code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
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from dm_control import suite
from dm_control.rl.control import flatten_observation
from dm_control.rl.environment import StepType
import numpy as np
import pygame
from rllab.core import Serializable
from rllab.envs import Env
from rllab.envs import Step
from rllab.envs.dm_control_viewer import DmControlViewer
from rll... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/dm_control_env.py | 0.773644 | 0.257768 | dm_control_env.py | pypi |
import logging
import os
import os.path as osp
import traceback
import gym
import gym.envs
import gym.spaces
import gym.wrappers
try:
from gym import logger as monitor_logger
monitor_logger.setLevel(logging.WARNING)
except Exception as e:
traceback.print_exc()
from rllab.core import Serializable
from rlla... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/gym_env.py | 0.698432 | 0.210523 | gym_env.py | pypi |
import numpy as np
from rllab.core import Serializable
from rllab.envs import Step
from rllab.envs.mujoco import MujocoEnv
from rllab.misc import autoargs
from rllab.misc import logger
from rllab.misc.overrides import overrides
class SimpleHumanoidEnv(MujocoEnv, Serializable):
FILE = 'simple_humanoid.xml'
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/mujoco/simple_humanoid_env.py | 0.678753 | 0.317611 | simple_humanoid_env.py | pypi |
import numpy as np
from rllab.core import Serializable
from rllab.envs import Step
from rllab.envs.mujoco import MujocoEnv
from rllab.misc import autoargs
from rllab.misc import logger
from rllab.misc.overrides import overrides
class SwimmerEnv(MujocoEnv, Serializable):
FILE = 'swimmer.xml'
ORI_IND = 2
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/mujoco/swimmer_env.py | 0.63273 | 0.211682 | swimmer_env.py | pypi |
import os
import os.path as osp
import tempfile
import warnings
from cached_property import cached_property
import mako.lookup
import mako.template
import mujoco_py
from mujoco_py import functions
from mujoco_py import load_model_from_path
from mujoco_py import MjSim
from mujoco_py import MjViewer
import numpy as np
i... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/mujoco/mujoco_env.py | 0.579995 | 0.296616 | mujoco_env.py | pypi |
import math
import numpy as np
from rllab.core import Serializable
from rllab.envs import Step
from rllab.envs.mujoco import MujocoEnv
from rllab.envs.mujoco.mujoco_env import q_inv
from rllab.envs.mujoco.mujoco_env import q_mult
from rllab.misc import logger
from rllab.misc.overrides import overrides
class AntEnv(... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/mujoco/ant_env.py | 0.621541 | 0.27881 | ant_env.py | pypi |
import numpy as np
from rllab.core import Serializable
from rllab.envs import Step
from rllab.envs.mujoco import MujocoEnv
from rllab.misc import autoargs
from rllab.misc import logger
from rllab.misc.overrides import overrides
# states: [
# 0: z-coord,
# 1: x-coord (forward distance),
# 2: forward pitch along y-axis... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/mujoco/hopper_env.py | 0.665628 | 0.361672 | hopper_env.py | pypi |
import math
import os.path as osp
import tempfile
import xml.etree.ElementTree as ET
import numpy as np
from rllab import spaces
from rllab.core import Serializable
from rllab.envs import Step
from rllab.envs.mujoco.maze.maze_env_utils import construct_maze
from rllab.envs.mujoco.maze.maze_env_utils import point_dist... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/mujoco/maze/maze_env.py | 0.456652 | 0.255811 | maze_env.py | pypi |
from Box2D import b2ContactListener
from Box2D import b2DrawExtended
from Box2D import b2Vec2
import pygame
from pygame import KEYDOWN
from pygame import KEYUP
from pygame import MOUSEBUTTONDOWN
from pygame import MOUSEMOTION
from pygame import QUIT
class PygameDraw(b2DrawExtended):
"""
This debug draw class ... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/box2d/box2d_viewer.py | 0.713132 | 0.415314 | box2d_viewer.py | pypi |
import numpy as np
import pygame
from rllab.core import Serializable
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.envs.box2d.parser import find_body
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
# Tornio, Matti, and Tapani Raiko. "Variational Bayesian approach for
# nonline... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/box2d/cartpole_swingup_env.py | 0.67104 | 0.33406 | cartpole_swingup_env.py | pypi |
import numpy as np
import pygame
from rllab.core import Serializable
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.envs.box2d.parser import find_body
from rllab.envs.box2d.parser.xml_box2d import _get_name
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
class CarParkingEnv(Box... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/box2d/car_parking_env.py | 0.822225 | 0.363477 | car_parking_env.py | pypi |
import os.path as osp
import mako.lookup
import mako.template
import numpy as np
from rllab import spaces
from rllab.envs import Env
from rllab.envs import Step
from rllab.envs.box2d.box2d_viewer import Box2DViewer
from rllab.envs.box2d.parser.xml_box2d import find_body
from rllab.envs.box2d.parser.xml_box2d import f... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/box2d/box2d_env.py | 0.659405 | 0.397997 | box2d_env.py | pypi |
import numpy as np
from rllab.core import Serializable
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.envs.box2d.parser import find_body
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
# http://mlg.eng.cam.ac.uk/pilco/
class DoublePendulumEnv(Box2DEnv, Serializable):
@autoa... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/box2d/double_pendulum_env.py | 0.562417 | 0.24049 | double_pendulum_env.py | pypi |
import xml.etree.ElementTree as ET
import Box2D
import numpy as np
from rllab.envs.box2d.parser.xml_attr_types import Angle
from rllab.envs.box2d.parser.xml_attr_types import Bool
from rllab.envs.box2d.parser.xml_attr_types import Choice
from rllab.envs.box2d.parser.xml_attr_types import Either
from rllab.envs.box2d.... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/box2d/parser/xml_box2d.py | 0.620047 | 0.288995 | xml_box2d.py | pypi |
import numpy as np
class Type(object):
def __eq__(self, other):
return self.__class__ == other.__class__
def from_str(self, s):
raise NotImplementedError
class Float(Type):
def from_str(self, s):
return float(s)
class Int(Type):
def from_str(self, s):
return int(s)... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/envs/box2d/parser/xml_attr_types.py | 0.681515 | 0.290156 | xml_attr_types.py | pypi |
from os.path import abspath
from os.path import dirname
import shutil
import google.protobuf.json_format as json_format
from jsonmerge import merge
import numpy as np
from tensorboard import summary as summary_lib
from tensorboard.backend.event_processing import plugin_event_multiplexer \
as event_multiplexer
from... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/misc/tensorboard_output.py | 0.519278 | 0.278931 | tensorboard_output.py | pypi |
import numpy as np
import pygame
import pygame.gfxdraw
class Colors(object):
black = (0, 0, 0)
white = (255, 255, 255)
blue = (0, 0, 255)
red = (255, 0, 0)
green = (0, 255, 0)
class Viewer2D(object):
def __init__(self, size=(640, 480), xlim=None, ylim=None):
pygame.init()
scr... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/misc/viewer2d.py | 0.649245 | 0.25406 | viewer2d.py | pypi |
import numpy as np
from rllab.misc import sliced_fun
EPS = np.finfo('float64').tiny
def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10):
"""
Demmel p 312
"""
p = b.copy()
r = b.copy()
x = np.zeros_like(b)
rdotr = r.dot(r)
fmtstr = "%10i %10.3g %10.3g"
... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/misc/krylov.py | 0.568416 | 0.316475 | krylov.py | pypi |
import numpy as np
import numpy.random as nr
from rllab.core import Serializable
from rllab.exploration_strategies import ExplorationStrategy
from rllab.misc import AttrDict
from rllab.misc.overrides import overrides
from rllab.spaces import Box
class OUStrategy(ExplorationStrategy, Serializable):
"""
This s... | /rlgarage-0.1.0.tar.gz/rlgarage-0.1.0/rllab/exploration_strategies/ou_strategy.py | 0.822474 | 0.586641 | ou_strategy.py | pypi |
[](https://badge.fury.io/py/rlgraph)
[](https://www.python.org/downloads/release/python-356/)
[](https://github.com/rlgraph/rlgraph/... | /rlgraph-0.5.5.tar.gz/rlgraph-0.5.5/README.md | 0.667581 | 0.99264 | README.md | pypi |
from typing import Any, List, Dict, Tuple, Generic, Optional
from .config import ActionParser, DoneCondition, ObsBuilder, RewardFunction, StateMutator, Renderer, TransitionEngine
from .typing import AgentID, ObsType, ActionType, EngineActionType, RewardType, StateType, SpaceType
class RLGym(Generic[AgentID, ObsType,... | /rlgym-api-2.0.0a1.tar.gz/rlgym-api-2.0.0a1/rlgym/api/rlgym.py | 0.669853 | 0.454593 | rlgym.py | pypi |
from abc import abstractmethod
from typing import Any, Dict, List, Generic
from ..typing import AgentID, ObsType, StateType, SpaceType
class ObsBuilder(Generic[AgentID, ObsType, StateType, SpaceType]):
@abstractmethod
def get_obs_space(self, agent: AgentID) -> SpaceType:
"""
Function that ret... | /rlgym-api-2.0.0a1.tar.gz/rlgym-api-2.0.0a1/rlgym/api/config/obs_builder.py | 0.913334 | 0.590514 | obs_builder.py | pypi |
from abc import abstractmethod
from typing import Any, Dict, Generic
from ..typing import AgentID, ActionType, EngineActionType, StateType, SpaceType
class ActionParser(Generic[AgentID, ActionType, EngineActionType, StateType, SpaceType]):
@abstractmethod
def get_action_space(self, agent: AgentID) -> SpaceTy... | /rlgym-api-2.0.0a1.tar.gz/rlgym-api-2.0.0a1/rlgym/api/config/action_parser.py | 0.753829 | 0.57946 | action_parser.py | pypi |
import numpy as np
from typing import List
from rlbot.utils.structures.game_data_struct import GameTickPacket, FieldInfoPacket, PlayerInfo
from .physics_object import PhysicsObject
from .player_data import PlayerData
class GameState:
def __init__(self, game_info: FieldInfoPacket):
self.blue_score = 0
... | /rlgym_compat-1.1.0.tar.gz/rlgym_compat-1.1.0/rlgym_compat/game_state.py | 0.637821 | 0.2328 | game_state.py | pypi |
import math
import numpy as np
from rlbot.utils.structures.game_data_struct import Physics, Vector3, Rotator
class PhysicsObject:
def __init__(self, position=None, euler_angles=None, linear_velocity=None, angular_velocity=None):
self.position: np.ndarray = position if position else np.zeros(3)
# ... | /rlgym_compat-1.1.0.tar.gz/rlgym_compat-1.1.0/rlgym_compat/physics_object.py | 0.824603 | 0.662547 | physics_object.py | pypi |
import numpy as np
def get_dist(x, y):
return np.subtract(x, y)
def vector_projection(vec, dest_vec, mag_squared=None):
if mag_squared is None:
norm = vecmag(dest_vec)
if norm == 0:
return dest_vec
mag_squared = norm * norm
if mag_squared == 0:
return dest_ve... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/math.py | 0.694095 | 0.630799 | math.py | pypi |
import numpy as np
from dataclasses import dataclass
from typing import TypeVar, Optional
from rlgym.rocket_league.engine.utils import create_default_init
from rlgym.rocket_league import math
T = TypeVar('T')
@dataclass(init=False)
class PhysicsObject:
INV_VEC = np.array([-1, -1, 1], dtype=np.float32)
INV_M... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/engine/physics_object.py | 0.802672 | 0.398202 | physics_object.py | pypi |
import numpy as np
from dataclasses import dataclass
from typing import Optional, Generic
from rlgym.api.typing import AgentID
from rlgym.rocket_league.common_values import DOUBLEJUMP_MAX_DELAY, FLIP_TORQUE_TIME
from rlgym.rocket_league.engine.physics_object import PhysicsObject
from rlgym.rocket_league.engine.utils ... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/engine/car.py | 0.504639 | 0.23524 | car.py | pypi |
import random
from typing import Dict, Any
import numpy as np
from rlgym.api.config.state_mutator import StateMutator
from rlgym.rocket_league.common_values import BLUE_TEAM, BALL_RADIUS
from rlgym.rocket_league.engine.game_state import GameState
class KickoffMutator(StateMutator[GameState]):
SPAWN_BLUE_POS = n... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/state_mutators/kickoff_mutator.py | 0.574753 | 0.291762 | kickoff_mutator.py | pypi |
from typing import Any
import RocketSim as rsim
import rlviser_py as rlviser
from rlgym.api.engine.renderer import Renderer
from rlgym.rocket_league.common_values import BOOST_LOCATIONS
from rlgym.rocket_league.engine.car import Car
from rlgym.rocket_league.engine.game_state import GameState
class RLViserRenderer(R... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/sim/rlviser_renderer.py | 0.613584 | 0.295455 | rlviser_renderer.py | pypi |
import numpy as np
from typing import Dict, Any, List
from rlgym.api.engine.transition_engine import TransitionEngine
from rlgym.api.typing import AgentID
from rlgym.rocket_league.engine.game_state import GameState
class GameEngine(TransitionEngine[AgentID, GameState, np.ndarray]):
"""
WIP Don't use yet
... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/game/game_engine.py | 0.81772 | 0.28318 | game_engine.py | pypi |
import math
from typing import List, Dict, Any
import numpy as np
from rlgym.api.config.obs_builder import ObsBuilder
from rlgym.api.typing import AgentID
from rlgym.rocket_league.common_values import ORANGE_TEAM
from rlgym.rocket_league.engine.car import Car
from rlgym.rocket_league.engine.game_state import GameStat... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/obs_builders/default_obs.py | 0.761671 | 0.403038 | default_obs.py | pypi |
from typing import Dict, Any
import numpy as np
from rlgym.api.config.action_parser import ActionParser
from rlgym.api.typing import AgentID
from rlgym.rocket_league.engine.game_state import GameState
class LookupTableAction(ActionParser[AgentID, np.ndarray, np.ndarray, GameState, int]):
"""
World-famous di... | /rlgym-rocket-league-2.0.0a2.tar.gz/rlgym-rocket-league-2.0.0a2/rlgym/rocket_league/action_parsers/lookup_table_action.py | 0.849472 | 0.404184 | lookup_table_action.py | pypi |
__version__ = '1.8.2'
release_notes = {
'1.8.2': """
- Fix no touch timer in GameCondition (Rolv)
- Update RLLib example (Aech)
""",
'1.8.1': """
- Refactor GameCondition (Rolv, Impossibum)
- Fix a small mistake in LookupAction (Rolv)
""",
'1.8.0': """
- Add l... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/version.py | 0.703244 | 0.377082 | version.py | pypi |
from typing import List
import numpy as np
from rlgym.utils.gamestates import GameState, PlayerData
from rlgym.utils.reward_functions import DefaultReward
class MultiModelReward(DefaultReward):
"""
Handles the distribution of rewards to specific models where each model uses a different reward function
""... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_rewards/multi_model_rewards.py | 0.92563 | 0.645371 | multi_model_rewards.py | pypi |
import numpy as np
from rlgym.utils import RewardFunction
from rlgym.utils.common_values import BLUE_TEAM
from rlgym.utils.gamestates import GameState, PlayerData
class DistributeRewards(RewardFunction):
"""
Inspired by OpenAI's Dota bot (OpenAI Five).
Modifies rewards using the formula (1-team_spirit) * ... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_rewards/distribute_rewards.py | 0.783947 | 0.360039 | distribute_rewards.py | pypi |
import math
from typing import Union
import numpy as np
from rlgym.utils import RewardFunction
from rlgym.utils.gamestates import PlayerData, GameState
from rlgym.utils.reward_functions.common_rewards import ConstantReward
class _DummyReward(RewardFunction):
def reset(self, initial_state: GameState): pass
... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_rewards/anneal_rewards.py | 0.898463 | 0.595728 | anneal_rewards.py | pypi |
from rlgym.utils import TerminalCondition
from rlgym.utils.gamestates import GameState
class GameCondition(TerminalCondition): # Mimics a Rocket League game
def __init__(self, tick_skip=8, seconds_left=300, seconds_per_goal_forfeit=None,
max_overtime_seconds=float("inf"), max_no_touch_seconds=fl... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_terminals/game_condition.py | 0.666062 | 0.275824 | game_condition.py | pypi |
import numpy as np
from rlgym.envs import Match
from rlgym.utils.action_parsers import DiscreteAction
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import CheckpointCallback
from stable_baselines3.common.vec_env import VecMonitor, VecNormalize, VecCheckNan
from stable_baselines3.ppo import M... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/examples/sb3_multi_example.py | 0.695131 | 0.385722 | sb3_multi_example.py | pypi |
import numpy as np
from rlgym.envs import Match
from rlgym.utils.action_parsers import DiscreteAction
from rlgym.utils.obs_builders import AdvancedObs
from rlgym.utils.reward_functions import DefaultReward
from rlgym.utils.reward_functions.common_rewards import VelocityPlayerToBallReward
from rlgym.utils.state_setters ... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/examples/sb3_multiple_models_example.py | 0.501709 | 0.395251 | sb3_multiple_models_example.py | pypi |
from rlgym.utils.state_setters import StateSetter
from rlgym.utils.state_setters import StateWrapper
from rlgym.utils.common_values import BALL_RADIUS, CEILING_Z, BLUE_TEAM, ORANGE_TEAM
import numpy as np
from numpy import random as rand
X_MAX = 7000
Y_MAX = 9000
Z_MAX_CAR = 1900
GOAL_HEIGHT = 642.775
PITCH_MAX = np.p... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_state_setters/hoops_setter.py | 0.789599 | 0.412648 | hoops_setter.py | pypi |
import random
from typing import List, Union
import numpy as np
from rlgym.utils.state_setters import StateSetter
from rlgym.utils.state_setters import StateWrapper
class ReplaySetter(StateSetter):
def __init__(self, ndarray_or_file: Union[str, np.ndarray]):
"""
ReplayBasedSetter constructor
... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_state_setters/replay_setter.py | 0.897382 | 0.623363 | replay_setter.py | pypi |
from rlgym.utils.state_setters import StateSetter
from rlgym.utils.state_setters import StateWrapper
from rlgym.utils.common_values import BALL_RADIUS, CEILING_Z, BLUE_TEAM
import numpy as np
from numpy import random as rand
X_MAX = 7000
Y_MAX = 9000
Z_MAX_CAR = 1900
PITCH_MAX = np.pi / 2
ROLL_MAX = np.pi
class Kick... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_state_setters/symmetric_setter.py | 0.767167 | 0.39004 | symmetric_setter.py | pypi |
import math
from copy import deepcopy
from random import getrandbits, shuffle
from typing import List
import numpy as np
from rlgym.utils.state_setters.state_setter import StateSetter
from rlgym.utils.state_setters.wrappers import CarWrapper
from rlgym.utils.state_setters.wrappers import StateWrapper
PI = math.pi
c... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_state_setters/augment_setter.py | 0.775605 | 0.264133 | augment_setter.py | pypi |
from rlgym.utils.gamestates import PlayerData, GameState, PhysicsObject
from rlgym.utils.obs_builders import ObsBuilder
from typing import Any, List
from rlgym.utils import common_values
import numpy as np
import math
class AdvancedObsPadder(ObsBuilder):
"""adds 0 padding to accommodate differing numbers of agent... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_obs/advanced_padder.py | 0.725551 | 0.288776 | advanced_padder.py | pypi |
import numpy as np
from typing import Any, List
from rlgym.utils import common_values
from rlgym.utils.gamestates import PlayerData, GameState, PhysicsObject
from rlgym.utils.obs_builders import ObsBuilder
class AdvancedStacker(ObsBuilder):
"""
Alternative observation to AdvancedObs. action_stacks past stack_... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_obs/advanced_stacker.py | 0.786008 | 0.415432 | advanced_stacker.py | pypi |
import numpy as np
from pettingzoo import AECEnv
from rlgym.gym import Gym
class PettingZooEnv(AECEnv):
"""
Wrapper for using the RLGym env with PettingZoo,
"""
def __init__(self, env: Gym):
"""
:param env: the environment to wrap.
"""
super().__init__()
self.... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/pettingzoo_utils/pettingzoo_env.py | 0.828488 | 0.506408 | pettingzoo_env.py | pypi |
from typing import Any
import gym
import numpy as np
from gym.spaces import Discrete
from rlgym.utils.action_parsers import ActionParser
from rlgym.utils.gamestates import GameState
class LookupAction(ActionParser):
def __init__(self, bins=None):
super().__init__()
if bins is None:
se... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/extra_action_parsers/lookup_act.py | 0.742048 | 0.435421 | lookup_act.py | pypi |
import time
from collections import deque
from typing import List
import gym
import numpy as np
import torch as th
from stable_baselines3 import PPO
from stable_baselines3.common import utils
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.type_aliases import GymEnv, MaybeCall... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/sb3_utils/sb3_multi_agent_tools.py | 0.684159 | 0.431105 | sb3_multi_agent_tools.py | pypi |
import json
import os
from typing import Tuple, Optional, List
import numpy as np
from rlgym.utils import RewardFunction
from rlgym.utils.gamestates import PlayerData, GameState
from rlgym.utils.reward_functions import CombinedReward
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.co... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/sb3_utils/sb3_log_reward.py | 0.837454 | 0.318442 | sb3_log_reward.py | pypi |
from typing import Optional, List, Union, Sequence, Type, Any
import gym
import numpy as np
from stable_baselines3.common.vec_env import VecEnv
from stable_baselines3.common.vec_env.base_vec_env import VecEnvIndices, VecEnvStepReturn, VecEnvObs
from rlgym.gym import Gym
class SB3SingleInstanceEnv(VecEnv):
"""
... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/sb3_utils/sb3_single_instance_env.py | 0.919769 | 0.51312 | sb3_single_instance_env.py | pypi |
import multiprocessing as mp
import os
import time
from typing import Optional, List, Union, Any, Callable, Sequence
import numpy as np
from stable_baselines3.common.vec_env import SubprocVecEnv, CloudpickleWrapper, VecEnv
from stable_baselines3.common.vec_env.base_vec_env import (
VecEnvObs,
VecEnvStepReturn,... | /rlgym_tools-1.8.2.tar.gz/rlgym_tools-1.8.2/rlgym_tools/sb3_utils/sb3_multiple_instance_env.py | 0.774541 | 0.34139 | sb3_multiple_instance_env.py | pypi |
from abc import ABC
from typing import List
from hive.agents.agent import Agent
from hive.envs.base import BaseEnv
from hive.runners.utils import Metrics
from hive.utils import schedule
from hive.utils.experiment import Experiment
from hive.utils.loggers import ScheduledLogger
class Runner(ABC):
"""Base Runner c... | /rlhive-1.0.1-py3-none-any.whl/hive/runners/base.py | 0.939325 | 0.445831 | base.py | pypi |
import os
from collections import deque
import numpy as np
import torch
import yaml
from hive.utils.utils import PACKAGE_ROOT
def load_config(
config=None,
preset_config=None,
agent_config=None,
env_config=None,
logger_config=None,
):
"""Used to load config for experiments. Agents, environme... | /rlhive-1.0.1-py3-none-any.whl/hive/runners/utils.py | 0.82925 | 0.192141 | utils.py | pypi |
import argparse
import copy
from hive import agents as agent_lib
from hive import envs
from hive.runners.base import Runner
from hive.runners.utils import TransitionInfo, load_config
from hive.utils import experiment, loggers, schedule, utils
from hive.utils.registry import get_parsed_args
class SingleAgentRunner(Ru... | /rlhive-1.0.1-py3-none-any.whl/hive/runners/single_agent_loop.py | 0.781997 | 0.403449 | single_agent_loop.py | pypi |
import argparse
import copy
from hive import agents as agent_lib
from hive import envs
from hive.runners.base import Runner
from hive.runners.utils import TransitionInfo, load_config
from hive.utils import experiment, loggers, schedule, utils
from hive.utils.registry import get_parsed_args
class MultiAgentRunner(Run... | /rlhive-1.0.1-py3-none-any.whl/hive/runners/multi_agent_loop.py | 0.811676 | 0.33546 | multi_agent_loop.py | pypi |
import logging
import os
import yaml
from hive.utils.utils import Chomp, create_folder
class Experiment(object):
"""Implementation of a simple experiment class."""
def __init__(self, name, dir_name, schedule):
"""Initializes an experiment object.
The experiment state is an exposed property... | /rlhive-1.0.1-py3-none-any.whl/hive/utils/experiment.py | 0.792825 | 0.238622 | experiment.py | pypi |
import abc
from hive.utils.registry import Registrable, registry
class Schedule(abc.ABC, Registrable):
@abc.abstractmethod
def get_value(self):
"""Returns the current value of the variable we are tracking"""
pass
@abc.abstractmethod
def update(self):
"""Update the value of th... | /rlhive-1.0.1-py3-none-any.whl/hive/utils/schedule.py | 0.913288 | 0.711346 | schedule.py | pypi |
import abc
import copy
import os
from typing import List
import torch
import wandb
from hive.utils.registry import Registrable, registry
from hive.utils.schedule import ConstantSchedule, Schedule, get_schedule
from hive.utils.utils import Chomp, create_folder
class Logger(abc.ABC, Registrable):
"""Abstract clas... | /rlhive-1.0.1-py3-none-any.whl/hive/utils/loggers.py | 0.884894 | 0.458409 | loggers.py | pypi |
import argparse
import inspect
from copy import deepcopy
from functools import partial, update_wrapper
from typing import List, Mapping, Sequence, _GenericAlias
import yaml
class Registrable:
"""Class used to denote which types of objects can be registered in the RLHive
Registry. These objects can also be co... | /rlhive-1.0.1-py3-none-any.whl/hive/utils/registry.py | 0.911975 | 0.467575 | registry.py | pypi |
import argparse
import json
import logging
import os
import pickle
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import cm
logging.basicConfig()
def find_single_run_data(run_folder):
"""Looks for a chomp logger data file in `run_folder... | /rlhive-1.0.1-py3-none-any.whl/hive/utils/visualization.py | 0.676834 | 0.479747 | visualization.py | pypi |
import os
import pickle
import random
import numpy as np
import torch
from hive.utils.registry import CallableType
PACKAGE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def create_folder(folder):
"""Creates a folder.
Args:
folder (str): Folder to create.
"""
if not os.... | /rlhive-1.0.1-py3-none-any.whl/hive/utils/utils.py | 0.754282 | 0.258025 | utils.py | pypi |
import numpy as np
import torch
from torch import optim
from hive.utils.registry import registry
from hive.utils.utils import LossFn, OptimizerFn
def numpify(t):
"""Convert object to a numpy array.
Args:
t (np.ndarray | torch.Tensor | obj): Converts object to :py:class:`np.ndarray`.
"""
if i... | /rlhive-1.0.1-py3-none-any.whl/hive/utils/torch_utils.py | 0.939796 | 0.598635 | torch_utils.py | pypi |
import os
from typing import Dict, Tuple
import numpy as np
from hive.replays.circular_replay import CircularReplayBuffer
from hive.utils.torch_utils import numpify
class PrioritizedReplayBuffer(CircularReplayBuffer):
"""Implements a replay with prioritized sampling. See
https://arxiv.org/abs/1511.05952
... | /rlhive-1.0.1-py3-none-any.whl/hive/replays/prioritized_replay.py | 0.910394 | 0.588121 | prioritized_replay.py | pypi |
import os
import pickle
import numpy as np
from hive.replays.replay_buffer import BaseReplayBuffer
from hive.utils.utils import create_folder, seeder
class CircularReplayBuffer(BaseReplayBuffer):
"""An efficient version of a circular replay buffer that only stores each observation
once.
"""
def __i... | /rlhive-1.0.1-py3-none-any.whl/hive/replays/circular_replay.py | 0.867556 | 0.480783 | circular_replay.py | pypi |
import copy
from functools import partial
from typing import Tuple
import numpy as np
import torch
from hive.agents.dqn import DQNAgent
from hive.agents.qnets.base import FunctionApproximator
from hive.agents.qnets.noisy_linear import NoisyLinear
from hive.agents.qnets.qnet_heads import (
DistributionalNetwork,
... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/rainbow.py | 0.946794 | 0.411584 | rainbow.py | pypi |
import numpy as np
import torch
from hive.agents.rainbow import RainbowDQNAgent
class LegalMovesRainbowAgent(RainbowDQNAgent):
"""A Rainbow agent which supports games with legal actions."""
def create_q_networks(self, representation_net):
"""Creates the qnet and target qnet."""
super().creat... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/legal_moves_rainbow.py | 0.889924 | 0.403802 | legal_moves_rainbow.py | pypi |
import abc
from hive.utils.registry import Registrable
class Agent(abc.ABC, Registrable):
"""Base class for agents. Every implemented agent should be a subclass of
this class.
"""
def __init__(self, obs_dim, act_dim, id=0):
"""
Args:
obs_dim: Dimension of observations tha... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/agent.py | 0.889108 | 0.49585 | agent.py | pypi |
import copy
import os
import numpy as np
import torch
from hive.agents.agent import Agent
from hive.agents.qnets.base import FunctionApproximator
from hive.agents.qnets.qnet_heads import DQNNetwork
from hive.agents.qnets.utils import (
InitializationFn,
calculate_output_dim,
create_init_weights_fn,
)
from... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/dqn.py | 0.849691 | 0.449816 | dqn.py | pypi |
import torch
from torch import nn
from hive.agents.qnets.mlp import MLPNetwork
from hive.agents.qnets.utils import calculate_output_dim
class ConvNetwork(nn.Module):
"""
Basic convolutional neural network architecture. Applies a number of
convolutional layers (each followed by a ReLU activation), and the... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/qnets/conv.py | 0.955173 | 0.777638 | conv.py | pypi |
import torch
import torch.nn.functional as F
from torch import nn
class DQNNetwork(nn.Module):
"""Implements the standard DQN value computation. Transforms output from
:obj:`base_network` with output dimension :obj:`hidden_dim` to dimension
:obj:`out_dim`, which should be equal to the number of actions.
... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/qnets/qnet_heads.py | 0.962108 | 0.746208 | qnet_heads.py | pypi |
import math
import torch
import torch.nn.functional as F
from torch import nn
class NoisyLinear(nn.Module):
"""NoisyLinear Layer. Implements the layer described in
https://arxiv.org/abs/1706.10295."""
def __init__(self, in_dim: int, out_dim: int, std_init: float = 0.5):
"""
Args:
... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/qnets/noisy_linear.py | 0.93441 | 0.586168 | noisy_linear.py | pypi |
import math
import torch
from hive.utils.registry import registry
from hive.utils.utils import CallableType
def calculate_output_dim(net, input_shape):
"""Calculates the resulting output shape for a given input shape and network.
Args:
net (torch.nn.Module): The network which you want to calculate ... | /rlhive-1.0.1-py3-none-any.whl/hive/agents/qnets/utils.py | 0.941949 | 0.721804 | utils.py | pypi |
from abc import ABC, abstractmethod
from hive.utils.registry import Registrable
class BaseEnv(ABC, Registrable):
"""
Base class for environments.
"""
def __init__(self, env_spec, num_players):
"""
Args:
env_spec (EnvSpec): An object containing information about the
... | /rlhive-1.0.1-py3-none-any.whl/hive/envs/base.py | 0.924031 | 0.495911 | base.py | pypi |
import gym
from hive.envs.base import BaseEnv
from hive.envs.env_spec import EnvSpec
class GymEnv(BaseEnv):
"""
Class for loading gym environments.
"""
def __init__(self, env_name, num_players=1, **kwargs):
"""
Args:
env_name (str): Name of the environment (NOTE: make sur... | /rlhive-1.0.1-py3-none-any.whl/hive/envs/gym_env.py | 0.829803 | 0.364523 | gym_env.py | pypi |
import ale_py
import cv2
import numpy as np
from hive.envs.env_spec import EnvSpec
from hive.envs.gym_env import GymEnv
class AtariEnv(GymEnv):
"""
Class for loading Atari environments.
Adapted from the Dopamine's Atari preprocessing code:
https://github.com/google/dopamine/blob/6fbb58ad9bc1340f4289... | /rlhive-1.0.1-py3-none-any.whl/hive/envs/atari/atari.py | 0.911899 | 0.513546 | atari.py | pypi |
import gym
import numpy as np
from marlgrid.base import MultiGrid, MultiGridEnv, rotate_grid
from marlgrid.rendering import SimpleImageViewer
TILE_PIXELS = 32
class MultiGridEnvHive(MultiGridEnv):
def __init__(
self,
agents,
grid_size=None,
width=None,
height=None,
... | /rlhive-1.0.1-py3-none-any.whl/hive/envs/marlgrid/ma_envs/base.py | 0.624866 | 0.288243 | base.py | pypi |
import operator
from functools import reduce
import gym
import numpy as np
class FlattenWrapper(gym.core.ObservationWrapper):
"""
Flatten the observation to one dimensional vector.
"""
def __init__(self, env):
super().__init__(env)
if isinstance(env.observation_space, gym.spaces.Tup... | /rlhive-1.0.1-py3-none-any.whl/hive/envs/wrappers/gym_wrappers.py | 0.780955 | 0.46642 | gym_wrappers.py | pypi |
from importlib import import_module
import numpy as np
from hive.envs import BaseEnv
from hive.envs.env_spec import EnvSpec
class PettingZooEnv(BaseEnv):
"""
PettingZoo environment from https://github.com/PettingZoo-Team/PettingZoo
For now, we only support environments from PettingZoo with discrete act... | /rlhive-1.0.1-py3-none-any.whl/hive/envs/pettingzoo/pettingzoo.py | 0.754192 | 0.401864 | pettingzoo.py | pypi |
from importlib import import_module
import numpy as np
from hive.envs.base import BaseEnv
from hive.envs.env_spec import EnvSpec
class MinAtarEnv(BaseEnv):
"""
Class for loading MinAtar environments. See https://github.com/kenjyoung/MinAtar.
"""
def __init__(
self,
env_name,
... | /rlhive-1.0.1-py3-none-any.whl/hive/envs/minatar/minatar.py | 0.714528 | 0.311977 | minatar.py | pypi |
# [](https://colab.research.google.com/drive/1a0pSD-1tWhMmeJeeoyZM1A-HCW3yf1xR?usp=sharing) [](https://agarwl.github.io/rliable) [](https:... | /rliable-1.0.7.tar.gz/rliable-1.0.7/README.md | 0.925596 | 0.98631 | README.md | pypi |
import calendar as _calendar
import datetime as _datetime
import enum as _enum
import re as _re
__all__ = ['Period', 'Weekday', 'set_first_day_of_week', 'Time', 'Date', 'DateTime', 'timestamp']
class Period(_enum.Enum):
"""Enumeration of time periods."""
Day = 'd'
Week = 'w'
Month = 'm'
Year =... | /rlib-date-0.1.tar.gz/rlib-date-0.1/rdate/__init__.py | 0.914032 | 0.430506 | __init__.py | pypi |
# @Author: Olivier Watté <user>
# @Date: 2018-04-22T06:05:58-04:00
# @Email: owatte@ipeos.com
# @Last modified by: user
# @Last modified time: 2018-12-18T14:47:24-04:00
# @License: GPLv3
# @Copyright: IPEOS I-Solutions
import argparse
import configparser
import json
import requests
from requests.adapters import ... | /rlieh_satlight-0.0.4.tar.gz/rlieh_satlight-0.0.4/rlieh_satlight/core.py | 0.706393 | 0.218576 | core.py | pypi |
# Install
`pip install rlim`
# What purpose does `rlim` serve?
When working with various APIs, I have found that in some cases the rate limits imposed on the user can be somewhat complex. For example, a single endpoint may have a limit of 3 calls per second *and* 5000 calls per hour (in a few rare instances, I have se... | /rlim-0.0.3.tar.gz/rlim-0.0.3/README.md | 0.611382 | 0.994123 | README.md | pypi |
**WARNING: Rljax is currently in a beta version and being actively improved. Any contributions are welcome :)**
# Rljax
Rljax is a collection of RL algorithms written in JAX.
## Setup
You can install dependencies simply by executing the following. To use GPUs, CUDA (10.0, 10.1, 10.2 or 11.0) must be installed.
```bas... | /rljax-0.0.4.tar.gz/rljax-0.0.4/README.md | 0.738292 | 0.891811 | README.md | pypi |
import numpy as np
from scipy.stats import norm
from sklearn.preprocessing import OneHotEncoder
from gym import spaces
import gym
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)
class JITAI_env(gym.Env):
def __init__(self, sigma, ... | /rljitai-0.0.4.tar.gz/rljitai-0.0.4/rl_jitai_simulation/envs.py | 0.605799 | 0.379062 | envs.py | pypi |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class PolicyNetwork(nn.Module):
def __init__(self, lr, input_dims, n_actions, fc1_dim, fc2_dim):
super(PolicyNetwork, self).__init__()
self.fc2_dim = fc2_dim
self.fc1 = nn.Linear(*input_d... | /rljitai-0.0.4.tar.gz/rljitai-0.0.4/rl_jitai_simulation/agents.py | 0.929919 | 0.361728 | agents.py | pypi |
import torch
import numpy as np
import torch.nn.functional as F
from .dqn import Qnet
class DoubleDQN:
"""Double DQN 算法"""
def __init__(self,
state_dim,
hidden_dim,
action_dim,
learning_rate,
gamma,
epsilon,... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/dqn/dqn_improved.py | 0.839504 | 0.564038 | dqn_improved.py | pypi |
import torch
import torch.nn.functional as F
import numpy as np
class Qnet(torch.nn.Module):
''' 只有一层隐藏层的Q网络 '''
def __init__(self, state_dim, hidden_dim, action_dim):
super(Qnet, self).__init__()
self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
self.fc2 = torch.nn.Linear(hidden_dim,... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/dqn/dqn.py | 0.806738 | 0.604282 | dqn.py | pypi |
import numpy as np
class Sarsa:
def __init__(self, n_state, epsilon, alpha, gamma, n_action=4):
"""Sarsa算法
Arguments:
ncol -- 环境列数
nrow -- 环境行数
epsilon -- 随机选择动作的概率
alpha -- 学习率
gamma -- 折扣因子
Keyword Arguments:
n_act... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/base/sarsa.py | 0.449513 | 0.530176 | sarsa.py | pypi |
import numpy as np
import random
class QLearning:
"""Q-Learning算法"""
def __init__(self, n_state, epsilon, alpha, gamma, n_action=4):
self.Q_table = np.zeros((n_state, n_action))
self.n_action = n_action
self.epsilon = epsilon
self.alpha = alpha
self.gamma = gamma
... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/base/q_learning.py | 0.510985 | 0.491273 | q_learning.py | pypi |
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
class TwoLayerFC(nn.Module):
def __init__(self, num_in, num_out, hidden_dim, activation=F.relu, out_fn=lambda x: x) -> None:
super().__init__()
self.fc1 = nn.Linear(num_in, hidden_dim)
self.fc2 = nn.Linear... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/policy/ddpg.py | 0.882877 | 0.529385 | ddpg.py | pypi |
import torch
from torch import nn
import torch.nn.functional as F
from torch.distributions import Normal
import numpy as np
class PolicyNetContinuous(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim, action_bound):
super(PolicyNetContinuous, self).__init__()
self.fc1 = nn.Linear(st... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/policy/sac.py | 0.911098 | 0.560914 | sac.py | pypi |
import numpy as np
import random
class SumTree:
def __init__(self, capacity: int):
self.capacity = capacity # 叶子节点个数
self.data_pointer = 0
self.n_entries = 0
self.tree = np.zeros(2 * capacity - 1) # 树中总的节点个数
self.data = np.zeros(capacity, dtype=object)
def update(sel... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/utils/replay/per_replay_buffer.py | 0.605566 | 0.337913 | per_replay_buffer.py | pypi |
import torch
from torch import nn
import torch.nn.functional as F
class Discriminator(nn.Module):
"""判别器模型"""
def __init__(self, state_dim, hidden_dim, action_dim) -> None:
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, 1)
... | /rllife-1.0.3.tar.gz/rllife-1.0.3/life/imitation/gail.py | 0.897316 | 0.520679 | gail.py | pypi |
<div align=center>
<img src='./docs/assets/images/rllte-logo.png' style="width: 40%">
</div>
|<img src="https://img.shields.io/badge/License-MIT-%230677b8"> <img src="https://img.shields.io/badge/GPU-NVIDIA-%2377b900"> <img src="https://img.shields.io/badge/NPU-Ascend-%23c31d20"> <img src="https://img.shields.io/badge... | /rllte_core-0.0.1b1.tar.gz/rllte_core-0.0.1b1/README.md | 0.931103 | 0.957278 | README.md | pypi |
<div align=center>
<img src='/assets/images/structure.svg' style="width: 100%">
</div>
### <font color="#0053D6"><b>Common</b></font>: Auxiliary modules like trainer and logger.
- **Engine**: *Engine for building Hsuanwu application.*
- **Logger**: *Logger for managing output information.*
### <font color="#0053D6"><... | /rllte_core-0.0.1b1.tar.gz/rllte_core-0.0.1b1/docs/api_old.md | 0.838051 | 0.761804 | api_old.md | pypi |
#
### plot_interval_estimates
[source](https://github.com/RLE-Foundation/rllte/blob/main/rllte/evaluation/visualization.py/#L141)
```python
.plot_interval_estimates(
metrics_dict: Dict[str, Dict], metric_names: List[str], algorithms: List[str],
colors: Optional[List[str]] = None, color_palette: str = 'colorblin... | /rllte_core-0.0.1b1.tar.gz/rllte_core-0.0.1b1/docs/api_docs/evaluation/visualization.md | 0.939123 | 0.959913 | visualization.md | pypi |
# Model Evaluation
**rllte** provides evaluation methods based on:
> [Agarwal R, Schwarzer M, Castro P S, et al. Deep reinforcement learning at the edge of the statistical precipice[J]. Advances in neural information processing systems, 2021, 34: 29304-29320.](https://proceedings.neurips.cc/paper/2021/file/f514cec81cb... | /rllte_core-0.0.1b1.tar.gz/rllte_core-0.0.1b1/docs/tutorials/evaluation.md | 0.946708 | 0.979629 | evaluation.md | pypi |
# Decoupling Algorithms by Module Replacement
## Decoupling Algorithms
The actual performance of an RL algorithm is affected by various factors (e.g., different network architectures and experience usage
strategies), which are difficult to quantify.
> Huang S, Dossa R F J, Raffin A, et al. The 37 Implementation Deta... | /rllte_core-0.0.1b1.tar.gz/rllte_core-0.0.1b1/docs/tutorials/module_replacement.md | 0.790207 | 0.950365 | module_replacement.md | pypi |
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