repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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d3rlpy | d3rlpy-master/tests/models/torch/test_transformers.py | import pytest
import torch
from d3rlpy.models.torch.transformers import (
GPT2,
MLP,
Block,
CausalSelfAttention,
ContinuousDecisionTransformer,
DiscreteDecisionTransformer,
GlobalPositionEncoding,
SimplePositionEncoding,
)
from .model_test import DummyEncoder, check_parameter_updates
... | 8,028 | 27.371025 | 80 | py |
d3rlpy | d3rlpy-master/tests/models/torch/model_test.py | import copy
from typing import Any, Sequence
import numpy as np
import torch
from torch.optim import SGD
from d3rlpy.models.torch.encoders import Encoder, EncoderWithAction
def check_parameter_updates(
model: torch.nn.Module, inputs: Any = None, output: Any = None
) -> None:
model.train()
params_before ... | 3,115 | 31.458333 | 81 | py |
d3rlpy | d3rlpy-master/tests/models/torch/q_functions/test_utility.py | import numpy as np
import pytest
import torch
from d3rlpy.models.torch.q_functions.utility import (
compute_quantile_huber_loss,
pick_quantile_value_by_action,
pick_value_by_action,
)
from ..model_test import ref_quantile_huber_loss
@pytest.mark.parametrize("batch_size", [32])
@pytest.mark.parametrize("... | 2,265 | 29.213333 | 80 | py |
d3rlpy | d3rlpy-master/tests/models/torch/q_functions/test_iqn_q_function.py | import pytest
import torch
from d3rlpy.models.torch import ContinuousIQNQFunction, DiscreteIQNQFunction
from ..model_test import (
DummyEncoder,
DummyEncoderWithAction,
check_parameter_updates,
)
@pytest.mark.parametrize("feature_size", [100])
@pytest.mark.parametrize("action_size", [2])
@pytest.mark.pa... | 3,902 | 30.731707 | 76 | py |
d3rlpy | d3rlpy-master/tests/models/torch/q_functions/test_fqf_q_function.py | import pytest
import torch
from d3rlpy.models.torch import ContinuousFQFQFunction, DiscreteFQFQFunction
from ..model_test import (
DummyEncoder,
DummyEncoderWithAction,
check_parameter_updates,
)
@pytest.mark.parametrize("feature_size", [100])
@pytest.mark.parametrize("action_size", [2])
@pytest.mark.pa... | 3,264 | 31.65 | 80 | py |
d3rlpy | d3rlpy-master/tests/models/torch/q_functions/test_qr_q_function.py | # pylint: disable=protected-access
import numpy as np
import pytest
import torch
from d3rlpy.models.torch import ContinuousQRQFunction, DiscreteQRQFunction
from d3rlpy.models.torch.q_functions.qr_q_function import _make_taus
from d3rlpy.models.torch.q_functions.utility import (
pick_quantile_value_by_action,
)
fr... | 4,889 | 32.724138 | 79 | py |
d3rlpy | d3rlpy-master/tests/models/torch/q_functions/test_mean_q_function.py | import numpy as np
import pytest
import torch
from d3rlpy.models.torch import ContinuousMeanQFunction, DiscreteMeanQFunction
from ..model_test import (
DummyEncoder,
DummyEncoderWithAction,
check_parameter_updates,
ref_huber_loss,
)
def filter_by_action(
value: np.ndarray, action: np.ndarray, ac... | 3,956 | 33.112069 | 79 | py |
d3rlpy | d3rlpy-master/tests/models/torch/q_functions/test_ensemble_q_function.py | from typing import List
import pytest
import torch
from d3rlpy.models.torch import (
ContinuousFQFQFunction,
ContinuousIQNQFunction,
ContinuousMeanQFunction,
ContinuousQFunction,
ContinuousQRQFunction,
DiscreteFQFQFunction,
DiscreteIQNQFunction,
DiscreteMeanQFunction,
DiscreteQFunc... | 8,711 | 33.709163 | 77 | py |
d3rlpy | d3rlpy-master/tests/algos/qlearning/algo_test.py | import os
from typing import Sequence, cast
from unittest.mock import Mock
import numpy as np
import onnxruntime as ort
import torch
from d3rlpy.algos import QLearningAlgoBase, QLearningAlgoImplBase
from d3rlpy.base import LearnableConfig
from d3rlpy.constants import ActionSpace
from d3rlpy.dataset import (
Episo... | 11,807 | 32.450425 | 81 | py |
d3rlpy | d3rlpy-master/tests/algos/transformer/test_inputs.py | from typing import Sequence
import numpy as np
import pytest
from d3rlpy.algos.transformer.inputs import (
TorchTransformerInput,
TransformerInput,
)
from d3rlpy.dataset import batch_pad_array, batch_pad_observations
from ...dummy_scalers import (
DummyActionScaler,
DummyObservationScaler,
DummyR... | 3,680 | 31.008696 | 80 | py |
d3rlpy | d3rlpy-master/tests/preprocessing/test_observation_scalers.py | from typing import Sequence
import numpy as np
import pytest
import torch
from d3rlpy.dataset import (
BasicTrajectorySlicer,
BasicTransitionPicker,
EpisodeGenerator,
)
from d3rlpy.preprocessing import (
MinMaxObservationScaler,
PixelObservationScaler,
StandardObservationScaler,
)
from ..dumm... | 8,391 | 31.527132 | 80 | py |
d3rlpy | d3rlpy-master/tests/preprocessing/test_action_scalers.py | from typing import Sequence
import gym
import numpy as np
import pytest
import torch
from d3rlpy.dataset import (
BasicTrajectorySlicer,
BasicTransitionPicker,
EpisodeGenerator,
)
from d3rlpy.preprocessing import MinMaxActionScaler
@pytest.mark.parametrize("action_size", [10])
@pytest.mark.parametrize("... | 4,189 | 30.037037 | 79 | py |
d3rlpy | d3rlpy-master/tests/preprocessing/test_reward_scalers.py | from typing import Sequence
import numpy as np
import pytest
import torch
from d3rlpy.dataset import (
BasicTrajectorySlicer,
BasicTransitionPicker,
EpisodeGenerator,
)
from d3rlpy.preprocessing import (
ClipRewardScaler,
ConstantShiftRewardScaler,
MinMaxRewardScaler,
MultiplyRewardScaler,... | 14,838 | 32.196868 | 80 | py |
d3rlpy | d3rlpy-master/tests/preprocessing/test_base.py | import numpy as np
import torch
from d3rlpy.preprocessing.base import add_leading_dims, add_leading_dims_numpy
def test_add_leading_dims() -> None:
x = torch.rand(3)
target = torch.rand(1, 2, 3)
assert add_leading_dims(x, target).shape == (1, 1, 3)
def test_add_leading_dims_numpy() -> None:
x = np.... | 442 | 25.058824 | 78 | py |
d3rlpy | d3rlpy-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,813 | 29.923077 | 79 | py |
d3rlpy | d3rlpy-master/reproductions/offline/iql.py | import argparse
from torch.optim.lr_scheduler import CosineAnnealingLR
import d3rlpy
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default="hopper-medium-v0")
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--gpu", type=int)... | 1,506 | 24.982759 | 78 | py |
d3rlpy | d3rlpy-master/reproductions/finetuning/iql_finetune.py | # pylint: disable=protected-access
import argparse
import copy
from torch.optim.lr_scheduler import CosineAnnealingLR
import d3rlpy
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default="antmaze-umaze-v0")
parser.add_argument("--seed", type=int, defaul... | 2,264 | 27.3125 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/base.py | import dataclasses
import io
import pickle
from abc import ABCMeta, abstractmethod
from typing import Any, BinaryIO, Generic, Optional, Type, TypeVar, Union
import gym
import torch
from gym.spaces import Box, Discrete
from typing_extensions import Self
from ._version import __version__
from .constants import IMPL_NOT... | 12,802 | 27.514477 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/torch_utility.py | import collections
import dataclasses
from typing import Any, Dict, List, Optional, Sequence, TypeVar, Union
import numpy as np
import torch
from torch import nn
from torch.optim import Optimizer
from .dataset import TrajectoryMiniBatch, TransitionMiniBatch
from .preprocessing import ActionScaler, ObservationScaler, ... | 9,874 | 29.956113 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/__init__.py | import random
import numpy as np
import torch
from . import (
algos,
dataset,
datasets,
envs,
logging,
metrics,
models,
ope,
preprocessing,
)
from ._version import __version__
from .base import load_learnable
def seed(n: int) -> None:
"""Sets random seed value.
Args:
... | 496 | 14.53125 | 45 | py |
d3rlpy | d3rlpy-master/d3rlpy/ope/fqe.py | import dataclasses
from typing import Dict, Optional
import numpy as np
from ..algos.qlearning import QLearningAlgoBase, QLearningAlgoImplBase
from ..base import DeviceArg, LearnableConfig, register_learnable
from ..constants import (
ALGO_NOT_GIVEN_ERROR,
IMPL_NOT_INITIALIZED_ERROR,
ActionSpace,
)
from .... | 8,378 | 33.9125 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/ope/torch/fqe_impl.py | import copy
import torch
from torch.optim import Optimizer
from ...algos.qlearning.base import QLearningAlgoImplBase
from ...algos.qlearning.torch.utility import (
ContinuousQFunctionMixin,
DiscreteQFunctionMixin,
)
from ...dataset import Shape
from ...models.torch import (
EnsembleContinuousQFunction,
... | 3,436 | 26.943089 | 73 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/utility.py | from torch import nn
from ..torch_utility import Swish
__all__ = ["create_activation"]
def create_activation(activation_type: str) -> nn.Module:
if activation_type == "relu":
return nn.ReLU()
elif activation_type == "tanh":
return nn.Tanh()
elif activation_type == "swish":
return... | 443 | 23.666667 | 57 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/optimizers.py | import dataclasses
from typing import Iterable, Tuple
from torch import nn
from torch.optim import SGD, Adam, AdamW, Optimizer, RMSprop
from ..serializable_config import DynamicConfig, generate_config_registration
__all__ = [
"OptimizerFactory",
"SGDFactory",
"AdamFactory",
"AdamWFactory",
"RMSpr... | 5,301 | 25.118227 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/q_functions.py | import dataclasses
from ..serializable_config import DynamicConfig, generate_config_registration
from .torch import (
ContinuousFQFQFunction,
ContinuousIQNQFunction,
ContinuousMeanQFunction,
ContinuousQFunction,
ContinuousQRQFunction,
DiscreteFQFQFunction,
DiscreteIQNQFunction,
Discrete... | 6,626 | 26.384298 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/builders.py | from typing import Sequence, cast
import torch
from torch import nn
from ..dataset import Shape
from .encoders import EncoderFactory
from .q_functions import QFunctionFactory
from .torch import (
CategoricalPolicy,
ConditionalVAE,
ContinuousDecisionTransformer,
DeterministicPolicy,
DeterministicRe... | 10,171 | 27.734463 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/encoders.py | from dataclasses import dataclass, field
from typing import List, Optional, Union
from ..dataset import Shape, cast_flat_shape
from ..serializable_config import DynamicConfig, generate_config_registration
from .torch import (
Encoder,
EncoderWithAction,
PixelEncoder,
PixelEncoderWithAction,
VectorE... | 10,380 | 31.747634 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/v_functions.py | from typing import cast
import torch
import torch.nn.functional as F
from torch import nn
from .encoders import Encoder
__all__ = ["ValueFunction"]
class ValueFunction(nn.Module): # type: ignore
_encoder: Encoder
_fc: nn.Linear
def __init__(self, encoder: Encoder):
super().__init__()
... | 860 | 24.323529 | 62 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/distributions.py | import math
from abc import ABCMeta, abstractmethod
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
from torch.distributions import Normal
__all__ = [
"Distribution",
"GaussianDistribution",
"SquashedGaussianDistribution",
]
class Distribution(metaclass=ABCMeta):
@abs... | 4,284 | 27.566667 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/transformers.py | import math
from abc import ABCMeta, abstractmethod
from typing import Tuple
import torch
import torch.nn.functional as F
from torch import nn
from .encoders import Encoder
from .parameters import Parameter
__all__ = [
"ContinuousDecisionTransformer",
"DiscreteDecisionTransformer",
"SimplePositionEncodin... | 12,509 | 30.994885 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/imitators.py | from abc import ABCMeta, abstractmethod
from typing import Tuple, cast
import torch
import torch.nn.functional as F
from torch import nn
from torch.distributions import Normal
from torch.distributions.kl import kl_divergence
from .encoders import Encoder, EncoderWithAction
__all__ = [
"ConditionalVAE",
"Imit... | 7,803 | 29.603922 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/policies.py | import math
from abc import ABCMeta, abstractmethod
from typing import Tuple, Union, cast
import torch
import torch.nn.functional as F
from torch import nn
from torch.distributions import Categorical
from .distributions import GaussianDistribution, SquashedGaussianDistribution
from .encoders import Encoder, EncoderWi... | 11,312 | 30.425 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/parameters.py | import torch
from torch import nn
__all__ = ["Parameter"]
class Parameter(nn.Module): # type: ignore
_parameter: nn.Parameter
def __init__(self, data: torch.Tensor):
super().__init__()
self._parameter = nn.Parameter(data)
def forward(self) -> torch.Tensor:
return self._paramete... | 484 | 20.086957 | 44 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/encoders.py | from abc import ABCMeta, abstractmethod
from typing import List, Optional, Sequence
import torch
import torch.nn.functional as F
from torch import nn
from ...itertools import last_flag
__all__ = [
"Encoder",
"EncoderWithAction",
"PixelEncoder",
"PixelEncoderWithAction",
"VectorEncoder",
"Vect... | 11,014 | 30.203966 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/q_functions/base.py | from abc import ABCMeta, abstractmethod
from typing import Optional
import torch
from ..encoders import Encoder, EncoderWithAction
__all__ = ["QFunction", "DiscreteQFunction", "ContinuousQFunction"]
class QFunction(metaclass=ABCMeta):
@abstractmethod
def compute_error(
self,
observations: t... | 1,574 | 21.826087 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/q_functions/utility.py | from typing import cast
import torch
import torch.nn.functional as F
__all__ = [
"pick_value_by_action",
"pick_quantile_value_by_action",
"compute_huber_loss",
"compute_quantile_huber_loss",
"compute_quantile_loss",
"compute_reduce",
]
def pick_value_by_action(
values: torch.Tensor, acti... | 2,552 | 30.518519 | 77 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/q_functions/mean_q_function.py | from typing import Optional, cast
import torch
import torch.nn.functional as F
from torch import nn
from ..encoders import Encoder, EncoderWithAction
from .base import ContinuousQFunction, DiscreteQFunction
from .utility import compute_huber_loss, compute_reduce, pick_value_by_action
__all__ = ["DiscreteMeanQFunctio... | 3,194 | 30.019417 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/q_functions/fqf_q_function.py | from typing import Optional, Tuple, cast
import torch
from torch import nn
from ..encoders import Encoder, EncoderWithAction
from .base import ContinuousQFunction, DiscreteQFunction
from .iqn_q_function import compute_iqn_feature
from .utility import (
compute_quantile_loss,
compute_reduce,
pick_quantile_... | 9,520 | 33.125448 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/q_functions/iqn_q_function.py | import math
from typing import Optional, cast
import torch
from torch import nn
from ..encoders import Encoder, EncoderWithAction
from .base import ContinuousQFunction, DiscreteQFunction
from .utility import (
compute_quantile_loss,
compute_reduce,
pick_quantile_value_by_action,
)
__all__ = ["DiscreteIQN... | 7,216 | 29.84188 | 77 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/q_functions/qr_q_function.py | from typing import Optional, cast
import torch
from torch import nn
from ..encoders import Encoder, EncoderWithAction
from .base import ContinuousQFunction, DiscreteQFunction
from .utility import (
compute_quantile_loss,
compute_reduce,
pick_quantile_value_by_action,
)
__all__ = ["DiscreteQRQFunction", "... | 5,040 | 29.737805 | 77 | py |
d3rlpy | d3rlpy-master/d3rlpy/models/torch/q_functions/ensemble_q_function.py | from typing import List, Optional, Tuple, Union, cast
import torch
from torch import nn
from .base import ContinuousQFunction, DiscreteQFunction
__all__ = [
"EnsembleQFunction",
"EnsembleDiscreteQFunction",
"EnsembleContinuousQFunction",
"compute_max_with_n_actions",
"compute_max_with_n_actions_a... | 9,237 | 34.394636 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/crr.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_continuous_q_function,
create_non_squashed_normal_policy,
)
from ..... | 7,331 | 35.477612 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/base.py | from abc import abstractmethod
from collections import defaultdict
from typing import (
Any,
Callable,
Dict,
Generator,
Generic,
List,
Optional,
Tuple,
TypeVar,
cast,
)
import gym
import numpy as np
import torch
from tqdm.auto import tqdm, trange
from typing_extensions import Se... | 32,009 | 33.1258 | 101 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/sac.py | import dataclasses
import math
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_categorical_policy,
create_continuous_q_function,
c... | 12,811 | 36.35277 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/ddpg.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_continuous_q_function,
create_deterministic_policy,
)
from ...model... | 5,449 | 36.328767 | 88 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/td3.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_continuous_q_function,
create_deterministic_policy,
)
from ...model... | 6,320 | 37.542683 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/plas.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_conditional_vae,
create_continuous_q_function,
create_determini... | 12,022 | 38.419672 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/dqn.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import create_discrete_q_function
from ...models.encoders import EncoderFactory, make_encod... | 6,647 | 34.174603 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/bear.py | import dataclasses
import math
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_conditional_vae,
create_continuous_q_function,
crea... | 10,883 | 38.868132 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/bc.py | import dataclasses
from typing import Dict, Generic, TypeVar
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_deterministic_regressor,
create_discrete_imitator... | 6,171 | 32.182796 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/nfq.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import create_discrete_q_function
from ...models.encoders import EncoderFactory, make_encod... | 3,697 | 33.886792 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/iql.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_continuous_q_function,
create_non_squashed_normal_policy,
creat... | 6,525 | 34.661202 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/cql.py | import dataclasses
import math
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_continuous_q_function,
create_discrete_q_function,
... | 12,588 | 37.975232 | 81 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/bcq.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_conditional_vae,
create_continuous_q_function,
create_determini... | 14,962 | 37.268542 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/random_policy.py | import dataclasses
from typing import Dict
import numpy as np
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import ActionSpace
from ...dataset import Observation, Shape
from ...torch_utility import TorchMiniBatch
from .base import QLearningAlgoBase
__all__ = [
"RandomPolicy... | 4,336 | 30.201439 | 96 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/awac.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_continuous_q_function,
create_non_squashed_normal_policy,
)
from ..... | 5,973 | 36.10559 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/td3_plus_bc.py | import dataclasses
from typing import Dict
from ...base import DeviceArg, LearnableConfig, register_learnable
from ...constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ...dataset import Shape
from ...models.builders import (
create_continuous_q_function,
create_deterministic_policy,
)
from ...model... | 5,837 | 36.184713 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/utility.py | from typing import Optional
import torch
from typing_extensions import Protocol
from ....models.torch import (
EnsembleContinuousQFunction,
EnsembleDiscreteQFunction,
)
__all__ = ["DiscreteQFunctionMixin", "ContinuousQFunctionMixin"]
class _DiscreteQFunctionProtocol(Protocol):
_q_func: Optional[Ensembl... | 1,104 | 26.625 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/cql_impl.py | import math
from typing import Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
EnsembleContinuousQFunction,
EnsembleDiscreteQFunction,
Parameter,
SquashedNormalPolicy,
)
from ....torch_... | 8,546 | 32.517647 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/dqn_impl.py | import copy
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import EnsembleDiscreteQFunction, EnsembleQFunction
from ....torch_utility import TorchMiniBatch, hard_sync, train_api
from ..base import QLearningAlgoImplBase
from .utility import DiscreteQFunctionMixin
__... | 3,014 | 27.990385 | 76 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/td3_plus_bc_impl.py | # pylint: disable=too-many-ancestors
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import DeterministicPolicy, EnsembleContinuousQFunction
from ....torch_utility import TorchMiniBatch
from .td3_impl import TD3Impl
__all__ = ["TD3PlusBCImpl"]
class TD3PlusBCImpl(... | 1,546 | 28.75 | 77 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/awac_impl.py | import torch
import torch.nn.functional as F
from torch.optim import Adam, Optimizer
from ....dataset import Shape
from ....models.torch import (
EnsembleContinuousQFunction,
NonSquashedNormalPolicy,
Parameter,
Policy,
)
from ....torch_utility import TorchMiniBatch
from .sac_impl import SACImpl
__all_... | 3,267 | 31.356436 | 74 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/bc_impl.py | from abc import ABCMeta
from typing import Union
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
DeterministicPolicy,
DeterministicRegressor,
DiscreteImitator,
Imitator,
Policy,
ProbablisticRegressor,
SquashedNormalPolicy,
)
from ... | 4,059 | 25.710526 | 73 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/bear_impl.py | from typing import Tuple
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
ConditionalVAE,
EnsembleContinuousQFunction,
Parameter,
SquashedNormalPolicy,
compute_max_with_n_actions_and_indices,
)
from ....torch_utility import TorchMiniBatch,... | 8,012 | 32.11157 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/plas_impl.py | import copy
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
ConditionalVAE,
DeterministicPolicy,
DeterministicResidualPolicy,
EnsembleContinuousQFunction,
)
from ....torch_utility import TorchMiniBatch, soft_sync, train_api
from .ddpg_impl im... | 5,343 | 31.785276 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/bcq_impl.py | import math
from typing import cast
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
ConditionalVAE,
DeterministicResidualPolicy,
DiscreteImitator,
EnsembleContinuousQFunction,
EnsembleDiscreteQFunction,
compute_max_with_n_actions,
)
f... | 7,013 | 33.722772 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/ddpg_impl.py | import copy
from abc import ABCMeta, abstractmethod
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
EnsembleContinuousQFunction,
EnsembleQFunction,
Policy,
)
from ....torch_utility import TorchMiniBatch, soft_sync, train_api
from ..base import QL... | 4,252 | 27.165563 | 73 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/td3_impl.py | import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import DeterministicPolicy, EnsembleContinuousQFunction
from ....torch_utility import TorchMiniBatch
from .ddpg_impl import DDPGImpl
__all__ = ["TD3Impl"]
class TD3Impl(DDPGImpl):
_target_smoothing_sigma: float
... | 1,936 | 31.283333 | 77 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/sac_impl.py | import copy
import math
from typing import Tuple
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
CategoricalPolicy,
EnsembleContinuousQFunction,
EnsembleDiscreteQFunction,
EnsembleQFunction,
Parameter,
Policy,
)
from ....torch_utility... | 7,320 | 29.890295 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/iql_impl.py | from typing import Tuple
import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
EnsembleContinuousQFunction,
NonSquashedNormalPolicy,
ValueFunction,
)
from ....torch_utility import TorchMiniBatch, train_api
from .ddpg_impl import DDPGBaseImpl
__all__ =... | 3,549 | 29.084746 | 74 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/qlearning/torch/crr_impl.py | import torch
import torch.nn.functional as F
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import (
EnsembleContinuousQFunction,
NonSquashedNormalPolicy,
)
from ....torch_utility import TorchMiniBatch, hard_sync
from .ddpg_impl import DDPGBaseImpl
__all__ = ["CRRImpl"]
... | 5,466 | 34.044872 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/transformer/base.py | import dataclasses
from abc import abstractmethod
from collections import defaultdict, deque
from typing import Any, Callable, Deque, Dict, Generic, Optional, TypeVar, Union
import gym
import numpy as np
import torch
from tqdm.auto import tqdm
from typing_extensions import Self
from ...base import ImplBase, Learnable... | 12,680 | 33.08871 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/transformer/inputs.py | import dataclasses
from typing import Optional
import numpy as np
import torch
from ...dataset import (
ObservationSequence,
batch_pad_array,
batch_pad_observations,
get_axis_size,
slice_observations,
)
from ...preprocessing import ActionScaler, ObservationScaler, RewardScaler
from ...torch_utilit... | 4,010 | 35.463636 | 78 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/transformer/decision_transformer.py | import dataclasses
from typing import Dict
import torch
from ...base import DeviceArg, register_learnable
from ...constants import ActionSpace
from ...dataset import Shape
from ...models import (
EncoderFactory,
OptimizerFactory,
make_encoder_field,
make_optimizer_field,
)
from ...models.builders impo... | 4,813 | 36.030769 | 80 | py |
d3rlpy | d3rlpy-master/d3rlpy/algos/transformer/torch/decision_transformer_impl.py | import torch
from torch.optim import Optimizer
from ....dataset import Shape
from ....models.torch import ContinuousDecisionTransformer
from ....torch_utility import TorchTrajectoryMiniBatch, eval_api, train_api
from ..base import TransformerAlgoImplBase
from ..inputs import TorchTransformerInput
__all__ = ["Decision... | 2,222 | 29.452055 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/preprocessing/base.py | from abc import ABCMeta, abstractmethod
from typing import Any, Sequence
import gym
import numpy as np
import torch
from ..dataset import (
EpisodeBase,
TrajectorySlicerProtocol,
TransitionPickerProtocol,
)
from ..serializable_config import DynamicConfig
__all__ = ["Scaler", "add_leading_dims", "add_lead... | 3,265 | 24.716535 | 76 | py |
d3rlpy | d3rlpy-master/d3rlpy/preprocessing/action_scalers.py | import dataclasses
from typing import Any, Optional, Sequence
import gym
import numpy as np
import torch
from ..dataset import (
EpisodeBase,
TrajectorySlicerProtocol,
TransitionPickerProtocol,
)
from ..serializable_config import (
generate_optional_config_generation,
make_optional_numpy_field,
)
... | 6,533 | 33.755319 | 79 | py |
d3rlpy | d3rlpy-master/d3rlpy/preprocessing/reward_scalers.py | import dataclasses
from typing import Any, Optional, Sequence
import gym
import numpy as np
import torch
from ..dataset import (
EpisodeBase,
TrajectorySlicerProtocol,
TransitionPickerProtocol,
)
from ..serializable_config import generate_optional_config_generation
from .base import Scaler
__all__ = [
... | 15,062 | 29.005976 | 74 | py |
d3rlpy | d3rlpy-master/d3rlpy/preprocessing/observation_scalers.py | import dataclasses
from typing import Any, Optional, Sequence
import gym
import numpy as np
import torch
from ..dataset import (
EpisodeBase,
TrajectorySlicerProtocol,
TransitionPickerProtocol,
)
from ..serializable_config import (
generate_optional_config_generation,
make_optional_numpy_field,
)
... | 13,692 | 33.578283 | 80 | py |
UQ360 | UQ360-main/tests/test_EnsembleHeteroscedasticRegression.py | import unittest
import numpy as np
import torch
np.random.seed(42)
torch.manual_seed(42)
class TestEnsembleHeteroscedasticRegression(unittest.TestCase):
def _generate_mock_data(self, n_samples, n_features):
from sklearn.datasets import make_regression
return make_regression(n_samples, n_feature... | 1,619 | 34.217391 | 139 | py |
UQ360 | UQ360-main/tests/test_AuxiliaryIntervalPredictor.py | import unittest
import numpy as np
import torch
np.random.seed(42)
torch.manual_seed(42)
class TestAuxiliaryIntervalPredictor(unittest.TestCase):
def _generate_mock_data(self, n_samples, n_features):
from sklearn.datasets import make_regression
return make_regression(n_samples, n_features, rand... | 1,575 | 34.818182 | 125 | py |
UQ360 | UQ360-main/tests/test_ActivelyLearnedModel.py | import unittest
import numpy as np
import torch
np.random.seed(42)
torch.manual_seed(42)
class TestActivelyLearnedModel(unittest.TestCase):
def test_fit_predict_and_metrics(self):
from uq360.algorithms.actively_learned_model import ActivelyLearnedModel
from uq360.algorithms.ensemble_heterosceda... | 2,274 | 31.971014 | 111 | py |
UQ360 | UQ360-main/tests/utils.py | import unittest
import numpy as np
import torch
from sklearn.model_selection import train_test_split
def create_train_test_prod_split(x, y, test_size=0.25):
"""
returns x_train, y_train, x_test, y_test, x_prod, y_prod
"""
x_train, x_test, y_train, y_test = train_test_split(x, y,
... | 3,184 | 39.316456 | 117 | py |
UQ360 | UQ360-main/tests/test_HeteroscedasticRegression.py | import unittest
import numpy as np
import torch
np.random.seed(42)
torch.manual_seed(42)
class TestHeteroscedasticRegression(unittest.TestCase):
def _generate_mock_data(self, n_samples, n_features):
from sklearn.datasets import make_regression
return make_regression(n_samples, n_features, rando... | 1,347 | 31.095238 | 126 | py |
UQ360 | UQ360-main/tests/test_HomoscedasticGPRegression.py | import unittest
import numpy as np
import torch
np.random.seed(42)
torch.manual_seed(42)
class TestHomoscedasticGPRegression(unittest.TestCase):
def _generate_mock_data(self, n_samples, n_features):
from sklearn.datasets import make_regression
return make_regression(n_samples, n_features, rando... | 1,167 | 28.2 | 132 | py |
UQ360 | UQ360-main/tests/test_Hshoe.py | import unittest
import numpy as np
import torch
from uq360.algorithms.variational_bayesian_neural_networks.bnn import BnnRegression, BnnClassification
from uq360.models.bayesian_neural_networks.layer_utils import InvGammaHalfCauchyLayer
from uq360.models.bayesian_neural_networks.layers import HorseshoeLayer
class T... | 6,084 | 48.072581 | 125 | py |
UQ360 | UQ360-main/tests/test_layer_scoring/__init__.py | import unittest
from typing import Type
from unittest import TestCase
import numpy as np
from torch import nn
from tests.utils import PlusOne
from uq360.algorithms.layer_scoring.latent_scorer import LatentScorer
class LatentScorerTester(TestCase):
ScorerClass: Type[LatentScorer]
scorer_kwargs: dict
pred... | 3,764 | 31.456897 | 81 | py |
UQ360 | UQ360-main/tests/test_utils/test_latent_features.py | from unittest import TestCase
import torch
from tests.utils import PlusOne
from uq360.utils.latent_features import LatentFeatures
class TestLatentFeatures(TestCase):
N = 10
d = 2
def setUp(self):
self.first_plus_one = PlusOne()
self.relu = torch.nn.ReLU()
self.second_plus_one = ... | 2,818 | 32.559524 | 87 | py |
UQ360 | UQ360-main/uq360/models/heteroscedastic_mlp.py | import torch
import torch.nn.functional as F
from uq360.models.noise_models.heteroscedastic_noise_models import GaussianNoise
class GaussianNoiseMLPNet(torch.nn.Module):
def __init__(self, num_features, num_outputs, num_hidden):
super(GaussianNoiseMLPNet, self).__init__()
self.fc = torch.nn.Linear... | 820 | 38.095238 | 85 | py |
UQ360 | UQ360-main/uq360/models/noise_models/heteroscedastic_noise_models.py | import math
import numpy as np
import torch
from scipy.special import gammaln
from uq360.models.noise_models.noisemodel import AbstractNoiseModel
from torch.nn import Parameter
td = torch.distributions
def transform(a):
return torch.log(1 + torch.exp(a))
class GaussianNoise(torch.nn.Module, AbstractNoiseModel... | 1,182 | 24.717391 | 110 | py |
UQ360 | UQ360-main/uq360/models/noise_models/homoscedastic_noise_models.py | import math
import numpy as np
import torch
from scipy.special import gammaln
from uq360.models.noise_models.noisemodel import AbstractNoiseModel
from torch.nn import Parameter
td = torch.distributions
def transform(a):
return torch.log(1 + torch.exp(a))
class GaussianNoiseGammaPrecision(torch.nn.Module, Abst... | 2,585 | 31.734177 | 113 | py |
UQ360 | UQ360-main/uq360/models/bayesian_neural_networks/layer_utils.py | """
Contains implementations of various utilities used by Horseshoe Bayesian layers
"""
import numpy as np
import torch
from torch.nn import Parameter
td = torch.distributions
gammaln = torch.lgamma
def diag_gaussian_entropy(log_std, D):
return 0.5 * D * (1.0 + torch.log(2 * np.pi)) + torch.sum(log_std)
def ... | 5,342 | 39.477273 | 118 | py |
UQ360 | UQ360-main/uq360/models/bayesian_neural_networks/misc.py | import numpy as np
import torch
from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseFixedPrecision
def compute_test_ll(y_test, y_pred_samples, std_y=1.):
"""
Computes test log likelihoods = (1 / Ntest) * \sum_n p(y_n | x_n, D_train)
:param y_test: True y
:param y_pred_samples:... | 853 | 41.7 | 104 | py |
UQ360 | UQ360-main/uq360/models/bayesian_neural_networks/layers.py | """
Contains implementations of various Bayesian layers
"""
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from uq360.models.bayesian_neural_networks.layer_utils import InvGammaHalfCauchyLayer, InvGammaLayer
td = torch.distributions
def reparam(mu, logvar, do_sample... | 8,223 | 43.454054 | 118 | py |
UQ360 | UQ360-main/uq360/models/bayesian_neural_networks/bnn_models/bayesian_mlp.py | from abc import ABC
import torch
from torch import nn
from uq360.models.bayesian_neural_networks.layers import BayesianLinearLayer
from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision
import numpy as np
td = torch.distributions
class BayesianNN(nn.Module, ABC):
"""
Bay... | 5,950 | 43.081481 | 137 | py |
UQ360 | UQ360-main/uq360/models/bayesian_neural_networks/bnn_models/horseshoe_mlp.py | from abc import ABC
import numpy as np
import torch
from torch import nn
from uq360.models.bayesian_neural_networks.layers import HorseshoeLayer, BayesianLinearLayer, RegularizedHorseshoeLayer
from uq360.models.noise_models.homoscedastic_noise_models import GaussianNoiseGammaPrecision
import numpy as np
td = torch.di... | 6,754 | 42.863636 | 137 | py |
UQ360 | UQ360-main/uq360/algorithms/heteroscedastic_regression/heteroscedastic_regression.py | from collections import namedtuple
import numpy as np
import torch
from scipy.stats import norm
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
from uq360.algorithms.builtinuq import BuiltinUQ
from uq360.models.heteroscedastic_mlp import GaussianNoiseMLPNet as _MLPNet
np.random.seed... | 6,037 | 38.464052 | 123 | py |
UQ360 | UQ360-main/uq360/algorithms/variational_bayesian_neural_networks/bnn.py | import copy
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.utils.data as data_utils
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from uq360.algorithms.builtinuq import BuiltinUQ
fr... | 13,575 | 46.303136 | 119 | py |
UQ360 | UQ360-main/uq360/algorithms/auxiliary_interval_predictor/auxiliary_interval_predictor.py | from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from scipy.stats import norm
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
from uq360.algorithms.builtinuq import BuiltinUQ
np.random.seed(42)
torch.manual_seed(42)
class _MLPNet_... | 9,845 | 41.257511 | 137 | py |
UQ360 | UQ360-main/uq360/algorithms/ensemble_heteroscedastic_regression/ensemble_heteroscedastic_regression.py | from collections import namedtuple
import numpy as np
from scipy.stats import norm
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from uq360.algorithms.heteroscedastic_regression import HeteroscedasticRegression
from uq360.algorithms.builtinuq import BuiltinUQ
class _Ensemble(torch.nn.Mod... | 5,698 | 38.576389 | 130 | py |
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