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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import os
import torch
from torch import nn
import torch.distributed as dist
import gym
import numpy as np
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd, RunningMeanStdObs
from rl_games.common import common_losses
from rl_games.common import datasets
from r... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/central_value.py | 0.718594 | 0.343507 | central_value.py | pypi |
import torch
import torch.nn as nn
import numpy as np
import rl_games.algos_torch.torch_ext as torch_ext
'''
updates moving statistics with momentum
'''
class MovingMeanStd(nn.Module):
def __init__(self, insize, momentum = 0.25, epsilon=1e-05, per_channel=False, norm_only=False):
super(MovingMeanStd, self)... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/moving_mean_std.py | 0.910698 | 0.385808 | moving_mean_std.py | pypi |
from rl_games.algos_torch import torch_ext
import torch
import torch.nn as nn
import numpy as np
'''
updates statistic from a full data
'''
class RunningMeanStd(nn.Module):
def __init__(self, insize, epsilon=1e-05, per_channel=False, norm_only=False):
super(RunningMeanStd, self).__init__()
print('Ru... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/running_mean_std.py | 0.863895 | 0.440529 | running_mean_std.py | pypi |
from rl_games.common.player import BasePlayer
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from rl_games.common.tr_helpers import unsqueeze_obs
import gym
import torch
from torch import nn
import numpy as np
def rescale_actions(low, high, action):
d ... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/players.py | 0.763924 | 0.334753 | players.py | pypi |
import gym
import numpy as np
from pettingzoo.classic import connect_four_v0
import yaml
from rl_games.torch_runner import Runner
import os
from collections import deque
class ConnectFourSelfPlay(gym.Env):
def __init__(self, name="connect_four_v0", **kwargs):
gym.Env.__init__(self)
self.name = na... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/envs/connect4_selfplay.py | 0.507812 | 0.179279 | connect4_selfplay.py | pypi |
from rl_games.common.ivecenv import IVecEnv
import gym
import torch
import numpy as np
class CuleEnv(IVecEnv):
def __init__(self, config_name, num_actors, **kwargs):
import torchcule
from torchcule.atari import Env as AtariEnv
self.batch_size = num_actors
env_name=kwargs.pop('env_... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/envs/cule.py | 0.690142 | 0.239816 | cule.py | pypi |
import gym
import numpy as np
from pettingzoo.sisl import multiwalker_v6
import yaml
from rl_games.torch_runner import Runner
import os
from collections import deque
import rl_games.envs.connect4_network
class MultiWalker(gym.Env):
def __init__(self, name="multiwalker", **kwargs):
gym.Env.__init__(self)
... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/envs/multiwalker.py | 0.460532 | 0.24608 | multiwalker.py | pypi |
from rl_games.common.ivecenv import IVecEnv
import gym
import numpy as np
class Envpool(IVecEnv):
def __init__(self, config_name, num_actors, **kwargs):
import envpool
self.batch_size = num_actors
env_name=kwargs.pop('env_name')
self.has_lives = kwargs.pop('has_lives', False)
... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/envs/envpool.py | 0.752831 | 0.185246 | envpool.py | pypi |
import torch
from torch import nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.action_size = 7
self.conv1 = nn.Conv2d(4, 128, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(128)
def forward(self, s):
... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/envs/connect4_network.py | 0.915847 | 0.421909 | connect4_network.py | pypi |
import time
import gym
import numpy as np
import torch
import copy
from rl_games.common import vecenv
from rl_games.common import env_configurations
from rl_games.algos_torch import model_builder
class BasePlayer(object):
def __init__(self, params):
self.config = config = params['config']
self.lo... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/player.py | 0.624408 | 0.170404 | player.py | pypi |
import torch
import rl_games.algos_torch.torch_ext as torch_ext
class DefaultDiagnostics(object):
def __init__(self):
pass
def send_info(self, writter):
pass
def epoch(self, agent, current_epoch):
pass
def mini_epoch(self, agent, miniepoch):
pass
def mini_batch(s... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/diagnostics.py | 0.634317 | 0.234407 | diagnostics.py | pypi |
import time
class IntervalSummaryWriter:
"""
Summary writer wrapper designed to reduce the size of tf.events files.
It will prevent the learner from writing the summaries more often than a specified interval, i.e. if the
current interval is 20 seconds and we wrote our last summary for a particular sum... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/interval_summary_writer.py | 0.730097 | 0.538194 | interval_summary_writer.py | pypi |
from rl_games.algos_torch import torch_ext
import torch
import numpy as np
class AlgoObserver:
def __init__(self):
pass
def before_init(self, base_name, config, experiment_name):
pass
def after_init(self, algo):
pass
def process_infos(self, infos, done_indices):
pass... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/algo_observer.py | 0.783988 | 0.283186 | algo_observer.py | pypi |
from torch import nn
import torch
import math
def critic_loss(value_preds_batch, values, curr_e_clip, return_batch, clip_value):
if clip_value:
value_pred_clipped = value_preds_batch + \
(values - value_preds_batch).clamp(-curr_e_clip, curr_e_clip)
value_losses = (values - return_b... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/common_losses.py | 0.801276 | 0.468608 | common_losses.py | pypi |
import numpy as np
from collections import defaultdict
class LinearValueProcessor:
def __init__(self, start_eps, end_eps, end_eps_frames):
self.start_eps = start_eps
self.end_eps = end_eps
self.end_eps_frames = end_eps_frames
def __call__(self, frame):
if frame >= self.end_... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/tr_helpers.py | 0.569733 | 0.236406 | tr_helpers.py | pypi |
import rl_games.envs.test
from rl_games.common import wrappers
from rl_games.common import tr_helpers
from rl_games.envs.brax import create_brax_env
from rl_games.envs.envpool import create_envpool
from rl_games.envs.cule import create_cule
import gym
from gym.wrappers import FlattenObservation, FilterObservation
impor... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/env_configurations.py | 0.512449 | 0.402451 | env_configurations.py | pypi |
import numpy as np
import random
import gym
import torch
from rl_games.common.segment_tree import SumSegmentTree, MinSegmentTree
import torch
from rl_games.algos_torch.torch_ext import numpy_to_torch_dtype_dict
class ReplayBuffer(object):
def __init__(self, size, ob_space):
"""Create Replay buffer.
... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/experience.py | 0.88666 | 0.529081 | experience.py | pypi |
import torch
import copy
from torch.utils.data import Dataset
class PPODataset(Dataset):
def __init__(self, batch_size, minibatch_size, is_discrete, is_rnn, device, seq_len):
self.is_rnn = is_rnn
self.seq_len = seq_len
self.batch_size = batch_size
self.minibatch_size = minibatch_siz... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/datasets.py | 0.635222 | 0.291996 | datasets.py | pypi |
import torch
from torch import nn
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def multiply_hidden(h, mask):
if isinstance(h, to... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/common/layers/recurrent.py | 0.879244 | 0.37691 | recurrent.py | pypi |
from pathlib import Path
from rl_inventory_api.item import Item
from rl_inventory_api.constants import Types, Rarities, Tradeable, Certifies, Colors
import csv
from dataclasses import astuple
class Inventory:
def __init__(self, items: list[Item]):
self.items = items
@staticmethod
def read(path=Pa... | /rl-inventory-api-0.0.2.tar.gz/rl-inventory-api-0.0.2/src/rl_inventory_api/inventory.py | 0.808559 | 0.231397 | inventory.py | pypi |
from rl_inventory_api.constants import Colors, Certifies, Series, Tradeable, Rarities, Types
from dataclasses import dataclass, field
@dataclass
class Item:
product_id: int = field(repr=False)
name: str
slot: str
paint: str
certification: str = field(repr=False)
certification_value: int = fiel... | /rl-inventory-api-0.0.2.tar.gz/rl-inventory-api-0.0.2/src/rl_inventory_api/item.py | 0.663342 | 0.31076 | item.py | pypi |
import numpy as np
from rl_learn.bandits import K, N # Default number of arms and states
class Bandit:
"""
A class used to represent a Bandit environment where there is no concept of
state. Bandits interact with agents of class BanditAgent. This class
serves as an interface that all subclasses of Bandit must imp... | /rl_learn-1.0.2.tar.gz/rl_learn-1.0.2/rl_learn/bandits/environments.py | 0.889078 | 0.93049 | environments.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | /rl_mle_distributions-0.1.tar.gz/rl_mle_distributions-0.1/rl_mle_distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
import functools
import random
from copy import deepcopy
from typing import Any, Dict, List, Optional, NamedTuple
from rlmusician.environment import CounterpointEnv
from rlmusician.utils import generate_copies, imap_in_parallel
class EnvWithActions(NamedTuple):
"""A tuple of `CounterpointEnv` and actions previou... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/agent/monte_carlo_beam_search.py | 0.888245 | 0.547585 | monte_carlo_beam_search.py | pypi |
from typing import List, NamedTuple
from sinethesizer.utils.music_theory import get_note_to_position_mapping
NOTE_TO_POSITION = get_note_to_position_mapping()
TONIC_TRIAD_DEGREES = (1, 3, 5)
class ScaleElement(NamedTuple):
"""A pitch from a diatonic scale."""
note: str
position_in_semitones: int
p... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/utils/music_theory.py | 0.936 | 0.379925 | music_theory.py | pypi |
import copy
import multiprocessing as mp
from typing import Any, Callable, Dict, Iterator, List, Optional
def convert_to_base(
number: int, base: int, min_length: Optional[int] = None
) -> List[int]:
"""
Convert number to its representation in a given system.
:param number:
positive integ... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/utils/misc.py | 0.89393 | 0.465448 | misc.py | pypi |
import os
import subprocess
import traceback
from pkg_resources import resource_filename
from typing import List
import pretty_midi
from sinethesizer.io import (
convert_events_to_timeline,
convert_tsv_to_events,
create_instruments_registry,
write_timeline_to_wav
)
from sinethesizer.utils.music_theory ... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/utils/io.py | 0.816589 | 0.249859 | io.py | pypi |
import datetime
import os
from typing import Any, Dict, List, NamedTuple
import numpy as np
from sinethesizer.utils.music_theory import get_note_to_position_mapping
from rlmusician.environment.rules import get_rules_registry
from rlmusician.utils import (
Scale,
ScaleElement,
check_consonance,
create_... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/environment/piece.py | 0.795301 | 0.238861 | piece.py | pypi |
from math import ceil
from typing import Callable, Dict, List
from rlmusician.utils.music_theory import ScaleElement, check_consonance
N_EIGHTHS_PER_MEASURE = 8
# Rhythm rules.
def check_validity_of_rhythmic_pattern(durations: List[int], **kwargs) -> bool:
"""
Check that current measure is properly divide... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/environment/rules.py | 0.942255 | 0.599016 | rules.py | pypi |
from collections import Counter
from typing import Any, Callable, Dict, Optional
import numpy as np
from scipy.stats import entropy
from rlmusician.environment.piece import Piece
from rlmusician.utils import rolling_aggregate
def evaluate_absence_of_looped_fragments(
piece: Piece, min_size: int = 4, max_siz... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/environment/evaluation.py | 0.940422 | 0.609873 | evaluation.py | pypi |
from typing import Any, Dict, List, Tuple
import gym
import numpy as np
from rlmusician.environment.piece import Piece
from rlmusician.environment.evaluation import evaluate
from rlmusician.utils import convert_to_base
class CounterpointEnv(gym.Env):
"""
An environment where counterpoint line is composed gi... | /rl-musician-0.4.6.tar.gz/rl-musician-0.4.6/rlmusician/environment/environment.py | 0.94156 | 0.552841 | environment.py | pypi |
# %% auto 0
__all__ = ['constant_velocity_generator', 'mfpt_rw', 'mfpt_informed_rw', 'rw_generator', 'exp_time_generator', 'Biexp',
'biexp_time_generator', 'constant_velocity_generator_2D', 'mfpt_rw_2D', 'mfpt_informed_rw_2D']
# %% ../nbs/lib_nbs/05_mfpt.ipynb 3
import numpy as np
from tqdm.notebook import... | /rl_opts-0.0.1.tar.gz/rl_opts-0.0.1/rl_opts/mfpt.py | 0.707809 | 0.471832 | mfpt.py | pypi |
# %% auto 0
__all__ = ['learning', 'walk_from_policy', 'agent_efficiency', 'average_search_efficiency']
# %% ../nbs/lib_nbs/02_learning_and_benchmark.ipynb 3
import numpy as np
import pathlib
from .rl_framework import TargetEnv, Forager
from .utils import get_encounters
# %% ../nbs/lib_nbs/02_learning_and_benchmark... | /rl_opts-0.0.1.tar.gz/rl_opts-0.0.1/rl_opts/learn_and_bench.py | 0.549641 | 0.357147 | learn_and_bench.py | pypi |
# %% auto 0
__all__ = ['pdf_multimode', 'pdf_powerlaw', 'pdf_discrete_sample', 'get_policy_from_dist']
# %% ../nbs/lib_nbs/03_analytics.ipynb 2
import numpy as np
# %% ../nbs/lib_nbs/03_analytics.ipynb 5
def pdf_multimode(L: int, # Either int or array for which pdf is calculated
lambdas: list, # Sc... | /rl_opts-0.0.1.tar.gz/rl_opts-0.0.1/rl_opts/analytics.py | 0.746046 | 0.811303 | analytics.py | pypi |
# %% auto 0
__all__ = ['PS_imitation']
# %% ../nbs/lib_nbs/04_imitation_learning.ipynb 2
import numpy as np
# %% ../nbs/lib_nbs/04_imitation_learning.ipynb 4
class PS_imitation():
def __init__(self,
num_states: int, # Number of states
eta: float, # Glow parameter of PS
... | /rl_opts-0.0.1.tar.gz/rl_opts-0.0.1/rl_opts/imitation.py | 0.633977 | 0.589953 | imitation.py | pypi |
# rl-plotter
  
[README](README.md) | [中文文档](README_zh.md)
This is a... | /rl_plotter-2.4.0.tar.gz/rl_plotter-2.4.0/README.md | 0.637031 | 0.899431 | README.md | pypi |
from operator import itemgetter
from typing import Dict, List
import numpy as np
from rl_replicas.experience import Experience
class ReplayBuffer:
"""
Replay buffer for off-policy algorithms
:param buffer_size: (int) The size of the replay buffer.
"""
def __init__(self, buffer_size: int = int(... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/replay_buffer.py | 0.944747 | 0.436862 | replay_buffer.py | pypi |
import random
from typing import Iterable, List
import numpy as np
import scipy.signal
import torch
from gym import Space
from torch import Tensor, nn
from rl_replicas.policies.policy import Policy
from rl_replicas.value_function import ValueFunction
def discounted_cumulative_sums(vector: np.ndarray, discount: floa... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/utils.py | 0.950537 | 0.825519 | utils.py | pypi |
from typing import List, Optional
import numpy as np
class Experience:
"""
Experience
N: The number of episodes.
L: The length of each episode (it may vary).
A^*: The shape of single action step.
O^*: The shape of single observation step.
:param observations: (Optional[List[List[np.ndar... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/experience.py | 0.957616 | 0.895477 | experience.py | pypi |
import logging
from typing import Callable, Iterable, List, Tuple
import numpy as np
import torch
from torch import Tensor
from torch.optim import Optimizer
from typing_extensions import TypedDict
logger = logging.getLogger(__name__)
State = TypedDict(
"State",
{
"max_constraint": float,
"n_c... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/optimizers/conjugate_gradient_optimizer.py | 0.942321 | 0.563378 | conjugate_gradient_optimizer.py | pypi |
import logging
from typing import List, Optional
import gym
import numpy as np
from rl_replicas.experience import Experience
from rl_replicas.policies import Policy
from rl_replicas.samplers import Sampler
logger = logging.getLogger(__name__)
class BatchSampler(Sampler):
"""
Batch sampler
:param env: ... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/samplers/batch_sampler.py | 0.921468 | 0.549338 | batch_sampler.py | pypi |
import logging
import os
import time
from typing import List
import gym
import numpy as np
import torch
from torch import Tensor
from torch.distributions import Distribution
from torch.nn import functional as F
from rl_replicas.experience import Experience
from rl_replicas.metrics_manager import MetricsManager
from r... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/algorithms/vpg.py | 0.891584 | 0.512693 | vpg.py | pypi |
import copy
import logging
import os
import time
from typing import List
import gym
import numpy as np
import torch
from torch import Tensor
from torch.distributions import Distribution
from torch.nn import functional as F
from rl_replicas.experience import Experience
from rl_replicas.metrics_manager import MetricsMa... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/algorithms/ppo.py | 0.915734 | 0.468547 | ppo.py | pypi |
import copy
import logging
import os
import time
from typing import Dict, List
import gym
import numpy as np
import torch
from torch import Tensor
from torch.nn import functional as F
from rl_replicas.evaluator import Evaluator
from rl_replicas.experience import Experience
from rl_replicas.metrics_manager import Metr... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/algorithms/ddpg.py | 0.887984 | 0.3534 | ddpg.py | pypi |
import copy
import logging
import os
import time
from typing import Callable, List
import gym
import numpy as np
import torch
from torch import Tensor
from torch.distributions import Distribution, kl
from torch.nn import functional as F
from rl_replicas.experience import Experience
from rl_replicas.metrics_manager im... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/algorithms/trpo.py | 0.899365 | 0.424531 | trpo.py | pypi |
import copy
import logging
import os
import time
from typing import Dict, List
import gym
import numpy as np
import torch
from torch import Tensor
from torch.nn import functional as F
from rl_replicas.evaluator import Evaluator
from rl_replicas.experience import Experience
from rl_replicas.metrics_manager import Metr... | /rl_replicas-0.0.6-py3-none-any.whl/rl_replicas/algorithms/td3.py | 0.883933 | 0.398582 | td3.py | pypi |
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import Model
from tensorflow.keras.initializers import Constant, VarianceScaling
from tensorflow.keras.layers import Dense, Lambda
from rl_toolkit.networks.layers import MultivariateGaussianNoise
uniform_initializer = VarianceScaling(d... | /rl_toolkit-4.1.1-py3-none-any.whl/rl_toolkit/networks/models/actor.py | 0.728748 | 0.545588 | actor.py | pypi |
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.initializers import VarianceScaling
from tensorflow.keras.layers import Activation, Add, Dense
uniform_initializer = VarianceScaling(distribution="uniform", mode="fan_in", scale=1.0)
class Critic(Model):
"""
Critic
=========... | /rl_toolkit-4.1.1-py3-none-any.whl/rl_toolkit/networks/models/critic.py | 0.78964 | 0.628635 | critic.py | pypi |
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import constraints, initializers, regularizers
from tensorflow.keras.layers import Layer
class MultivariateGaussianNoise(Layer):
"""
Multivariate Gaussian Noise for exploration
===========
Attributes:
units (in... | /rl_toolkit-4.1.1-py3-none-any.whl/rl_toolkit/networks/layers/noise.py | 0.954393 | 0.713856 | noise.py | pypi |
import os
import numpy as np
import reverb
import wandb
from tensorflow.keras.optimizers import Adam
from wandb.keras import WandbCallback
from rl_toolkit.networks.callbacks import AgentCallback
from rl_toolkit.networks.models import ActorCritic
from rl_toolkit.utils import make_reverb_dataset
from .process import P... | /rl_toolkit-4.1.1-py3-none-any.whl/rl_toolkit/core/learner.py | 0.779028 | 0.421314 | learner.py | pypi |
import numpy as np
import reverb
import tensorflow as tf
import wandb
from rl_toolkit.networks.models import Actor
from rl_toolkit.utils import VariableContainer
from .process import Process
class Agent(Process):
"""
Agent
=================
Attributes:
env_name (str): the name of environmen... | /rl_toolkit-4.1.1-py3-none-any.whl/rl_toolkit/core/agent.py | 0.736211 | 0.33846 | agent.py | pypi |
import numpy as np
import reverb
import tensorflow as tf
from rl_toolkit.networks.models import Actor
from rl_toolkit.utils import VariableContainer
from .process import Process
class Server(Process):
"""
Learner
=================
Attributes:
env_name (str): the name of environment
... | /rl_toolkit-4.1.1-py3-none-any.whl/rl_toolkit/core/server.py | 0.823186 | 0.283856 | server.py | pypi |
# WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
WarpDrive is a flexible, lightweight, and easy-to-use open-source reinforcement learning (RL)
framework that implements end-to-end multi-agent RL on a single or multiple GPUs (Graphics Processing Unit).
Using the extreme paralle... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/README.md | 0.796055 | 0.944177 | README.md | pypi |
# Salesforce Open Source Community Code of Conduct
## About the Code of Conduct
Equality is a core value at Salesforce. We believe a diverse and inclusive
community fosters innovation and creativity, and are committed to building a
culture where everyone feels included.
Salesforce open-source projects are committed ... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/CODE_OF_CONDUCT.md | 0.642208 | 0.832951 | CODE_OF_CONDUCT.md | pypi |
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import animation
from matplotlib.patches import Polygon
from mpl_toolkits.mplot3d import art3d
def generate_tag_env_rollout_animation(
trainer,
fps=50,
tagger_color="#C843C3",
runner_color="#245EB6",
runner_not_in_game_color="#6666... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/example_envs/tag_continuous/generate_rollout_animation.py | 0.698638 | 0.630912 | generate_rollout_animation.py | pypi |
import copy
import heapq
import numpy as np
from gym import spaces
from warp_drive.utils.constants import Constants
from warp_drive.utils.data_feed import DataFeed
from warp_drive.utils.gpu_environment_context import CUDAEnvironmentContext
_OBSERVATIONS = Constants.OBSERVATIONS
_ACTIONS = Constants.ACTIONS
_REWARDS... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/example_envs/tag_continuous/tag_continuous.py | 0.799168 | 0.39423 | tag_continuous.py | pypi |
import math
import numba.cuda as numba_driver
from numba import float32, int32, boolean
kTwoPi = 6.283185308
kEpsilon = 1.0e-10
# Device helper function to compute distances between two agents
@numba_driver.jit((float32[:, ::1],
float32[:, ::1],
int32,
int32... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/example_envs/tag_continuous/tag_continuous_step_numba.py | 0.635222 | 0.307225 | tag_continuous_step_numba.py | pypi |
import math
import numpy as np
import numba.cuda as numba_driver
from numba import float32, int32, boolean
try:
from warp_drive.numba_includes.env_config import *
except ImportError:
raise Exception("warp_drive.numba_includes.env_config is not available")
kIndexToActionArr = np.array([[0, 0], [1, 0], [-1, 0], ... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/example_envs/tag_gridworld/tag_gridworld_step_numba.py | 0.474875 | 0.35855 | tag_gridworld_step_numba.py | pypi |
Copyright (c) 2021, salesforce.com, inc. \
All rights reserved. \
SPDX-License-Identifier: BSD-3-Clause. \
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause.
# Introduction
In this tutorial, we will describe how to implement your own environment in CUDA C, an... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/tutorials/tutorial-4.a-create_custom_environments_pycuda.md | 0.524395 | 0.939637 | tutorial-4.a-create_custom_environments_pycuda.md | pypi |
Copyright (c) 2021, salesforce.com, inc.\
All rights reserved.\
SPDX-License-Identifier: BSD-3-Clause\
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
Get started quickly with end-to-end multi-agent RL using WarpDrive! This shows a basic example to create a... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/tutorials/simple-end-to-end-example.ipynb | 0.808446 | 0.776411 | simple-end-to-end-example.ipynb | pypi |
Copyright (c) 2021, salesforce.com, inc.\
All rights reserved.\
SPDX-License-Identifier: BSD-3-Clause\
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
Try this notebook on [Colab](http://colab.research.google.com/github/salesforce/warp-drive/blob/master/tut... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/tutorials/tutorial-5-training_with_warp_drive.ipynb | 0.891043 | 0.932913 | tutorial-5-training_with_warp_drive.ipynb | pypi |
Copyright (c) 2021, salesforce.com, inc. \
All rights reserved. \
SPDX-License-Identifier: BSD-3-Clause \
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
**Try this notebook on [Colab](http://colab.research.google.com/github/salesforce/warp-drive/blob/maste... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/tutorials/tutorial-3-warp_drive_reset_and_log.ipynb | 0.488527 | 0.838746 | tutorial-3-warp_drive_reset_and_log.ipynb | pypi |
Copyright (c) 2021, salesforce.com, inc.\
All rights reserved.\
SPDX-License-Identifier: BSD-3-Clause\
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
**Try this notebook on [Colab](http://colab.research.google.com/github/salesforce/warp-drive/blob/master/t... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/tutorials/tutorial-1.b-warp_drive_basics.ipynb | 0.768646 | 0.919715 | tutorial-1.b-warp_drive_basics.ipynb | pypi |
Copyright (c) 2021, salesforce.com, inc. \
All rights reserved. \
SPDX-License-Identifier: BSD-3-Clause \
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
**Try this notebook on [Colab](http://colab.research.google.com/github/salesforce/warp-drive/blob/maste... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/tutorials/tutorial-2.b-warp_drive_sampler.ipynb | 0.782579 | 0.935876 | tutorial-2.b-warp_drive_sampler.ipynb | pypi |
Copyright (c) 2021, salesforce.com, inc. \
All rights reserved. \
SPDX-License-Identifier: BSD-3-Clause \
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
**Try this notebook on [Colab](http://colab.research.google.com/github/salesforce/warp-drive/blob/maste... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/tutorials/tutorial-2.a-warp_drive_sampler.ipynb | 0.799833 | 0.871639 | tutorial-2.a-warp_drive_sampler.ipynb | pypi |
from numba import cuda as numba_driver
from numba import float32, int32, boolean, from_dtype
from numba.cuda.random import init_xoroshiro128p_states, xoroshiro128p_uniform_float32
import numpy as np
kEps = 1.0e-8
xoroshiro128p_type = from_dtype(np.dtype([("s0", np.uint64), ("s1", np.uint64)], align=True))
@numba_dri... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/numba_includes/core/random.py | 0.60054 | 0.453322 | random.py | pypi |
import logging
from typing import Optional
import numba.cuda as numba_driver
import numpy as np
import torch
from warp_drive.managers.data_manager import CUDADataManager
class NumbaDataManager(CUDADataManager):
""""""
"""
Example:
numba_data_manager = NumbaDataManager(
num_agents=10, nu... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/managers/numba_managers/numba_data_manager.py | 0.838746 | 0.323233 | numba_data_manager.py | pypi |
from typing import Optional
import numpy as np
from warp_drive.utils import autoinit_pycuda
import pycuda.driver as pycuda_driver
import torch
from warp_drive.managers.data_manager import CUDADataManager
class CudaTensorHolder(pycuda_driver.PointerHolderBase):
"""
A class that facilitates casting tensors ... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/managers/pycuda_managers/pycuda_data_manager.py | 0.884077 | 0.341939 | pycuda_data_manager.py | pypi |
import logging
from warp_drive.utils import autoinit_pycuda
from pycuda.driver import Context
class DeviceArchitectures:
"""
Reference:
"https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities"
"""
MaxBlocksPerSM = {
"sm_35": 16,
"sm_37": 16,
... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/utils/architecture_validate.py | 0.58439 | 0.277381 | architecture_validate.py | pypi |
import logging
class EnvironmentRegistrar:
"""
Environment Registrar Class
"""
_cpu_envs = {}
_cuda_envs = {}
_numba_envs = {}
_customized_cuda_env_src_paths = {
"pycuda": {},
"numba": {},
}
def add(self, env_backend="cpu", cuda_env_src_path=None):
if not ... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/utils/env_registrar.py | 0.42322 | 0.255853 | env_registrar.py | pypi |
class DataFeed(dict):
"""
Helper class to build up the data dict for CUDADataManager.push_data_to_device(data)
Example:
data = DataFeed()
data.add(name="X", data=[1,2,3], save_copy_and_apply_at_reset=True,
log_data_across_episode=True)
"""
def add_data(
self,... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/utils/data_feed.py | 0.815967 | 0.593344 | data_feed.py | pypi |
import logging
import os
import re
from warp_drive.utils.common import get_project_root
from warp_drive.utils.env_registrar import EnvironmentRegistrar
def get_default_env_directory(env_name):
envs = {
"DummyEnv": "example_envs.dummy_env.test_step_numba",
"TagGridWorld": "example_envs.tag_gridwo... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/utils/numba_utils/misc.py | 0.518546 | 0.259204 | misc.py | pypi |
import logging
import os
import re
from warp_drive.utils.common import get_project_root
from warp_drive.utils.env_registrar import EnvironmentRegistrar
def get_default_env_directory(env_name):
envs = {
"TagGridWorld": f"{get_project_root()}"
f"/example_envs/tag_gridworld/tag_gridworld_step_pycud... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/utils/pycuda_utils/misc.py | 0.515376 | 0.247214 | misc.py | pypi |
import argparse
import logging
import os
import sys
import time
import torch
import yaml
from example_envs.tag_continuous.tag_continuous import TagContinuous
from example_envs.tag_gridworld.tag_gridworld import CUDATagGridWorld, CUDATagGridWorldWithResetPool
from warp_drive.env_wrapper import EnvWrapper
from warp_dri... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/training/example_training_script_numba.py | 0.591015 | 0.215021 | example_training_script_numba.py | pypi |
import argparse
import logging
import os
import sys
import time
import torch
import yaml
from example_envs.tag_continuous.tag_continuous import TagContinuous
from example_envs.tag_gridworld.tag_gridworld import CUDATagGridWorld
from warp_drive.env_wrapper import EnvWrapper
from warp_drive.training.trainer import Trai... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/training/example_training_script_pycuda.py | 0.599602 | 0.233008 | example_training_script_pycuda.py | pypi |
import numpy as np
import torch
import torch.nn.functional as func
from gym.spaces import Box, Dict, Discrete, MultiDiscrete
from torch import nn
from warp_drive.utils.constants import Constants
from warp_drive.utils.data_feed import DataFeed
from warp_drive.training.utils.data_loader import get_flattened_obs_size
_O... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/training/models/fully_connected.py | 0.918918 | 0.575648 | fully_connected.py | pypi |
import logging
from torch.optim.lr_scheduler import LambdaLR
def _linear_interpolation(l_v, r_v, slope):
"""linear interpolation between l_v and r_v with a slope"""
return l_v + slope * (r_v - l_v)
class ParamScheduler:
"""
A generic scheduler for the adapting parameters such as
learning rate a... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/training/utils/param_scheduler.py | 0.9067 | 0.750324 | param_scheduler.py | pypi |
import logging
from warp_drive.training.utils.device_child_process.child_process_base import ProcessWrapper
def best_param_search(low=1, margin=1, func=None):
"""
Perform a binary search to determine the best parameter value.
In this specific context, the best
parameter is (the highest) value of the ... | /rl-warp-drive-2.5.0.tar.gz/rl-warp-drive-2.5.0/warp_drive/training/utils/vertical_scaler.py | 0.765769 | 0.750313 | vertical_scaler.py | pypi |
import argparse
import difflib
import importlib
import os
import time
import uuid
import gym as gym26
import gymnasium as gym
import numpy as np
import stable_baselines3 as sb3
import torch as th
from stable_baselines3.common.utils import set_random_seed
# Register custom envs
import rl_zoo3.import_envs # noqa: F401... | /rl_zoo3-2.1.0.tar.gz/rl_zoo3-2.1.0/rl_zoo3/train.py | 0.592784 | 0.167866 | train.py | pypi |
from typing import Any, Dict
import numpy as np
# Deprecation warning with gym 0.26 and numpy 1.24
np.bool8 = np.bool_ # type: ignore[attr-defined]
import gym # noqa: E402
import gymnasium # noqa: E402
class PatchedRegistry(dict):
"""
gym.envs.registration.registry
is now a dictionnary and no longer... | /rl_zoo3-2.1.0.tar.gz/rl_zoo3-2.1.0/rl_zoo3/gym_patches.py | 0.888704 | 0.439086 | gym_patches.py | pypi |
import os
import tempfile
import time
from copy import deepcopy
from functools import wraps
from threading import Thread
from typing import Optional, Type, Union
import optuna
from sb3_contrib import TQC
from stable_baselines3 import SAC
from stable_baselines3.common.callbacks import BaseCallback, EvalCallback
from st... | /rl_zoo3-2.1.0.tar.gz/rl_zoo3-2.1.0/rl_zoo3/callbacks.py | 0.771801 | 0.246567 | callbacks.py | pypi |
import argparse
import glob
import importlib
import os
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
import gym as gym26
import gymnasium as gym
import stable_baselines3 as sb3 # noqa: F401
import torch as th # noqa: F401
import yaml
from gymnasium import spaces... | /rl_zoo3-2.1.0.tar.gz/rl_zoo3-2.1.0/rl_zoo3/utils.py | 0.789842 | 0.295697 | utils.py | pypi |
import argparse
import os
import shutil
import zipfile
from pathlib import Path
from typing import Optional
from huggingface_sb3 import EnvironmentName, ModelName, ModelRepoId, load_from_hub
from requests.exceptions import HTTPError
from rl_zoo3 import ALGOS, get_latest_run_id
def download_from_hub(
algo: str,
... | /rl_zoo3-2.1.0.tar.gz/rl_zoo3-2.1.0/rl_zoo3/load_from_hub.py | 0.64713 | 0.212559 | load_from_hub.py | pypi |
import argparse
import os
import re
import shutil
import subprocess
from copy import deepcopy
from huggingface_sb3 import EnvironmentName
from rl_zoo3.utils import ALGOS, get_latest_run_id
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--env", help="environment ID", type=E... | /rl_zoo3-2.1.0.tar.gz/rl_zoo3-2.1.0/rl_zoo3/record_training.py | 0.46563 | 0.18168 | record_training.py | pypi |
import argparse
import os
import numpy as np
import seaborn
from matplotlib import pyplot as plt
from stable_baselines3.common.monitor import LoadMonitorResultsError, load_results
from stable_baselines3.common.results_plotter import X_EPISODES, X_TIMESTEPS, X_WALLTIME, ts2xy, window_func
# Activate seaborn
seaborn.se... | /rl_zoo3-2.1.0.tar.gz/rl_zoo3-2.1.0/rl_zoo3/plots/plot_train.py | 0.61451 | 0.363901 | plot_train.py | pypi |
<div align="center">
<!-- <img src="https://github.com/kaist-silab/rl4co/assets/34462374/249462ea-b15d-4358-8a11-6508903dae58" style="width:40%"> -->
<img src="https://github.com/kaist-silab/rl4co/assets/48984123/01a547b2-9722-4540-b0e1-9c12af094b15" style="width:40%">
</br></br>
<a href="https://pytorch.org/get-... | /rl4co-0.2.0.tar.gz/rl4co-0.2.0/README.md | 0.639511 | 0.955569 | README.md | pypi |
import pandas as pd
from ..utils import getList, getFactor
__all__=[
"list",
"query",
"basic_derivation",
"valuation_estimation",
"reversal",
"sentiment",
"power_volume",
"price_volume",
"momentum",
"volatility_value",
"earning_expectation",
"solvency",
"operation_ca... | /factor/factor.py | 0.655557 | 0.380126 | factor.py | pypi |
import pandas as pd
from ..utils import getList, getFactor
__all__=[
"list",
"query",
"basic_derivation",
"valuation_estimation",
"reversal",
"sentiment",
"power_volume",
"price_volume",
"momentum",
"volatility_value",
"earning_expectation",
"solvency",
"operation_ca... | /factor/std.py | 0.671794 | 0.379005 | std.py | pypi |
import pandas as pd
from ...utils import getList, getFactor
__all__=[
"list",
"query",
"rl_characteristic",
"dx_securities",
"tf_securities",
"inhouse",
]
GROUP="factor/vip"
def list():
""" 获取列表
获取因子列表
Args:
无
Returns:
(status,ret)
"""
return getList... | /factor/vip/vip.py | 0.63307 | 0.250357 | vip.py | pypi |
import pandas as pd
from ...utils import getList,getFactor
__all__=[
"list",
"query",
"rl_characteristic",
"dx_securities",
"tf_securities",
"inhouse",
]
GROUP="factor/vip/standard"
def list():
""" 获取列表
获取因子列表
Args:
无
Returns:
(status,ret)
"""
return... | /factor/vip/vip_std.py | 0.634543 | 0.247948 | vip_std.py | pypi |
# 1. Make OpenAI Gym like environment
- This example uses DDPG(Deep Deterministic Policy Gradient) with pybullet_env
- pybullet_env prerequisites: Open AI Gym, pybullet.
pip install gym
pip install pybullet
```
import gym
import pybullet_envs
import time
env = gym.make("InvertedPendulumBulletEnv-v0")
env.render(mod... | /rlagent-0.1.4.tar.gz/rlagent-0.1.4/tutorial/rlagent_tutorial_1_getting_started.ipynb | 0.406862 | 0.851953 | rlagent_tutorial_1_getting_started.ipynb | pypi |
import gym
import pybullet_envs
import tensorflow as tf
import numpy as np
import argparse
from rlagent.models import ActorCriticFF
from rlagent.agents import NStepMPIAgentFF
from rlagent.memories import NStepMemory
from rlagent.algorithms import A2C
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--env... | /rlagent-0.1.4.tar.gz/rlagent-0.1.4/train/train_a2c_mpi.py | 0.563138 | 0.154121 | train_a2c_mpi.py | pypi |
import curses
from libcurses.border import Border
from libcurses.bw import BorderedWindow
class WindowStack:
"""Vertical stack of windows."""
def __init__(self, neighbor_left, padding_y):
"""Create a vertical stack of windows with 'border-collapse: collapse'.
A visual stack, not a push-pop... | /rlane_libcurses-1.0.5-py3-none-any.whl/libcurses/stack.py | 0.803174 | 0.28708 | stack.py | pypi |
import curses
from collections import defaultdict, namedtuple
from loguru import logger
from libcurses.mouseevent import MouseEvent
class Mouse:
"""Mouse handling."""
@staticmethod
def enable():
"""Enable `curses.getkey` to return mouse events.
Call after `curses.initscr`. If trouble,... | /rlane_libcurses-1.0.5-py3-none-any.whl/libcurses/mouse.py | 0.733547 | 0.17006 | mouse.py | pypi |
import curses
import re
from loguru import logger
_COLORMAP = None # key=loguru-level-name, value=curses-color/attr
def get_colormap() -> dict[str, int]:
"""Return map of `loguru-level-name` to `curses-color/attr`.
Call after creating all custom levels with `logger.level()`.
Map is build once and cac... | /rlane_libcurses-1.0.5-py3-none-any.whl/libcurses/colormap.py | 0.572006 | 0.177152 | colormap.py | pypi |
import curses
from libcurses.border import Border
class BorderedWindow:
"""Bordered Window."""
def __init__(self, nlines, ncols, begin_y, begin_x, _border=None):
"""Create new bordered window with the given dimensions and optional border stylings.
A bordered window is composed of two windo... | /rlane_libcurses-1.0.5-py3-none-any.whl/libcurses/bw.py | 0.819533 | 0.38217 | bw.py | pypi |
from copy import copy
import iso8601
import datetime
import itertools
import re
from urllib.parse import urljoin as _urljoin
from m3u8 import protocol
'''
http://tools.ietf.org/html/draft-pantos-http-live-streaming-08#section-3.2
http://stackoverflow.com/questions/2785755/how-to-split-but-ignore-separators-in-quoted-... | /rlaphoenix.m3u8-3.4.0-py3-none-any.whl/m3u8/parser.py | 0.500977 | 0.182535 | parser.py | pypi |
import decimal
import os
import errno
from m3u8.protocol import (
ext_oatcls_scte35,
ext_x_asset,
ext_x_key,
ext_x_map,
ext_x_session_key,
ext_x_start,
)
from m3u8.parser import parse, format_date_time
from m3u8.mixins import BasePathMixin, GroupedBasePathMixin
class MalformedPlaylistError(Ex... | /rlaphoenix.m3u8-3.4.0-py3-none-any.whl/m3u8/model.py | 0.595257 | 0.301966 | model.py | pypi |
"""A simple double-DQN agent trained to play BSuite's Catch env."""
import collections
import random
from absl import app
from absl import flags
from bsuite.environments import catch
import haiku as hk
from haiku import nets
import jax
import jax.numpy as jnp
import numpy as np
import optax
import rlax
from rlax.examp... | /rlax-0.1.6-py3-none-any.whl/examples/simple_dqn.py | 0.863377 | 0.473779 | simple_dqn.py | pypi |
import collections
from absl import app
from absl import flags
from bsuite.environments import catch
from bsuite.utils import wrappers
import haiku as hk
from haiku import nets
import jax
import jax.numpy as jnp
import optax
import rlax
from rlax.examples import experiment
ActorOutput = collections.namedtuple("ActorOu... | /rlax-0.1.6-py3-none-any.whl/examples/pop_art.py | 0.822759 | 0.498901 | pop_art.py | pypi |
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