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repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
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null | coax-main/coax/proba_dists/_normal.py | import warnings
import jax
import jax.numpy as jnp
import numpy as onp
from gymnasium.spaces import Box
from ..utils import clipped_logit, jit
from ._base import BaseProbaDist
__all__ = (
'NormalDist',
)
class NormalDist(BaseProbaDist):
r"""
A differentiable normal distribution.
The input ``dist... | 13,182 | 33.692105 | 100 | py |
null | coax-main/coax/proba_dists/_normal_test.py | import gymnasium
import jax
import haiku as hk
from .._base.test_case import TestCase
from ._normal import NormalDist
class TestNormalDist(TestCase):
decimal = 5
def setUp(self):
self.rngs = hk.PRNGSequence(13)
def tearDown(self):
del self.rngs
def test_kl_divergence(self):
... | 1,443 | 33.380952 | 89 | py |
null | coax-main/coax/proba_dists/_squashed_normal.py | import jax
import jax.numpy as jnp
import numpy as onp
from ._base import BaseProbaDist
from ._normal import NormalDist
class SquashedNormalDist(BaseProbaDist):
r"""
A differentiable squashed normal distribution.
The input ``dist_params`` to each of the functions is expected to be of the form:
.. ... | 7,209 | 26.519084 | 100 | py |
null | coax-main/coax/regularizers/__init__.py | r"""
Regularizers
============
.. autosummary::
:nosignatures:
coax.regularizers.EntropyRegularizer
coax.regularizers.KLDivRegularizer
----
This is a collection of regularizers that can be used to put soft constraints on stochastic function
approximators. These is typically added to the loss/objective t... | 789 | 19.789474 | 100 | py |
null | coax-main/coax/regularizers/_base.py | import haiku as hk
from ..utils import is_stochastic, jit
from .._core.base_stochastic_func_type1 import BaseStochasticFuncType1
from .._core.base_stochastic_func_type2 import BaseStochasticFuncType2
class Regularizer:
r"""
Abstract base class for policy regularizers. Check out
:class:`coax.regularizers... | 2,979 | 32.111111 | 100 | py |
null | coax-main/coax/regularizers/_entropy.py | import jax.numpy as jnp
from ..utils import jit
from ._base import Regularizer
class EntropyRegularizer(Regularizer):
r"""
Policy regularization term based on the entropy of the policy.
The regularization term is to be added to the loss function:
.. math::
\text{loss}(\theta; s,a)\ =\ -J(... | 2,326 | 25.146067 | 95 | py |
null | coax-main/coax/regularizers/_kl_div.py | import jax.numpy as jnp
from ..utils import jit
from ._base import Regularizer
class KLDivRegularizer(Regularizer):
r"""
Policy regularization term based on the Kullback-Leibler divergence of the policy relative to a
given set of priors.
The regularization term is to be added to the loss function:
... | 3,192 | 28.564815 | 99 | py |
null | coax-main/coax/regularizers/_nstep_entropy.py | import haiku as hk
import jax
import jax.numpy as jnp
from .._core.base_stochastic_func_type2 import BaseStochasticFuncType2
from ..utils import jit
from ._entropy import EntropyRegularizer
class NStepEntropyRegularizer(EntropyRegularizer):
r"""
Policy regularization term based on the n-step entropy of the ... | 5,091 | 37.575758 | 96 | py |
null | coax-main/coax/reward_tracing/__init__.py | r"""
Reward Tracing
==============
.. autosummary::
:nosignatures:
coax.reward_tracing.NStep
coax.reward_tracing.MonteCarlo
coax.reward_tracing.TransitionBatch
----
The term **reward tracing** refers to the process of turning raw experience into
:class:`TransitionBatch <coax.reward_tracing.Transitio... | 1,509 | 22.968254 | 99 | py |
null | coax-main/coax/reward_tracing/_base.py | from abc import ABC, abstractmethod
import jax
import numpy as onp
from .._base.errors import InsufficientCacheError
__all__ = (
'BaseRewardTracer',
)
class BaseRewardTracer(ABC):
@abstractmethod
def reset(self):
r"""
Reset the cache to the initial state.
"""
pass
... | 1,848 | 18.670213 | 90 | py |
null | coax-main/coax/reward_tracing/_montecarlo.py | from .._base.errors import InsufficientCacheError, EpisodeDoneError
from ._base import BaseRewardTracer
from ._transition import TransitionBatch
__all__ = (
'MonteCarlo',
)
class MonteCarlo(BaseRewardTracer):
r"""
A short-term cache for episodic Monte Carlo sampling.
Parameters
----------
g... | 1,854 | 27.538462 | 99 | py |
null | coax-main/coax/reward_tracing/_montecarlo_test.py | from itertools import islice
import pytest
import gymnasium
import jax.numpy as jnp
from numpy.testing import assert_array_almost_equal
from .._base.errors import InsufficientCacheError
from ..utils import check_array
from ._montecarlo import MonteCarlo
class MockEnv:
action_space = gymnasium.spaces.Discrete(10... | 3,645 | 35.828283 | 93 | py |
null | coax-main/coax/reward_tracing/_nstep.py | from collections import deque
from itertools import islice
import numpy as onp
from .._base.errors import InsufficientCacheError, EpisodeDoneError
from ._base import BaseRewardTracer
from ._transition import TransitionBatch
__all__ = (
'NStep',
)
class NStep(BaseRewardTracer):
r"""
A short-term cache ... | 4,059 | 30.968504 | 99 | py |
null | coax-main/coax/reward_tracing/_nstep_test.py | from itertools import islice
import pytest
import gymnasium
import jax.numpy as jnp
from numpy.testing import assert_array_almost_equal
from .._base.errors import InsufficientCacheError, EpisodeDoneError
from ..utils import check_array
from ._nstep import NStep
class MockEnv:
action_space = gymnasium.spaces.Dis... | 11,381 | 40.845588 | 93 | py |
null | coax-main/coax/reward_tracing/_transition.py | from functools import partial
import jax
import jax.numpy as jnp
import numpy as onp
from .._base.mixins import CopyMixin
from ..utils import pretty_repr
__all__ = (
'TransitionBatch',
)
class TransitionBatch(CopyMixin):
r"""
A container object for a batch of MDP transitions.
Parameters
----... | 7,570 | 29.651822 | 97 | py |
null | coax-main/coax/td_learning/__init__.py | r"""
TD Learning
===========
.. autosummary::
:nosignatures:
coax.td_learning.SimpleTD
coax.td_learning.Sarsa
coax.td_learning.ExpectedSarsa
coax.td_learning.QLearning
coax.td_learning.DoubleQLearning
coax.td_learning.SoftQLearning
coax.td_learning.ClippedDoubleQLearning
coax.td_le... | 1,702 | 27.383333 | 98 | py |
null | coax-main/coax/td_learning/_base.py | from abc import ABC, abstractmethod
import jax
import jax.numpy as jnp
import haiku as hk
import optax
import chex
from .._base.mixins import RandomStateMixin
from ..utils import get_grads_diagnostics, is_policy, is_stochastic, is_qfunction, is_vfunction, jit
from ..value_losses import huber, quantile_huber
from ..re... | 22,895 | 40.32852 | 100 | py |
null | coax-main/coax/td_learning/_clippeddoubleqlearning.py | import warnings
import jax
import jax.numpy as jnp
import haiku as hk
import chex
from gymnasium.spaces import Discrete
from ..proba_dists import DiscretizedIntervalDist, EmpiricalQuantileDist
from ..utils import (get_grads_diagnostics, is_policy, is_qfunction,
is_stochastic, jit, single_to_batch... | 17,026 | 46.166205 | 100 | py |
null | coax-main/coax/td_learning/_clippeddoubleqlearning_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.q import Q
from .._core.stochastic_q import StochasticQ
from .._core.policy import Policy
from ..utils import get_transition_batch
from ._clippeddoubleqlearning import ClippedDoubleQLearning
class TestClippedDoubleQL... | 8,951 | 37.586207 | 98 | py |
null | coax-main/coax/td_learning/_doubleqlearning.py | import warnings
import haiku as hk
import chex
from gymnasium.spaces import Discrete
from ..utils import is_stochastic
from ._base import BaseTDLearningQWithTargetPolicy
class DoubleQLearning(BaseTDLearningQWithTargetPolicy):
r"""
TD-learning with `Double-DQN <https://arxiv.org/abs/1509.06461>`_ style doub... | 5,168 | 37.288889 | 100 | py |
null | coax-main/coax/td_learning/_doubleqlearning_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.q import Q
from .._core.policy import Policy
from ..utils import get_transition_batch
from ._doubleqlearning import DoubleQLearning
class TestDoubleQLearning(TestCase):
def setUp(self):
self.transition_d... | 3,070 | 29.107843 | 90 | py |
null | coax-main/coax/td_learning/_expectedsarsa.py | import gymnasium
import jax
import haiku as hk
from ..utils import is_stochastic
from ._base import BaseTDLearningQWithTargetPolicy
class ExpectedSarsa(BaseTDLearningQWithTargetPolicy):
r"""
TD-learning with expected-SARSA updates. The :math:`n`-step bootstrapped target is constructed
as:
.. math::... | 4,207 | 34.361345 | 100 | py |
null | coax-main/coax/td_learning/_expectedsarsa_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.q import Q
from .._core.policy import Policy
from ..utils import get_transition_batch
from ..regularizers import EntropyRegularizer
from ._expectedsarsa import ExpectedSarsa
class TestExpectedSarsa(TestCase):
de... | 4,302 | 35.159664 | 98 | py |
null | coax-main/coax/td_learning/_qlearning.py | import warnings
import haiku as hk
import chex
from gymnasium.spaces import Discrete
from ..utils import is_stochastic
from ._base import BaseTDLearningQWithTargetPolicy
class QLearning(BaseTDLearningQWithTargetPolicy):
r"""
TD-learning with Q-Learning updates.
The :math:`n`-step bootstrapped target f... | 5,556 | 36.802721 | 100 | py |
null | coax-main/coax/td_learning/_qlearning_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.q import Q
from .._core.policy import Policy
from ..utils import get_transition_batch
from ._qlearning import QLearning
class TestQLearning(TestCase):
def setUp(self):
self.transition_discrete = get_tran... | 3,016 | 28.578431 | 90 | py |
null | coax-main/coax/td_learning/_sarsa.py | import haiku as hk
from ..utils import is_stochastic
from ._base import BaseTDLearningQ
class Sarsa(BaseTDLearningQ):
r"""
TD-learning with SARSA updates. The :math:`n`-step bootstrapped target is constructed as:
.. math::
G^{(n)}_t\ =\ R^{(n)}_t + I^{(n)}_t\,q_\text{targ}(S_{t+n}, A_{t+n})
... | 2,826 | 33.901235 | 100 | py |
null | coax-main/coax/td_learning/_sarsa_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.q import Q
from .._core.policy import Policy
from ..utils import get_transition_batch
from ..regularizers import EntropyRegularizer
from ._sarsa import Sarsa
class TestSarsa(TestCase):
def setUp(self):
s... | 5,906 | 38.119205 | 98 | py |
null | coax-main/coax/td_learning/_simple_td.py | import haiku as hk
from ..utils import is_stochastic
from ._base import BaseTDLearningV
class SimpleTD(BaseTDLearningV):
r"""
TD-learning for state value functions :math:`v(s)`. The :math:`n`-step bootstrapped target is
constructed as:
.. math::
G^{(n)}_t\ =\ R^{(n)}_t + I^{(n)}_t\,v_\text... | 2,704 | 31.987805 | 99 | py |
null | coax-main/coax/td_learning/_simple_td_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.v import V
from .._core.policy import Policy
from ..utils import get_transition_batch
from ..regularizers import EntropyRegularizer
from ..value_transforms import LogTransform
from ._simple_td import SimpleTD
class T... | 6,622 | 39.384146 | 98 | py |
null | coax-main/coax/td_learning/_softclippeddoubleqlearning.py | import haiku as hk
import jax
import jax.numpy as jnp
from gymnasium.spaces import Discrete
from ..utils import (batch_to_single, is_stochastic, single_to_batch,
stack_trees)
from ._clippeddoubleqlearning import ClippedDoubleQLearning
class SoftClippedDoubleQLearning(ClippedDoubleQLearning):
... | 6,368 | 51.204918 | 99 | py |
null | coax-main/coax/td_learning/_softqlearning.py | import haiku as hk
from jax.scipy.special import logsumexp
from gymnasium.spaces import Discrete
from ..utils import is_stochastic
from ._base import BaseTDLearningQ
class SoftQLearning(BaseTDLearningQ):
r"""
TD-learning with soft Q-learning updates. The :math:`n`-step bootstrapped target is constructed
... | 3,698 | 33.570093 | 100 | py |
null | coax-main/coax/td_learning/_softqlearning_test.py | from copy import deepcopy
from optax import sgd
from .._base.test_case import TestCase
from .._core.q import Q
from .._core.policy import Policy
from ..utils import get_transition_batch
from ..regularizers import EntropyRegularizer
from ._softqlearning import SoftQLearning
class TestSoftQLearning(TestCase):
de... | 3,958 | 36.704762 | 98 | py |
null | coax-main/coax/utils/__init__.py | r"""
Utilities
=========
This is a collection of utility (helper) functions used throughout the package.
.. autosummary::
:nosignatures:
coax.utils.OrnsteinUhlenbeckNoise
coax.utils.StepwiseLinearFunction
coax.utils.SegmentTree
coax.utils.SumTree
coax.utils.MinTree
coax.utils.MaxTree
... | 6,051 | 25.086207 | 81 | py |
null | coax-main/coax/utils/_action_noise.py | import numpy as onp
__all__ = (
'OrnsteinUhlenbeckNoise',
)
class OrnsteinUhlenbeckNoise:
r"""
Add `Ornstein-Uhlenbeck <https://en.wikipedia.org/wiki/Ornstein-Uhlenbeck_process>`_ noise to
continuous actions.
.. math::
A_t\ \mapsto\ \widetilde{A}_t = A_t + X_t
As a side effect, t... | 3,119 | 27.108108 | 100 | py |
null | coax-main/coax/utils/_action_noise_test.py | import jax.numpy as jnp
from .._base.test_case import TestCase
from ._action_noise import OrnsteinUhlenbeckNoise
class TestOrnsteinUhlenbeckNoise(TestCase):
def test_overall_mean_variance(self):
noise = OrnsteinUhlenbeckNoise(random_seed=13)
x = jnp.stack([noise(0.) for _ in range(1000)])
... | 498 | 32.266667 | 55 | py |
null | coax-main/coax/utils/_array.py | import warnings
from collections import Counter
from functools import partial
import chex
import gymnasium
import jax
import jax.numpy as jnp
import numpy as onp
import haiku as hk
from scipy.linalg import pascal
__all__ = (
'StepwiseLinearFunction',
'argmax',
'argmin',
'batch_to_single',
'check_... | 34,240 | 28.774783 | 100 | py |
null | coax-main/coax/utils/_array_test.py | import gymnasium
import jax
import jax.numpy as jnp
import numpy as onp
from haiku import PRNGSequence
from .._base.test_case import TestCase
from ..proba_dists import NormalDist
from ._array import (
argmax,
check_preprocessors,
chunks_pow2,
default_preprocessor,
get_transition_batch,
tree_sam... | 5,291 | 42.377049 | 100 | py |
null | coax-main/coax/utils/_array_test_unvectorize.py | import pytest
import jax
import haiku as hk
from ._array import unvectorize
@pytest.fixture
def rngs():
return hk.PRNGSequence(42)
@pytest.fixture
def x_batch():
rng = jax.random.PRNGKey(13)
return jax.random.normal(rng, shape=(7, 11))
@pytest.fixture
def x_single():
rng = jax.random.PRNGKey(17)
... | 2,542 | 31.602564 | 93 | py |
null | coax-main/coax/utils/_dmc_gym.py | """ Adapted from https://github.com/denisyarats/dmc2gym/blob/master/dmc2gym/wrappers.py """
import numpy as onp
from dm_control import suite
from dm_env import specs
from gymnasium import spaces, Env
from gymnasium.envs.registration import register, make, registry
def make_dmc(domain, task, seed=0, max_episode_step... | 3,936 | 29.757813 | 98 | py |
null | coax-main/coax/utils/_jit.py |
from inspect import signature
import jax
__all__ = (
'JittedFunc',
'jit',
)
def jit(func, static_argnums=(), donate_argnums=()):
r"""
An alternative of :func:`jax.jit` that returns a picklable JIT-compiled function.
Note that :func:`jax.jit` produces non-picklable functions, because the JIT ... | 2,163 | 26.05 | 99 | py |
null | coax-main/coax/utils/_misc.py | import os
import time
import logging
from importlib import reload, import_module
from types import ModuleType
import jax.numpy as jnp
import numpy as onp
import pandas as pd
import lz4.frame
import cloudpickle as pickle
from PIL import Image
__all__ = (
'docstring',
'enable_logging',
'dump',
'dumps',... | 18,988 | 23.470361 | 100 | py |
null | coax-main/coax/utils/_misc_test.py | import os
import tempfile
from ..utils import jit
from ._misc import dump, dumps, load, loads
def test_dump_load():
with tempfile.TemporaryDirectory() as d:
a = [13]
b = {'a': a}
# references preserved
dump((a, b), os.path.join(d, 'ab.pkl.lz4'))
a_new, b_new = load(os.pat... | 1,377 | 21.966667 | 58 | py |
null | coax-main/coax/utils/_quantile_funcs.py | import haiku as hk
import jax
import jax.numpy as jnp
import numpy as onp
__all__ = (
'quantiles',
'quantiles_uniform',
'quantile_cos_embedding'
)
def quantiles_uniform(rng, batch_size, num_quantiles=32):
"""
Generate :code:`batch_size` quantile fractions that split the interval :math:`[0, 1]`
... | 2,764 | 27.505155 | 90 | py |
null | coax-main/coax/utils/_rolling.py | from collections import deque
class RollingAverage:
def __init__(self, n=100):
self._value = 0.
self._deque = deque(maxlen=n)
@property
def value(self):
return self._value
def update(self, observed_value):
if len(self._deque) == self._deque.maxlen:
self._v... | 992 | 25.131579 | 88 | py |
null | coax-main/coax/utils/_segment_tree.py | import numpy as onp
__all__ = (
'SegmentTree',
'SumTree',
'MinTree',
'MaxTree',
)
class SegmentTree:
r"""
A `segment tree <https://en.wikipedia.org/wiki/Segment_tree>`_ data structure that allows
for batched updating and batched partial-range (segment) reductions.
Parameters
--... | 15,361 | 33.290179 | 99 | py |
null | coax-main/coax/utils/_segment_tree_test.py | import pytest
import numpy as onp
import pandas as pd
from ._segment_tree import SumTree, MinTree
@pytest.fixture
def sum_tree():
return SumTree(capacity=14)
@pytest.fixture
def min_tree():
tr = MinTree(capacity=8)
tr.set_values(..., onp.array([13, 7, 11, 17, 19, 5, 3, 23]))
return tr
def test_su... | 4,278 | 29.564286 | 100 | py |
null | coax-main/coax/value_losses/__init__.py | r"""
Value Losses
============
.. autosummary::
:nosignatures:
coax.value_losses.mse
coax.value_losses.huber
coax.value_losses.logloss
coax.value_losses.logloss_sign
coax.value_losses.quantile_huber
----
This is a collection of loss functions that may be used for learning a value function. T... | 825 | 19.146341 | 100 | py |
null | coax-main/coax/value_losses/_losses.py | import jax
import jax.numpy as jnp
__all__ = (
'mse',
'huber',
'logloss',
'logloss_sign',
)
def mse(y_true, y_pred, w=None):
r"""
Ordinary mean-squared error loss function.
.. math::
L\ =\ \frac12(\hat{y} - y)^2
.. image:: /_static/img/mse.svg
:alt: Mean-Squared E... | 5,578 | 21.864754 | 97 | py |
null | coax-main/coax/value_transforms/__init__.py | r"""
Value Transforms
================
.. autosummary::
:nosignatures:
coax.value_transforms.ValueTransform
coax.value_transforms.LogTransform
----
This module contains some useful **value transforms**. These are functions
that can be used to rescale or warp the returns for more a more robust training
s... | 659 | 18.411765 | 79 | py |
null | coax-main/coax/value_transforms/_base.py |
class ValueTransform:
r"""
Abstract base class for value transforms. See
:class:`coax.value_transforms.LogTransform` for a specific implementation.
"""
__slots__ = ('_transform_func', '_inverse_func')
def __init__(self, transform_func, inverse_func):
self._transform_func = transform_... | 1,263 | 20.423729 | 78 | py |
null | coax-main/coax/value_transforms/_log_transform.py | import jax.numpy as jnp
from ._base import ValueTransform
class LogTransform(ValueTransform):
r"""
A simple invertible log-transform.
.. math::
x\ \mapsto\ y\ =\ \lambda\,\text{sign}(x)\,
\log\left(1+\frac{|x|}{\lambda}\right)
with inverse:
.. math::
y\ \mapsto\... | 1,385 | 24.666667 | 99 | py |
null | coax-main/coax/value_transforms/_log_transform_test.py | import jax.numpy as jnp
from .._base.test_case import TestCase
from ._log_transform import LogTransform
class TestLogTransform(TestCase):
decimal = 5
def test_inverse(self):
f = LogTransform(scale=7)
# some consistency checks
values = jnp.array([-100, -10, -1, 0, 1, 10, 100], dtype='... | 429 | 25.875 | 75 | py |
null | coax-main/coax/wrappers/__init__.py | r"""
Wrappers
========
.. autosummary::
:nosignatures:
coax.wrappers.TrainMonitor
coax.wrappers.FrameStacking
coax.wrappers.BoxActionsToReals
coax.wrappers.BoxActionsToDiscrete
coax.wrappers.MetaPolicyEnv
----
Gymnasium provides a nice modular interface to extend existing using
`environment ... | 1,366 | 25.288462 | 78 | py |
null | coax-main/coax/wrappers/_box_spaces.py | import gymnasium
import numpy as onp
from scipy.special import expit as sigmoid
from .._base.mixins import AddOrigToInfoDictMixin
__all__ = (
'BoxActionsToReals',
'BoxActionsToDiscrete',
)
class BoxActionsToReals(gymnasium.Wrapper, AddOrigToInfoDictMixin):
r"""
This wrapper decompactifies a :class... | 4,466 | 38.184211 | 100 | py |
null | coax-main/coax/wrappers/_box_spaces_test.py | from itertools import combinations
import gymnasium
import numpy as onp
from .._base.test_case import TestCase
from ._box_spaces import BoxActionsToDiscrete
class TestBoxActionsToDiscrete(TestCase):
def test_inverse(self):
num_bins = 100
env = gymnasium.make('BipedalWalker-v3')
env = Bo... | 1,259 | 29 | 70 | py |
null | coax-main/coax/wrappers/_frame_stacking.py | from collections import deque
import gymnasium
class FrameStacking(gymnasium.Wrapper):
r"""
Wrapper that does frame stacking (see `DQN paper
<https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf>`_).
This implementation is different from most implementations in that it doesn't perform the
stacking it... | 2,186 | 34.274194 | 99 | py |
null | coax-main/coax/wrappers/_meta_policy.py | import inspect
import gymnasium
class MetaPolicyEnv(gymnasium.Wrapper):
r"""
Wrap a gymnasium-style environment such that it may be used by a meta-policy,
i.e. a bandit that selects a policy (an *arm*), which is then used to
sample a lower-level action and fed the original environment. In other
... | 1,824 | 32.181818 | 81 | py |
null | coax-main/coax/wrappers/_train_monitor.py | import os
import re
import datetime
import logging
import time
from collections import deque
from typing import Mapping
import numpy as np
import lz4.frame
import cloudpickle as pickle
from gymnasium import Wrapper
from gymnasium.spaces import Discrete
from tensorboardX import SummaryWriter
from .._base.mixins import... | 12,563 | 28.84323 | 99 | py |
null | coax-main/doc/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 10,762 | 32.321981 | 100 | py |
null | coax-main/doc/create_notebooks.py | #!/usr/bin/env python3
import os
import json
import shutil
from glob import glob
from copy import deepcopy
PACKAGEDIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
nb_template = {
"cells": [
{
"cell_type": "code",
"execution_count": None,
"metadata": {... | 2,859 | 25.481481 | 85 | py |
null | coax-main/doc/versions.html | <style>
.version.matrix {
width: 100%;
}
.version.options {
column-fill: balance;
column-gap: 0;
text-align: center;
}
.version.title {
width: 25%;
padding: 10px 10px 10px 0px;
}
.version.option {
background: #E3E3E3;
padding: 10px 5px 10px 5px;
margin: 0px 1px;
}
.version.option:hover {
color: #FFF... | 4,720 | 29.458065 | 170 | html |
null | coax-main/doc/_static/css/custom.css | body a {
font-weight: bold;
}
.math {
text-align: left;
}
.eqno {
float: right;
}
.keep-us-sustainable {
/* hide ads */
display: none !important;
}
.wy-nav-top {
max-width: 900px;
}
.wy-nav-content {
background: #fff;
max-width: 900px;
}
.rst-content dl:not(.docutils) dl dt strong {
padding-... | 622 | 13.159091 | 82 | css |
null | coax-main/doc/examples/README.md | # Example Notebooks
To interact with these examples, please go to:
* https://coax.readthedocs.io
From there you can view them easily and open the scripts as Jupyter notebooks in Google Colab.
| 195 | 23.5 | 94 | md |
null | coax-main/doc/examples/sandbox.py | 0 | 0 | 0 | py | |
null | coax-main/doc/examples/atari/apex_dqn.py | import os
os.environ['JAX_PLATFORM_NAME'] = 'cpu'
# os.environ['JAX_PLATFORM_NAME'] = 'gpu'
# os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
import gymnasium
import ray
import jax
import jax.numpy as jnp
import coax
import haiku as hk
import optax
# name of this script
name, _ = os.pa... | 3,880 | 31.613445 | 116 | py |
null | coax-main/doc/examples/atari/ddpg.py | import os
# set some env vars
os.environ.setdefault('JAX_PLATFORM_NAME', 'gpu') # tell JAX to use GPU
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tell XLA to be quiet
import gymnasium
import jax
import coax
import haiku as h... | 3,394 | 29.863636 | 99 | py |
null | coax-main/doc/examples/atari/dqn.py | import os
# set some env vars
os.environ.setdefault('JAX_PLATFORM_NAME', 'gpu') # tell JAX to use GPU
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tell XLA to be quiet
import gymnasium
import jax
import coax
import haiku as h... | 2,794 | 29.380435 | 112 | py |
null | coax-main/doc/examples/atari/dqn_boltzmann.py | import os
# set some env vars
os.environ.setdefault('JAX_PLATFORM_NAME', 'gpu') # tell JAX to use GPU
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tell XLA to be quiet
import gymnasium
import jax
import coax
import haiku as h... | 2,705 | 30.103448 | 112 | py |
null | coax-main/doc/examples/atari/dqn_per.py | import os
# set some env vars
os.environ.setdefault('JAX_PLATFORM_NAME', 'gpu') # tell JAX to use GPU
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tell XLA to be quiet
import gymnasium
import jax
import coax
import haiku as h... | 3,070 | 31.670213 | 112 | py |
null | coax-main/doc/examples/atari/dqn_soft.py | import os
# set some env vars
os.environ.setdefault('JAX_PLATFORM_NAME', 'gpu') # tell JAX to use GPU
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tell XLA to be quiet
import gymnasium
import jax
import coax
import haiku as h... | 2,700 | 29.693182 | 112 | py |
null | coax-main/doc/examples/atari/dqn_type1.py | import os
# set some env vars
os.environ.setdefault('JAX_PLATFORM_NAME', 'gpu') # tell JAX to use GPU
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tell XLA to be quiet
import gymnasium
import jax
import coax
import haiku as h... | 2,736 | 29.752809 | 112 | py |
null | coax-main/doc/examples/atari/ppo.py | import os
# set some env vars
os.environ.setdefault('JAX_PLATFORM_NAME', 'gpu') # tell JAX to use GPU
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.1' # don't use all gpu mem
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tell XLA to be quiet
import gymnasium
import jax
import coax
import haiku as h... | 3,521 | 29.362069 | 94 | py |
null | coax-main/doc/examples/atari/run_all.sh | #!/bin/bash
trap "kill 0" EXIT
gio trash -f ./data
for f in $(ls ./*.py); do
python3 $f &
done
wait
| 107 | 8.818182 | 25 | sh |
null | coax-main/doc/examples/atari/experiment/dqn_sqil.py | 0 | 0 | 0 | py | |
null | coax-main/doc/examples/cartpole/a2c.py | import coax
import gymnasium
import haiku as hk
import jax
import jax.numpy as jnp
import optax
from coax.value_losses import mse
# the name of this script
name = 'a2c'
# the cart-pole MDP
env = gymnasium.make('CartPole-v0', render_mode='rgb_array')
env = coax.wrappers.TrainMonitor(env, name=name, tensorboard_dir=f"... | 2,386 | 26.436782 | 94 | py |
null | coax-main/doc/examples/cartpole/dqn.py | import coax
import gymnasium
import haiku as hk
import jax
import jax.numpy as jnp
from coax.value_losses import mse
from optax import adam
# the name of this script
name = 'dqn'
# the cart-pole MDP
env = gymnasium.make('CartPole-v0', render_mode='rgb_array')
env = coax.wrappers.TrainMonitor(env, name=name, tensorbo... | 2,219 | 25.428571 | 98 | py |
null | coax-main/doc/examples/cartpole/iqn.py | import coax
import gymnasium
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as onp
from optax import adam
# the name of this script
name = 'iqn'
# the cart-pole MDP
env = gymnasium.make('CartPole-v0', render_mode='rgb_array')
env = coax.wrappers.TrainMonitor(
env, name=name, tensorboard_dir=f"... | 3,092 | 29.029126 | 91 | py |
null | coax-main/doc/examples/cartpole/model_based.py | import coax
import gymnasium
import jax.numpy as jnp
import haiku as hk
import optax
from coax.value_losses import mse
# the name of this script
name = 'model_based'
# the cart-pole MDP
env = gymnasium.make('CartPole-v0', render_mode='rgb_array')
env = coax.wrappers.TrainMonitor(env, name=name, tensorboard_dir=f"./d... | 2,228 | 26.518519 | 97 | py |
null | coax-main/doc/examples/cartpole/run_all.sh | #!/bin/bash
trap "kill 0" EXIT
gio trash -f ./data
for f in $(ls ./*.py); do
JAX_PLATFORM_NAME=cpu python3 $f &
done
wait
| 129 | 10.818182 | 38 | sh |
null | coax-main/doc/examples/dmc/run_all.sh | #!/bin/bash
trap "kill 0" EXIT
gio trash -f ./data
for f in $(ls ./*.py); do
JAX_PLATFORM_NAME=cpu python3 $f &
done
wait
| 129 | 10.818182 | 38 | sh |
null | coax-main/doc/examples/dmc/sac.py | import os
os.environ["MUJOCO_GL"] = "egl"
import coax
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as onp
import optax
from coax.utils import make_dmc
# the name of this script
name = 'sac'
# the dm_control MDP
env = make_dmc("walker", "walk")
env = coax.wrappers.TrainMonitor(env, name=name)
... | 3,943 | 31.595041 | 95 | py |
null | coax-main/doc/examples/frozen_lake/a2c.py | import coax
import jax
import jax.numpy as jnp
import gymnasium
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func_v(S, is_training):
value = hk.Sequential((hk.Linear(1, w_init=jnp.zeros), jnp.ravel))
return value(S)
de... | 2,313 | 21.910891 | 82 | py |
null | coax-main/doc/examples/frozen_lake/ddpg.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func_pi(S, is_training):
logits = hk.Linear(env.action_space.n, w_init=jnp.zeros)
return {'logits': logits(S)}
... | 2,594 | 23.252336 | 86 | py |
null | coax-main/doc/examples/frozen_lake/double_qlearning.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
value = hk.Sequential((hk.Flatten(), hk.Linear(1, w_init=jnp.zeros), jnp.ravel))
X = j... | 1,943 | 20.6 | 90 | py |
null | coax-main/doc/examples/frozen_lake/expected_sarsa.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
value = hk.Sequential((hk.Flatten(), hk.Linear(1, w_init=jnp.zeros), jnp.ravel))
X = j... | 1,809 | 20.807229 | 84 | py |
null | coax-main/doc/examples/frozen_lake/ppo.py | import coax
import jax
import jax.numpy as jnp
import gymnasium
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func_v(S, is_training):
value = hk.Sequential((hk.Linear(1, w_init=jnp.zeros), jnp.ravel))
return value(S)
de... | 2,415 | 22.456311 | 82 | py |
null | coax-main/doc/examples/frozen_lake/qlearning.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
value = hk.Sequential((hk.Flatten(), hk.Linear(1, w_init=jnp.zeros), jnp.ravel))
X = j... | 1,807 | 20.783133 | 84 | py |
null | coax-main/doc/examples/frozen_lake/reinforce.py | import coax
import jax
import jax.numpy as jnp
import gymnasium
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func_pi(S, is_training):
logits = hk.Linear(env.action_space.n, w_init=jnp.zeros)
return {'logits': logits(S)}
... | 1,820 | 20.939759 | 77 | py |
null | coax-main/doc/examples/frozen_lake/run_all.sh | #!/bin/bash
trap "kill 0" EXIT
gio trash -f ./data
for f in $(ls ./*.py); do
JAX_PLATFORM_NAME=cpu python3 $f &
done
wait
| 129 | 10.818182 | 38 | sh |
null | coax-main/doc/examples/frozen_lake/sarsa.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
value = hk.Sequential((hk.Flatten(), hk.Linear(1, w_init=jnp.zeros), jnp.ravel))
X = j... | 1,795 | 20.638554 | 84 | py |
null | coax-main/doc/examples/frozen_lake/stochastic_double_qlearning.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
from matplotlib import pyplot as plt
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
logits = hk.Sequential((hk.Flatten(), hk.Linear(20, w... | 2,469 | 22.75 | 90 | py |
null | coax-main/doc/examples/frozen_lake/stochastic_expected_sarsa.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
from matplotlib import pyplot as plt
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
logits = hk.Sequential((hk.Flatten(), hk.Linear(20, w... | 2,335 | 23.082474 | 82 | py |
null | coax-main/doc/examples/frozen_lake/stochastic_qlearning.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
from matplotlib import pyplot as plt
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
logits = hk.Sequential((hk.Flatten(), hk.Linear(20, w... | 2,325 | 22.979381 | 82 | py |
null | coax-main/doc/examples/frozen_lake/stochastic_sarsa.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
from matplotlib import pyplot as plt
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func(S, A, is_training):
logits = hk.Sequential((hk.Flatten(), hk.Linear(20, w... | 2,321 | 22.938144 | 82 | py |
null | coax-main/doc/examples/frozen_lake/td3.py | import coax
import gymnasium
import jax
import jax.numpy as jnp
import haiku as hk
import optax
# the MDP
env = gymnasium.make('FrozenLakeNonSlippery-v0')
env = coax.wrappers.TrainMonitor(env)
def func_pi(S, is_training):
logits = hk.Linear(env.action_space.n, w_init=jnp.zeros)
return {'logits': logits(S)}
... | 3,176 | 25.256198 | 91 | py |
null | coax-main/doc/examples/linear_regression/haiku.py | import jax
import jax.numpy as jnp
import haiku as hk
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
# create our dataset
X, y = make_regression(n_features=3)
X, X_test, y, y_test = train_test_split(X, y)
# params are defined *implicitly* in haiku
def forward(X):
... | 1,062 | 20.693878 | 76 | py |
null | coax-main/doc/examples/linear_regression/jax.py | import jax
import jax.numpy as jnp
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
# create our dataset
X, y = make_regression(n_features=3)
X, X_test, y, y_test = train_test_split(X, y)
# model weights
params = {
'w': jnp.zeros(X.shape[1:]),
'b': 0.
}
def... | 797 | 18 | 65 | py |
null | coax-main/doc/examples/pendulum/ddpg.py | import gymnasium
import jax
import coax
import haiku as hk
import jax.numpy as jnp
from numpy import prod
import optax
# the name of this script
name = 'ddpg'
# the Pendulum MDP
env = gymnasium.make('Pendulum-v1', render_mode='rgb_array')
env = coax.wrappers.TrainMonitor(env, name=name, tensorboard_dir=f"./data/tens... | 2,903 | 26.923077 | 94 | py |
null | coax-main/doc/examples/pendulum/dsac.py | import gymnasium
import jax
import coax
import haiku as hk
import jax.numpy as jnp
from numpy import prod
import optax
# the name of this script
name = 'dsac'
# the Pendulum MDP
env = gymnasium.make('Pendulum-v1', render_mode='rgb_array')
env = coax.wrappers.TrainMonitor(env, name=name, tensorboard_dir=f"./data/tens... | 5,106 | 34.713287 | 95 | py |
null | coax-main/doc/examples/pendulum/ppo.py | import gymnasium
import jax
import jax.numpy as jnp
import coax
import haiku as hk
from numpy import prod
import optax
# the name of this script
name = 'ppo'
# the Pendulum MDP
env = gymnasium.make('Pendulum-v1', render_mode='rgb_array')
env = coax.wrappers.TrainMonitor(env, name=name, tensorboard_dir=f"./data/tenso... | 2,956 | 26.896226 | 97 | py |