflip.py | WindyLab_Gym-PPS/gym/envs/tests/test_kellycoinflip.py | from gym.envs.toy_text.kellycoinflip import KellyCoinflipEnv
class TestKellyCoinflipEnv:
@staticmethod
def test_done_when_reaches_max_wealth():
# https://github.com/openai/gym/issues/1266
env = KellyCoinflipEnv()
env.seed(1)
env.reset()
done = False
while not d... | 500 | Python | .py | 13 | 30.384615 | 66 | 0.643892 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,308 | guessing_game.py | WindyLab_Gym-PPS/gym/envs/toy_text/guessing_game.py | import numpy as np
import gym
from gym import spaces
from gym.utils import seeding
class GuessingGame(gym.Env):
"""Number guessing game
The object of the game is to guess within 1% of the randomly chosen number
within 200 time steps
After each step the agent is provided with one of four possible ob... | 3,267 | Python | .py | 80 | 32.5125 | 92 | 0.647264 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,309 | cliffwalking.py | WindyLab_Gym-PPS/gym/envs/toy_text/cliffwalking.py | import numpy as np
import sys
from contextlib import closing
from io import StringIO
from gym.envs.toy_text import discrete
UP = 0
RIGHT = 1
DOWN = 2
LEFT = 3
class CliffWalkingEnv(discrete.DiscreteEnv):
"""
This is a simple implementation of the Gridworld Cliff
reinforcement learning task.
Adapted ... | 4,116 | Python | .py | 98 | 32.938776 | 94 | 0.591592 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,310 | frozen_lake.py | WindyLab_Gym-PPS/gym/envs/toy_text/frozen_lake.py | import sys
from contextlib import closing
import numpy as np
from io import StringIO
from gym import utils
from gym.envs.toy_text import discrete
LEFT = 0
DOWN = 1
RIGHT = 2
UP = 3
MAPS = {
"4x4": ["SFFF", "FHFH", "FFFH", "HFFG"],
"8x8": [
"SFFFFFFF",
"FFFFFFFF",
"FFFHFFFF",
... | 5,545 | Python | .py | 143 | 27.832168 | 88 | 0.512835 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,311 | hotter_colder.py | WindyLab_Gym-PPS/gym/envs/toy_text/hotter_colder.py | import numpy as np
import gym
from gym import spaces
from gym.utils import seeding
class HotterColder(gym.Env):
"""Hotter Colder
The goal of hotter colder is to guess closer to a randomly selected number
After each step the agent receives an observation of:
0 - No guess yet submitted (only after res... | 2,401 | Python | .py | 64 | 29.0625 | 85 | 0.61399 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,312 | nchain.py | WindyLab_Gym-PPS/gym/envs/toy_text/nchain.py | import gym
from gym import spaces
from gym.utils import seeding
class NChainEnv(gym.Env):
"""n-Chain environment
This game presents moves along a linear chain of states, with two actions:
0) forward, which moves along the chain but returns no reward
1) backward, which returns to the beginning and h... | 2,171 | Python | .py | 46 | 39.565217 | 79 | 0.660833 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,313 | __init__.py | WindyLab_Gym-PPS/gym/envs/toy_text/__init__.py | from gym.envs.toy_text.blackjack import BlackjackEnv
from gym.envs.toy_text.roulette import RouletteEnv
from gym.envs.toy_text.frozen_lake import FrozenLakeEnv
from gym.envs.toy_text.nchain import NChainEnv
from gym.envs.toy_text.hotter_colder import HotterColder
from gym.envs.toy_text.guessing_game import GuessingGame... | 556 | Python | .py | 10 | 54.6 | 71 | 0.8663 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,314 | discrete.py | WindyLab_Gym-PPS/gym/envs/toy_text/discrete.py | import numpy as np
from gym import Env, spaces
from gym.utils import seeding
def categorical_sample(prob_n, np_random):
"""
Sample from categorical distribution
Each row specifies class probabilities
"""
prob_n = np.asarray(prob_n)
csprob_n = np.cumsum(prob_n)
return (csprob_n > np_random... | 1,556 | Python | .py | 46 | 27.152174 | 75 | 0.610033 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,315 | roulette.py | WindyLab_Gym-PPS/gym/envs/toy_text/roulette.py | import gym
from gym import spaces
from gym.utils import seeding
class RouletteEnv(gym.Env):
"""Simple roulette environment
The roulette wheel has s spots. If the bet is 0 and a 0 comes up, you win a reward of s-2.
If any other number comes up you get a reward of -1.
For non-zero bets, if the parity ... | 1,397 | Python | .py | 34 | 33.323529 | 94 | 0.610495 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,316 | taxi.py | WindyLab_Gym-PPS/gym/envs/toy_text/taxi.py | import sys
from contextlib import closing
from io import StringIO
from gym import utils
from gym.envs.toy_text import discrete
import numpy as np
MAP = [
"+---------+",
"|R: | : :G|",
"| : | : : |",
"| : : : : |",
"| | : | : |",
"|Y| : |B: |",
"+---------+",
]
class TaxiEnv(discrete.Discr... | 7,673 | Python | .py | 176 | 30.414773 | 456 | 0.499933 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,317 | blackjack.py | WindyLab_Gym-PPS/gym/envs/toy_text/blackjack.py | import gym
from gym import spaces
from gym.utils import seeding
def cmp(a, b):
return float(a > b) - float(a < b)
# 1 = Ace, 2-10 = Number cards, Jack/Queen/King = 10
deck = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]
def draw_card(np_random):
return int(np_random.choice(deck))
def draw_hand(np_random):... | 3,978 | Python | .py | 86 | 39.023256 | 80 | 0.654663 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,318 | kellycoinflip.py | WindyLab_Gym-PPS/gym/envs/toy_text/kellycoinflip.py | from scipy.stats import genpareto, norm
import numpy as np
import gym
from gym import spaces
from gym.utils import seeding
def flip(edge, np_random):
return 1 if np_random.uniform() < edge else -1
class KellyCoinflipEnv(gym.Env):
"""The Kelly coinflip game is a simple gambling introduced by Haghani & Dewey... | 11,761 | Python | .py | 238 | 39.802521 | 97 | 0.639094 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,319 | cube_crash.py | WindyLab_Gym-PPS/gym/envs/unittest/cube_crash.py | import numpy as np
import gym
from gym import spaces
from gym.utils import seeding
# Unit test environment for CNNs and CNN+RNN algorithms.
# Looks like this (RGB observations):
#
# ---------------------------
# | |
# | |
# | |
# | ... | 5,318 | Python | .py | 148 | 27.709459 | 99 | 0.521283 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,320 | memorize_digits.py | WindyLab_Gym-PPS/gym/envs/unittest/memorize_digits.py | import numpy as np
import gym
from gym import spaces
from gym.utils import seeding
# Unit test environment for CNNs.
# Looks like this (RGB observations):
#
# ---------------------------
# | |
# | ****** |
# | ****** |
# | ** ** |
# |... | 4,827 | Python | .py | 129 | 29.868217 | 86 | 0.424055 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,321 | __init__.py | WindyLab_Gym-PPS/gym/envs/unittest/__init__.py | from gym.envs.unittest.cube_crash import CubeCrash
from gym.envs.unittest.cube_crash import CubeCrashSparse
from gym.envs.unittest.cube_crash import CubeCrashScreenBecomesBlack
from gym.envs.unittest.memorize_digits import MemorizeDigits
| 238 | Python | .py | 4 | 58.5 | 68 | 0.880342 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,322 | mountain_car.py | WindyLab_Gym-PPS/gym/envs/classic_control/mountain_car.py | """
http://incompleteideas.net/MountainCar/MountainCar1.cp
permalink: https://perma.cc/6Z2N-PFWC
"""
import math
import numpy as np
import gym
from gym import spaces
from gym.utils import seeding
class MountainCarEnv(gym.Env):
"""
Description:
The agent (a car) is started at the bottom of a valley. ... | 6,141 | Python | .py | 141 | 33.957447 | 88 | 0.592388 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,323 | acrobot.py | WindyLab_Gym-PPS/gym/envs/classic_control/acrobot.py | """classic Acrobot task"""
import numpy as np
from numpy import sin, cos, pi
from gym import core, spaces
from gym.utils import seeding
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = [
"Alborz Geramifard",
"Robert H. Klein",
"Christoph Dann",
"William Dabney",
"Jonath... | 10,723 | Python | .py | 270 | 31.718519 | 93 | 0.571154 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,324 | continuous_mountain_car.py | WindyLab_Gym-PPS/gym/envs/classic_control/continuous_mountain_car.py | # -*- coding: utf-8 -*-
"""
@author: Olivier Sigaud
A merge between two sources:
* Adaptation of the MountainCar Environment from the "FAReinforcement" library
of Jose Antonio Martin H. (version 1.0), adapted by 'Tom Schaul, tom@idsia.ch'
and then modified by Arnaud de Broissia
* the OpenAI/gym MountainCar environm... | 6,795 | Python | .py | 160 | 33.1 | 105 | 0.590999 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,325 | __init__.py | WindyLab_Gym-PPS/gym/envs/classic_control/__init__.py | from gym.envs.classic_control.cartpole import CartPoleEnv
from gym.envs.classic_control.mountain_car import MountainCarEnv
from gym.envs.classic_control.continuous_mountain_car import Continuous_MountainCarEnv
from gym.envs.classic_control.pendulum import PendulumEnv
from gym.envs.classic_control.acrobot import Acrobot... | 325 | Python | .py | 5 | 63.8 | 86 | 0.877743 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,326 | pendulum.py | WindyLab_Gym-PPS/gym/envs/classic_control/pendulum.py | import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
from os import path
class PendulumEnv(gym.Env):
metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 30}
def __init__(self, g=10.0):
self.max_speed = 8
self.max_torque = 2.0
se... | 3,139 | Python | .py | 77 | 31.61039 | 86 | 0.565417 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,327 | cartpole.py | WindyLab_Gym-PPS/gym/envs/classic_control/cartpole.py | """
Classic cart-pole system implemented by Rich Sutton et al.
Copied from http://incompleteideas.net/sutton/book/code/pole.c
permalink: https://perma.cc/C9ZM-652R
"""
import math
import gym
from gym import spaces, logger
from gym.utils import seeding
import numpy as np
class CartPoleEnv(gym.Env):
"""
Descri... | 8,356 | Python | .py | 197 | 32.213198 | 87 | 0.566486 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,328 | rendering.py | WindyLab_Gym-PPS/gym/envs/classic_control/rendering.py | """
2D rendering framework
"""
import os
import sys
if "Apple" in sys.version:
if "DYLD_FALLBACK_LIBRARY_PATH" in os.environ:
os.environ["DYLD_FALLBACK_LIBRARY_PATH"] += ":/usr/lib"
# (JDS 2016/04/15): avoid bug on Anaconda 2.3.0 / Yosemite
from gym import error
try:
import pyglet
except Impo... | 13,702 | Python | .py | 390 | 26.953846 | 115 | 0.587171 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,329 | lunar_lander.py | WindyLab_Gym-PPS/gym/envs/box2d/lunar_lander.py | """
Rocket trajectory optimization is a classic topic in Optimal Control.
According to Pontryagin's maximum principle it's optimal to fire engine full throttle or
turn it off. That's the reason this environment is OK to have discreet actions (engine on or off).
The landing pad is always at coordinates (0,0). The coor... | 18,642 | Python | .py | 459 | 29.535948 | 120 | 0.536231 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,330 | __init__.py | WindyLab_Gym-PPS/gym/envs/box2d/__init__.py | try:
import Box2D
from gym.envs.box2d.lunar_lander import LunarLander
from gym.envs.box2d.lunar_lander import LunarLanderContinuous
from gym.envs.box2d.bipedal_walker import BipedalWalker, BipedalWalkerHardcore
from gym.envs.box2d.car_racing import CarRacing
except ImportError:
Box2D = None
| 316 | Python | .py | 8 | 35.5 | 82 | 0.801948 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,331 | bipedal_walker.py | WindyLab_Gym-PPS/gym/envs/box2d/bipedal_walker.py | import sys
import math
import numpy as np
import Box2D
from Box2D.b2 import (
edgeShape,
circleShape,
fixtureDef,
polygonShape,
revoluteJointDef,
contactListener,
)
import gym
from gym import spaces
from gym.utils import colorize, seeding, EzPickle
# This is simple 4-joints walker robot envir... | 24,191 | Python | .py | 582 | 29.853952 | 141 | 0.520122 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,332 | car_racing.py | WindyLab_Gym-PPS/gym/envs/box2d/car_racing.py | """
Easiest continuous control task to learn from pixels, a top-down racing
environment.
Discrete control is reasonable in this environment as well, on/off
discretization is fine.
State consists of STATE_W x STATE_H pixels.
The reward is -0.1 every frame and +1000/N for every track tile visited, where
N is the total ... | 21,173 | Python | .py | 570 | 25.468421 | 88 | 0.493494 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,333 | test_lunar_lander.py | WindyLab_Gym-PPS/gym/envs/box2d/test_lunar_lander.py | import pytest
try:
import Box2D
from .lunar_lander import LunarLander, LunarLanderContinuous, demo_heuristic_lander
except ImportError:
Box2D = None
@pytest.mark.skipif(Box2D is None, reason="Box2D not installed")
def test_lunar_lander():
_test_lander(LunarLander(), seed=0)
@pytest.mark.skipif(Box2... | 664 | Python | .py | 16 | 38.3125 | 87 | 0.764431 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,334 | car_dynamics.py | WindyLab_Gym-PPS/gym/envs/box2d/car_dynamics.py | """
Top-down car dynamics simulation.
Some ideas are taken from this great tutorial http://www.iforce2d.net/b2dtut/top-down-car by Chris Campbell.
This simulation is a bit more detailed, with wheels rotation.
Created by Oleg Klimov. Licensed on the same terms as the rest of OpenAI Gym.
"""
import numpy as np
import ... | 10,672 | Python | .py | 284 | 24.447183 | 113 | 0.467227 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,335 | sync_vector_env.py | WindyLab_Gym-PPS/gym/vector/sync_vector_env.py | import numpy as np
from copy import deepcopy
from gym import logger
from gym.vector.vector_env import VectorEnv
from gym.vector.utils import concatenate, create_empty_array
__all__ = ["SyncVectorEnv"]
class SyncVectorEnv(VectorEnv):
"""Vectorized environment that serially runs multiple environments.
Parame... | 3,991 | Python | .py | 93 | 33.612903 | 86 | 0.62687 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,336 | vector_env.py | WindyLab_Gym-PPS/gym/vector/vector_env.py | import gym
from gym.spaces import Tuple
from gym.vector.utils.spaces import batch_space
__all__ = ["VectorEnv"]
class VectorEnv(gym.Env):
r"""Base class for vectorized environments.
Each observation returned from vectorized environment is a batch of observations
for each sub-environment. And :meth:`step... | 6,317 | Python | .py | 150 | 33.533333 | 98 | 0.633628 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,337 | async_vector_env.py | WindyLab_Gym-PPS/gym/vector/async_vector_env.py | import numpy as np
import multiprocessing as mp
import time
import sys
from enum import Enum
from copy import deepcopy
from gym import logger
from gym.vector.vector_env import VectorEnv
from gym.error import (
AlreadyPendingCallError,
NoAsyncCallError,
ClosedEnvironmentError,
CustomSpaceError,
)
from g... | 17,588 | Python | .py | 422 | 29.973934 | 88 | 0.565492 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,338 | __init__.py | WindyLab_Gym-PPS/gym/vector/__init__.py | try:
from collections.abc import Iterable
except ImportError:
Iterable = (tuple, list)
from gym.vector.async_vector_env import AsyncVectorEnv
from gym.vector.sync_vector_env import SyncVectorEnv
from gym.vector.vector_env import VectorEnv, VectorEnvWrapper
__all__ = ["AsyncVectorEnv", "SyncVectorEnv", "Vector... | 2,172 | Python | .py | 54 | 32.555556 | 86 | 0.637702 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,339 | spaces.py | WindyLab_Gym-PPS/gym/vector/utils/spaces.py | import numpy as np
from collections import OrderedDict
from gym.spaces import Space, Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict
_BaseGymSpaces = (Box, Discrete, MultiDiscrete, MultiBinary)
__all__ = ["_BaseGymSpaces", "batch_space"]
def batch_space(space, n=1):
"""Create a (batched) space, containin... | 2,849 | Python | .py | 69 | 34.492754 | 84 | 0.639855 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,340 | __init__.py | WindyLab_Gym-PPS/gym/vector/utils/__init__.py | from gym.vector.utils.misc import CloudpickleWrapper, clear_mpi_env_vars
from gym.vector.utils.numpy_utils import concatenate, create_empty_array
from gym.vector.utils.shared_memory import (
create_shared_memory,
read_from_shared_memory,
write_to_shared_memory,
)
from gym.vector.utils.spaces import _BaseGym... | 582 | Python | .py | 19 | 27.052632 | 72 | 0.731317 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,341 | numpy_utils.py | WindyLab_Gym-PPS/gym/vector/utils/numpy_utils.py | import numpy as np
from gym.spaces import Space, Tuple, Dict
from gym.vector.utils.spaces import _BaseGymSpaces
from collections import OrderedDict
__all__ = ["concatenate", "create_empty_array"]
def concatenate(items, out, space):
"""Concatenate multiple samples from space into a single object.
Parameters... | 4,632 | Python | .py | 114 | 33.578947 | 87 | 0.628177 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,342 | shared_memory.py | WindyLab_Gym-PPS/gym/vector/utils/shared_memory.py | import numpy as np
import multiprocessing as mp
from ctypes import c_bool
from collections import OrderedDict
from gym import logger
from gym.spaces import Tuple, Dict
from gym.error import CustomSpaceError
from gym.vector.utils.spaces import _BaseGymSpaces
__all__ = ["create_shared_memory", "read_from_shared_memory"... | 6,695 | Python | .py | 152 | 37.177632 | 87 | 0.670614 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,343 | misc.py | WindyLab_Gym-PPS/gym/vector/utils/misc.py | import contextlib
import os
__all__ = ["CloudpickleWrapper", "clear_mpi_env_vars"]
class CloudpickleWrapper(object):
def __init__(self, fn):
self.fn = fn
def __getstate__(self):
import cloudpickle
return cloudpickle.dumps(self.fn)
def __setstate__(self, ob):
import pick... | 1,170 | Python | .py | 33 | 28.787879 | 80 | 0.652753 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,344 | test_sync_vector_env.py | WindyLab_Gym-PPS/gym/vector/tests/test_sync_vector_env.py | import pytest
import numpy as np
from gym.spaces import Box, Tuple
from gym.vector.tests.utils import CustomSpace, make_env, make_custom_space_env
from gym.vector.sync_vector_env import SyncVectorEnv
def test_create_sync_vector_env():
env_fns = [make_env("CubeCrash-v0", i) for i in range(8)]
try:
en... | 3,193 | Python | .py | 78 | 34.910256 | 79 | 0.676252 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,345 | test_vector_env_wrapper.py | WindyLab_Gym-PPS/gym/vector/tests/test_vector_env_wrapper.py | import gym
from gym.vector import make
from gym.vector import VectorEnvWrapper
class DummyWrapper(VectorEnvWrapper):
def __init__(self, env):
self.env = env
self.counter = 0
def reset_async(self):
super().reset_async()
self.counter += 1
def test_vector_env_wrapper_inheritanc... | 461 | Python | .py | 15 | 25.666667 | 51 | 0.69161 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,346 | test_spaces.py | WindyLab_Gym-PPS/gym/vector/tests/test_spaces.py | import pytest
import numpy as np
from gym.spaces import Box, MultiDiscrete, Tuple, Dict
from gym.vector.tests.utils import spaces, custom_spaces, CustomSpace
from gym.vector.utils.spaces import _BaseGymSpaces, batch_space
expected_batch_spaces_4 = [
Box(low=-1.0, high=1.0, shape=(4,), dtype=np.float64),
Box(... | 3,308 | Python | .py | 97 | 25.556701 | 84 | 0.49079 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,347 | test_shared_memory.py | WindyLab_Gym-PPS/gym/vector/tests/test_shared_memory.py | import pytest
import numpy as np
import multiprocessing as mp
from multiprocessing.sharedctypes import SynchronizedArray
from multiprocessing import Array, Process
from collections import OrderedDict
from gym.spaces import Box, Tuple, Dict
from gym.error import CustomSpaceError
from gym.vector.utils.spaces import _Ba... | 5,661 | Python | .py | 140 | 33.057143 | 87 | 0.608783 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,348 | test_numpy_utils.py | WindyLab_Gym-PPS/gym/vector/tests/test_numpy_utils.py | import pytest
import numpy as np
from collections import OrderedDict
from gym.spaces import Tuple, Dict
from gym.vector.utils.spaces import _BaseGymSpaces
from gym.vector.tests.utils import spaces
from gym.vector.utils.numpy_utils import concatenate, create_empty_array
@pytest.mark.parametrize(
"space", spaces... | 5,070 | Python | .py | 114 | 35.219298 | 78 | 0.601989 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,349 | test_vector_env.py | WindyLab_Gym-PPS/gym/vector/tests/test_vector_env.py | import pytest
import numpy as np
from gym.spaces import Tuple
from gym.vector.tests.utils import CustomSpace, make_env
from gym.vector.async_vector_env import AsyncVectorEnv
from gym.vector.sync_vector_env import SyncVectorEnv
from gym.vector.vector_env import VectorEnv
@pytest.mark.parametrize("shared_memory", [Tr... | 2,019 | Python | .py | 41 | 42.02439 | 87 | 0.703006 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,350 | utils.py | WindyLab_Gym-PPS/gym/vector/tests/utils.py | import numpy as np
import gym
import time
from gym.spaces import Box, Discrete, MultiDiscrete, MultiBinary, Tuple, Dict
spaces = [
Box(low=np.array(-1.0), high=np.array(1.0), dtype=np.float64),
Box(low=np.array([0.0]), high=np.array([10.0]), dtype=np.float32),
Box(
low=np.array([-1.0, 0.0, 0.0]), ... | 3,115 | Python | .py | 97 | 24.917526 | 88 | 0.573004 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,351 | test_async_vector_env.py | WindyLab_Gym-PPS/gym/vector/tests/test_async_vector_env.py | import pytest
import numpy as np
from multiprocessing import TimeoutError
from gym.spaces import Box, Tuple
from gym.error import AlreadyPendingCallError, NoAsyncCallError, ClosedEnvironmentError
from gym.vector.tests.utils import (
CustomSpace,
make_env,
make_slow_env,
make_custom_space_env,
)
from g... | 8,268 | Python | .py | 193 | 35.694301 | 87 | 0.668409 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,352 | test_resize_observation.py | WindyLab_Gym-PPS/gym/wrappers/test_resize_observation.py | import pytest
import gym
from gym.wrappers import ResizeObservation
try:
import atari_py
except ImportError:
atari_py = None
@pytest.mark.skipif(
atari_py is None, reason="Only run this test when atari_py is installed"
)
@pytest.mark.parametrize(
"env_id", ["PongNoFrameskip-v0", "SpaceInvadersNoFram... | 839 | Python | .py | 25 | 29.48 | 76 | 0.686032 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,353 | test_flatten_observation.py | WindyLab_Gym-PPS/gym/wrappers/test_flatten_observation.py | import pytest
import numpy as np
import gym
from gym.wrappers import FlattenObservation
from gym import spaces
@pytest.mark.parametrize("env_id", ["Blackjack-v0", "KellyCoinflip-v0"])
def test_flatten_observation(env_id):
env = gym.make(env_id)
wrapped_env = FlattenObservation(env)
obs = env.reset()
... | 1,014 | Python | .py | 25 | 34.48 | 83 | 0.643585 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,354 | test_transform_observation.py | WindyLab_Gym-PPS/gym/wrappers/test_transform_observation.py | import pytest
import numpy as np
import gym
from gym.wrappers import TransformObservation
@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v0"])
def test_transform_observation(env_id):
affine_transform = lambda x: 3 * x + 2
env = gym.make(env_id)
wrapped_env = TransformObservation(
g... | 836 | Python | .py | 22 | 33.590909 | 75 | 0.710037 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,355 | rescale_action.py | WindyLab_Gym-PPS/gym/wrappers/rescale_action.py | import numpy as np
import gym
from gym import spaces
class RescaleAction(gym.ActionWrapper):
r"""Rescales the continuous action space of the environment to a range [min_action, max_action].
Example::
>>> RescaleAction(env, min_action, max_action).action_space == Box(min_action, max_action)
T... | 1,633 | Python | .py | 40 | 31.925 | 100 | 0.608833 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,356 | test_rescale_action.py | WindyLab_Gym-PPS/gym/wrappers/test_rescale_action.py | import pytest
import numpy as np
import gym
from gym.wrappers import RescaleAction
def test_rescale_action():
env = gym.make("CartPole-v1")
with pytest.raises(AssertionError):
env = RescaleAction(env, -1, 1)
del env
env = gym.make("Pendulum-v0")
wrapped_env = RescaleAction(gym.make("Pen... | 768 | Python | .py | 23 | 28.521739 | 64 | 0.673913 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,357 | test_pixel_observation.py | WindyLab_Gym-PPS/gym/wrappers/test_pixel_observation.py | """Tests for the pixel observation wrapper."""
import pytest
import numpy as np
import gym
from gym import spaces
from gym.wrappers.pixel_observation import PixelObservationWrapper, STATE_KEY
class FakeEnvironment(gym.Env):
def __init__(self):
self.action_space = spaces.Box(shape=(1,), low=-1, high=1, ... | 4,304 | Python | .py | 94 | 36.478723 | 85 | 0.644343 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,358 | customize_pps.py | WindyLab_Gym-PPS/gym/wrappers/customize_pps.py | import os
import json
from typing import Any
import gym
import argparse
class PredatorPreySwarmCustomizer(gym.Wrapper):
def __init__(self, env, args):
super(PredatorPreySwarmCustomizer, self).__init__(env)
if isinstance(args, argparse.Namespace):
args_ = vars(args).items()
eli... | 1,514 | Python | .py | 37 | 31.810811 | 135 | 0.615649 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,359 | test_clip_action.py | WindyLab_Gym-PPS/gym/wrappers/test_clip_action.py | import numpy as np
import gym
from gym.wrappers import ClipAction
def test_clip_action():
# mountaincar: action-based rewards
make_env = lambda: gym.make("MountainCarContinuous-v0")
env = make_env()
wrapped_env = ClipAction(make_env())
seed = 0
env.seed(seed)
wrapped_env.seed(seed)
... | 704 | Python | .py | 22 | 26 | 72 | 0.616864 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,360 | test_record_episode_statistics.py | WindyLab_Gym-PPS/gym/wrappers/test_record_episode_statistics.py | import pytest
import gym
from gym.wrappers import RecordEpisodeStatistics
@pytest.mark.parametrize("env_id", ["CartPole-v0", "Pendulum-v0"])
@pytest.mark.parametrize("deque_size", [2, 5])
def test_record_episode_statistics(env_id, deque_size):
env = gym.make(env_id)
env = RecordEpisodeStatistics(env, deque_s... | 1,430 | Python | .py | 32 | 36.75 | 81 | 0.623563 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,361 | flatten_observation.py | WindyLab_Gym-PPS/gym/wrappers/flatten_observation.py | import gym.spaces as spaces
from gym import ObservationWrapper
class FlattenObservation(ObservationWrapper):
r"""Observation wrapper that flattens the observation."""
def __init__(self, env):
super(FlattenObservation, self).__init__(env)
self.observation_space = spaces.flatten_space(env.obser... | 446 | Python | .py | 9 | 44.111111 | 76 | 0.755196 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,362 | test_atari_preprocessing.py | WindyLab_Gym-PPS/gym/wrappers/test_atari_preprocessing.py | import numpy as np
import gym
from gym.wrappers import AtariPreprocessing
import pytest
pytest.importorskip("atari_py")
@pytest.fixture(scope="module")
def env_fn():
return lambda: gym.make("PongNoFrameskip-v4")
def test_atari_preprocessing_grayscale(env_fn):
import cv2
env1 = env_fn()
env2 = Atar... | 3,027 | Python | .py | 82 | 28.95122 | 110 | 0.595092 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,363 | gray_scale_observation.py | WindyLab_Gym-PPS/gym/wrappers/gray_scale_observation.py | import numpy as np
from gym.spaces import Box
from gym import ObservationWrapper
class GrayScaleObservation(ObservationWrapper):
r"""Convert the image observation from RGB to gray scale."""
def __init__(self, env, keep_dim=False):
super(GrayScaleObservation, self).__init__(env)
self.keep_dim ... | 1,060 | Python | .py | 27 | 30 | 86 | 0.61501 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,364 | atari_preprocessing.py | WindyLab_Gym-PPS/gym/wrappers/atari_preprocessing.py | import numpy as np
import gym
from gym.spaces import Box
from gym.wrappers import TimeLimit
try:
import cv2
except ImportError:
cv2 = None
class AtariPreprocessing(gym.Wrapper):
r"""Atari 2600 preprocessings.
This class follows the guidelines in
Machado et al. (2018), "Revisiting the Arcade Lear... | 5,953 | Python | .py | 146 | 30.479452 | 115 | 0.590218 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,365 | pixel_observation.py | WindyLab_Gym-PPS/gym/wrappers/pixel_observation.py | import collections
from collections.abc import MutableMapping
import copy
import numpy as np
from gym import spaces
from gym import ObservationWrapper
STATE_KEY = "state"
class PixelObservationWrapper(ObservationWrapper):
"""Augment observations by pixel values."""
def __init__(
self, env, pixels_... | 4,658 | Python | .py | 100 | 35.27 | 85 | 0.621938 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,366 | clip_action.py | WindyLab_Gym-PPS/gym/wrappers/clip_action.py | import numpy as np
from gym import ActionWrapper
from gym.spaces import Box
class ClipAction(ActionWrapper):
r"""Clip the continuous action within the valid bound."""
def __init__(self, env):
assert isinstance(env.action_space, Box)
super(ClipAction, self).__init__(env)
def action(self, ... | 407 | Python | .py | 10 | 35.7 | 77 | 0.715013 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,367 | filter_observation.py | WindyLab_Gym-PPS/gym/wrappers/filter_observation.py | import copy
from gym import spaces
from gym import ObservationWrapper
class FilterObservation(ObservationWrapper):
"""Filter dictionary observations by their keys.
Args:
env: The environment to wrap.
filter_keys: List of keys to be included in the observations.
Raises:
ValueError... | 2,341 | Python | .py | 57 | 29.842105 | 85 | 0.604231 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,368 | transform_reward.py | WindyLab_Gym-PPS/gym/wrappers/transform_reward.py | from gym import RewardWrapper
class TransformReward(RewardWrapper):
r"""Transform the reward via an arbitrary function.
Example::
>>> import gym
>>> env = gym.make('CartPole-v1')
>>> env = TransformReward(env, lambda r: 0.01*r)
>>> env.reset()
>>> observation, reward,... | 704 | Python | .py | 21 | 26.047619 | 81 | 0.595556 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,369 | test_gray_scale_observation.py | WindyLab_Gym-PPS/gym/wrappers/test_gray_scale_observation.py | import pytest
import numpy as np
import gym
from gym.wrappers import GrayScaleObservation
from gym.wrappers import AtariPreprocessing
pytest.importorskip("atari_py")
pytest.importorskip("cv2")
@pytest.mark.parametrize(
"env_id", ["PongNoFrameskip-v0", "SpaceInvadersNoFrameskip-v0"]
)
@pytest.mark.parametrize("... | 1,270 | Python | .py | 30 | 37.833333 | 87 | 0.722177 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,370 | test_filter_observation.py | WindyLab_Gym-PPS/gym/wrappers/test_filter_observation.py | import pytest
import numpy as np
import gym
from gym import spaces
from gym.wrappers.filter_observation import FilterObservation
class FakeEnvironment(gym.Env):
def __init__(self, observation_keys=("state")):
self.observation_space = spaces.Dict(
{
name: spaces.Box(shape=(2,),... | 2,796 | Python | .py | 64 | 36.3125 | 87 | 0.660767 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,371 | __init__.py | WindyLab_Gym-PPS/gym/wrappers/__init__.py | from gym import error
from gym.wrappers.monitor import Monitor
from gym.wrappers.time_limit import TimeLimit
from gym.wrappers.filter_observation import FilterObservation
from gym.wrappers.atari_preprocessing import AtariPreprocessing
from gym.wrappers.time_aware_observation import TimeAwareObservation
from gym.wrapper... | 1,077 | Python | .py | 19 | 55.684211 | 78 | 0.889414 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,372 | monitor.py | WindyLab_Gym-PPS/gym/wrappers/monitor.py | import json
import os
import numpy as np
import gym
import warnings
from gym import Wrapper
from gym import error, version, logger
from gym.wrappers.monitoring import stats_recorder, video_recorder
from gym.utils import atomic_write, closer
from gym.utils.json_utils import json_encode_np
FILE_PREFIX = "openaigym"
MA... | 16,078 | Python | .py | 398 | 30.819095 | 302 | 0.611186 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,373 | record_episode_statistics.py | WindyLab_Gym-PPS/gym/wrappers/record_episode_statistics.py | import time
from collections import deque
import numpy as np
import gym
class RecordEpisodeStatistics(gym.Wrapper):
def __init__(self, env, deque_size=100):
super(RecordEpisodeStatistics, self).__init__(env)
self.num_envs = getattr(env, "num_envs", 1)
self.t0 = (
time.time()
... | 2,141 | Python | .py | 53 | 29.283019 | 88 | 0.571017 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,374 | record_video.py | WindyLab_Gym-PPS/gym/wrappers/record_video.py | import os
import gym
from typing import Callable
import warnings
from gym.wrappers.monitoring import video_recorder
def capped_cubic_video_schedule(episode_id):
if episode_id < 1000:
return int(round(episode_id ** (1.0 / 3))) ** 3 == episode_id
else:
return episode_id % 1000 == 0
class Reco... | 3,740 | Python | .py | 91 | 30.934066 | 173 | 0.601875 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,375 | transform_observation.py | WindyLab_Gym-PPS/gym/wrappers/transform_observation.py | from gym import ObservationWrapper
class TransformObservation(ObservationWrapper):
r"""Transform the observation via an arbitrary function.
Example::
>>> import gym
>>> env = gym.make('CartPole-v1')
>>> env = TransformObservation(env, lambda obs: obs + 0.1*np.random.randn(*obs.shape)... | 741 | Python | .py | 19 | 31.684211 | 94 | 0.638655 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,376 | test_frame_stack.py | WindyLab_Gym-PPS/gym/wrappers/test_frame_stack.py | import pytest
pytest.importorskip("atari_py")
import numpy as np
import gym
from gym.wrappers import FrameStack
try:
import lz4
except ImportError:
lz4 = None
@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v0", "Pong-v0"])
@pytest.mark.parametrize("num_stack", [2, 3, 4])
@pytest.mark.parametr... | 1,387 | Python | .py | 42 | 27.642857 | 77 | 0.632012 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,377 | test_record_video.py | WindyLab_Gym-PPS/gym/wrappers/test_record_video.py | import pytest
import os
import shutil
import gym
from gym.wrappers import RecordEpisodeStatistics, RecordVideo
def test_record_video_using_default_trigger():
env = gym.make("CartPole-v1")
env = gym.wrappers.RecordVideo(env, "videos")
env.reset()
for _ in range(199):
action = env.action_space.... | 2,115 | Python | .py | 59 | 29.050847 | 87 | 0.612903 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,378 | test_transform_reward.py | WindyLab_Gym-PPS/gym/wrappers/test_transform_reward.py | import pytest
import numpy as np
import gym
from gym.wrappers import TransformReward
@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v0"])
def test_transform_reward(env_id):
# use case #1: scale
scales = [0.1, 200]
for scale in scales:
env = gym.make(env_id)
wrapped_env = Tr... | 1,669 | Python | .py | 49 | 27.959184 | 87 | 0.618929 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,379 | time_aware_observation.py | WindyLab_Gym-PPS/gym/wrappers/time_aware_observation.py | import numpy as np
from gym.spaces import Box
from gym import ObservationWrapper
class TimeAwareObservation(ObservationWrapper):
r"""Augment the observation with current time step in the trajectory.
.. note::
Currently it only works with one-dimensional observation space. It doesn't
support p... | 1,053 | Python | .py | 24 | 37.041667 | 82 | 0.694417 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,380 | resize_observation.py | WindyLab_Gym-PPS/gym/wrappers/resize_observation.py | import numpy as np
from gym.spaces import Box
from gym import ObservationWrapper
class ResizeObservation(ObservationWrapper):
r"""Downsample the image observation to a square image."""
def __init__(self, env, shape):
super(ResizeObservation, self).__init__(env)
if isinstance(shape, int):
... | 884 | Python | .py | 21 | 34.285714 | 86 | 0.655374 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,381 | time_limit.py | WindyLab_Gym-PPS/gym/wrappers/time_limit.py | import gym
class TimeLimit(gym.Wrapper):
def __init__(self, env, max_episode_steps=None):
super(TimeLimit, self).__init__(env)
if max_episode_steps is None and self.env.spec is not None:
max_episode_steps = env.spec.max_episode_steps
if self.env.spec is not None:
se... | 988 | Python | .py | 23 | 34.130435 | 67 | 0.621228 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,382 | frame_stack.py | WindyLab_Gym-PPS/gym/wrappers/frame_stack.py | from collections import deque
import numpy as np
from gym.spaces import Box
from gym import ObservationWrapper
class LazyFrames(object):
r"""Ensures common frames are only stored once to optimize memory use.
To further reduce the memory use, it is optionally to turn on lz4 to
compress the observations.
... | 3,939 | Python | .py | 90 | 35.611111 | 91 | 0.641865 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,383 | njp.py | WindyLab_Gym-PPS/gym/wrappers/njp.py | import os
import json
from typing import Any
import gym
import argparse
import numpy as np
class Agent:
def __init__(self, adversary=False):
self.adversary = adversary
class NJP(gym.Wrapper):
def __init__(self, env, args):
super(NJP, self).__init__(env)
if isinstance(args, arg... | 4,189 | Python | .py | 99 | 31.979798 | 135 | 0.55704 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,384 | test_time_aware_observation.py | WindyLab_Gym-PPS/gym/wrappers/test_time_aware_observation.py | import pytest
import gym
from gym.wrappers import TimeAwareObservation
@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v0"])
def test_time_aware_observation(env_id):
env = gym.make(env_id)
wrapped_env = TimeAwareObservation(env)
assert wrapped_env.observation_space.shape[0] == env.observati... | 1,058 | Python | .py | 25 | 37.8 | 87 | 0.66439 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,385 | stats_recorder.py | WindyLab_Gym-PPS/gym/wrappers/monitoring/stats_recorder.py | import json
import os
import time
from gym import error
from gym.utils import atomic_write
from gym.utils.json_utils import json_encode_np
class StatsRecorder(object):
def __init__(self, directory, file_prefix, autoreset=False, env_id=None):
self.autoreset = autoreset
self.env_id = env_id
... | 3,924 | Python | .py | 102 | 27.578431 | 205 | 0.573796 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,386 | video_recorder.py | WindyLab_Gym-PPS/gym/wrappers/monitoring/video_recorder.py | import json
import os
import os.path
import subprocess
import tempfile
from io import StringIO
import distutils.spawn
import distutils.version
import numpy as np
from gym import error, logger
from gym.utils import closer
def touch(path):
open(path, "a").close()
video_recorder_closer = closer.Closer()
class ... | 16,129 | Python | .py | 399 | 28.130326 | 295 | 0.538103 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,387 | test_video_recorder.py | WindyLab_Gym-PPS/gym/wrappers/monitoring/tests/test_video_recorder.py | import json
import os
import shutil
import tempfile
import numpy as np
import gym
from gym.wrappers.monitoring.video_recorder import VideoRecorder, video_recorder_closer
class BrokenRecordableEnv(object):
metadata = {"render.modes": [None, "rgb_array"]}
def render(self, mode=None):
pass
class Unre... | 2,137 | Python | .py | 72 | 24.305556 | 87 | 0.67189 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,388 | helpers.py | WindyLab_Gym-PPS/gym/wrappers/monitoring/tests/helpers.py | import contextlib
import shutil
import tempfile
@contextlib.contextmanager
def tempdir():
temp = tempfile.mkdtemp()
yield temp
shutil.rmtree(temp)
| 161 | Python | .py | 8 | 17.375 | 29 | 0.794702 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,389 | multi_discrete.py | WindyLab_Gym-PPS/gym/spaces/multi_discrete.py | import numpy as np
from .space import Space
class MultiDiscrete(Space):
"""
- The multi-discrete action space consists of a series of discrete action spaces with different number of actions in each
- It is useful to represent game controllers or keyboards where each key can be represented as a discrete ac... | 2,222 | Python | .py | 41 | 46.731707 | 125 | 0.655268 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,390 | dict.py | WindyLab_Gym-PPS/gym/spaces/dict.py | from collections import OrderedDict
from .space import Space
class Dict(Space):
"""
A dictionary of simpler spaces.
Example usage:
self.observation_space = spaces.Dict({"position": spaces.Discrete(2), "velocity": spaces.Discrete(3)})
Example usage [nested]:
self.nested_observation_space = sp... | 3,713 | Python | .py | 96 | 28.75 | 106 | 0.550556 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,391 | multi_binary.py | WindyLab_Gym-PPS/gym/spaces/multi_binary.py | import numpy as np
from .space import Space
class MultiBinary(Space):
"""
An n-shape binary space.
The argument to MultiBinary defines n, which could be a number or a `list` of numbers.
Example Usage:
>> self.observation_space = spaces.MultiBinary(5)
>> self.observation_space.sample()
... | 1,485 | Python | .py | 39 | 30.102564 | 90 | 0.588235 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,392 | utils.py | WindyLab_Gym-PPS/gym/spaces/utils.py | from collections import OrderedDict
from functools import singledispatch, reduce
import numpy as np
import operator as op
from gym.spaces import Box
from gym.spaces import Discrete
from gym.spaces import MultiDiscrete
from gym.spaces import MultiBinary
from gym.spaces import Tuple
from gym.spaces import Dict
@single... | 6,478 | Python | .py | 162 | 35.345679 | 85 | 0.699232 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,393 | box.py | WindyLab_Gym-PPS/gym/spaces/box.py | import numpy as np
import warnings
from .space import Space
from gym import logger
class Box(Space):
"""
A (possibly unbounded) box in R^n. Specifically, a Box represents the
Cartesian product of n closed intervals. Each interval has the form of one
of [a, b], (-oo, b], [a, oo), or (-oo, oo).
Th... | 5,707 | Python | .py | 136 | 31.875 | 88 | 0.586387 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,394 | __init__.py | WindyLab_Gym-PPS/gym/spaces/__init__.py | from gym.spaces.space import Space
from gym.spaces.box import Box
from gym.spaces.discrete import Discrete
from gym.spaces.multi_discrete import MultiDiscrete
from gym.spaces.multi_binary import MultiBinary
from gym.spaces.tuple import Tuple
from gym.spaces.dict import Dict
from gym.spaces.utils import flatdim
from gy... | 620 | Python | .py | 24 | 22.916667 | 51 | 0.755892 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,395 | discrete.py | WindyLab_Gym-PPS/gym/spaces/discrete.py | import numpy as np
from .space import Space
class Discrete(Space):
r"""A discrete space in :math:`\{ 0, 1, \\dots, n-1 \}`.
Example::
>>> Discrete(2)
"""
def __init__(self, n):
assert n >= 0
self.n = n
super(Discrete, self).__init__((), np.int64)
def sample(sel... | 868 | Python | .py | 27 | 24.259259 | 72 | 0.53911 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,396 | space.py | WindyLab_Gym-PPS/gym/spaces/space.py | from gym.utils import seeding
class Space(object):
"""Defines the observation and action spaces, so you can write generic
code that applies to any Env. For example, you can choose a random
action.
WARNING - Custom observation & action spaces can inherit from the `Space`
class. However, most use-c... | 2,329 | Python | .py | 51 | 38.294118 | 81 | 0.669757 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,397 | tuple.py | WindyLab_Gym-PPS/gym/spaces/tuple.py | import numpy as np
from .space import Space
class Tuple(Space):
"""
A tuple (i.e., product) of simpler spaces
Example usage:
self.observation_space = spaces.Tuple((spaces.Discrete(2), spaces.Discrete(3)))
"""
def __init__(self, spaces):
self.spaces = spaces
for space in space... | 1,827 | Python | .py | 51 | 26.411765 | 83 | 0.562677 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,398 | test_spaces.py | WindyLab_Gym-PPS/gym/spaces/tests/test_spaces.py | import json # note: ujson fails this test due to float equality
from copy import copy
import numpy as np
import pytest
from gym.spaces import Tuple, Box, Discrete, MultiDiscrete, MultiBinary, Dict
@pytest.mark.parametrize(
"space",
[
Discrete(3),
Box(low=0.0, high=np.inf, shape=(2, 2)),
... | 5,923 | Python | .py | 173 | 26.473988 | 87 | 0.554993 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |
2,289,399 | test_utils.py | WindyLab_Gym-PPS/gym/spaces/tests/test_utils.py | from collections import OrderedDict
import numpy as np
import pytest
from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Tuple, utils
spaces = [
Discrete(3),
Box(low=0.0, high=np.inf, shape=(2, 2)),
Box(low=0.0, high=np.inf, shape=(2, 2), dtype=np.float16),
Tuple([Discrete(5), Di... | 9,138 | Python | .py | 217 | 36.004608 | 120 | 0.645608 | WindyLab/Gym-PPS | 8 | 2 | 1 | GPL-2.0 | 9/5/2024, 10:48:35 PM (Europe/Amsterdam) |