repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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rllab | rllab-master/rllab/envs/box2d/mountain_car_env.py | import numpy as np
import pygame
from rllab.envs.box2d.parser import find_body
from rllab.core.serializable import Serializable
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
class MountainCarEnv(Box2DEnv, Serializable):
@autoargs.inher... | 1,964 | 29.703125 | 77 | py |
rllab | rllab-master/rllab/envs/box2d/box2d_env.py | import os.path as osp
import mako.lookup
import mako.template
import numpy as np
from rllab import spaces
from rllab.envs.base import Env, Step
from rllab.envs.box2d.box2d_viewer import Box2DViewer
from rllab.envs.box2d.parser.xml_box2d import world_from_xml, find_body, \
find_joint
from rllab.misc import autoar... | 13,078 | 35.229917 | 85 | py |
rllab | rllab-master/rllab/envs/box2d/double_pendulum_env.py | import numpy as np
from rllab.envs.box2d.parser import find_body
from rllab.core.serializable import Serializable
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
# http://mlg.eng.cam.ac.uk/pilco/
class DoublePendulumEnv(Box2DEnv, Serializable... | 2,093 | 32.238095 | 65 | py |
rllab | rllab-master/rllab/envs/box2d/cartpole_env.py | import numpy as np
from rllab.envs.box2d.parser import find_body
from rllab.core.serializable import Serializable
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
class CartpoleEnv(Box2DEnv, Serializable):
@autoargs.inherit(Box2DEnv.__ini... | 1,908 | 31.913793 | 70 | py |
rllab | rllab-master/rllab/envs/box2d/box2d_viewer.py | from Box2D import b2ContactListener, b2Vec2, b2DrawExtended
import pygame
from pygame import (QUIT, KEYDOWN, KEYUP, MOUSEBUTTONDOWN, MOUSEMOTION)
class PygameDraw(b2DrawExtended):
"""
This debug draw class accepts callbacks from Box2D (which specifies what to
draw) and handles all of the rendering.
I... | 9,331 | 31.975265 | 79 | py |
rllab | rllab-master/rllab/envs/box2d/cartpole_swingup_env.py | import numpy as np
import pygame
from rllab.envs.box2d.parser import find_body
from rllab.core.serializable import Serializable
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
# Tornio, Matti, and Tapani Raiko. "Variational Bayesian approach ... | 2,167 | 30.882353 | 71 | py |
rllab | rllab-master/rllab/envs/box2d/car_parking_env.py | import numpy as np
import pygame
from rllab.envs.box2d.box2d_env import Box2DEnv
from rllab.envs.box2d.parser import find_body
from rllab.core.serializable import Serializable
from rllab.envs.box2d.parser.xml_box2d import _get_name
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
class CarP... | 4,347 | 36.162393 | 97 | py |
rllab | rllab-master/rllab/envs/box2d/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/envs/box2d/parser/xml_box2d.py | # pylint: disable=no-init, too-few-public-methods, old-style-class
import xml.etree.ElementTree as ET
import Box2D
import numpy as np
from rllab.envs.box2d.parser.xml_types import XmlElem, XmlChild, XmlAttr, \
XmlChildren
from rllab.envs.box2d.parser.xml_attr_types import Tuple, Float, Choice, \
String, List... | 11,482 | 30.546703 | 89 | py |
rllab | rllab-master/rllab/envs/box2d/parser/xml_attr_types.py | # pylint: disable=no-init, too-few-public-methods, old-style-class
import numpy as np
class Type(object):
def __eq__(self, other):
return self.__class__ == other.__class__
def from_str(self, s):
raise NotImplementedError
class Float(Type):
def from_str(self, s):
return float(... | 2,807 | 21.285714 | 82 | py |
rllab | rllab-master/rllab/envs/box2d/parser/xml_types.py | # pylint: disable=no-init, too-few-public-methods, old-style-class
from types import LambdaType
def _extract_type(typ):
if isinstance(typ, LambdaType):
return typ()
else:
return typ
class AttrDecl(object):
pass
class XmlChildren(AttrDecl):
def __init__(self, tag, typ):
se... | 3,055 | 26.781818 | 79 | py |
rllab | rllab-master/rllab/envs/box2d/parser/__init__.py | from .xml_box2d import world_from_xml, find_body, find_joint
| 61 | 30 | 60 | py |
rllab | rllab-master/rllab/distributions/recurrent_diagonal_gaussian.py | import theano.tensor as TT
import numpy as np
from rllab.distributions.base import Distribution
from rllab.distributions.diagonal_gaussian import DiagonalGaussian
RecurrentDiagonalGaussian = DiagonalGaussian
| 209 | 29 | 66 | py |
rllab | rllab-master/rllab/distributions/base.py | import theano.tensor as TT
class Distribution(object):
@property
def dim(self):
raise NotImplementedError
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
"""
Compute the symbolic KL divergence of two distributions
"""
raise NotImplementedError
def kl... | 1,033 | 25.512821 | 82 | py |
rllab | rllab-master/rllab/distributions/categorical.py | import theano.tensor as TT
import numpy as np
from .base import Distribution
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
TINY = 1e-8
# def from_onehot_sym(x_var):
# ret = TT.zeros((x_var.shape[0],), x_var.dtype)
# nonzero_n, nonzero_a = TT.nonzero(x_var)[:2]
# ret = TT.set_subte... | 2,797 | 30.795455 | 110 | py |
rllab | rllab-master/rllab/distributions/recurrent_categorical.py | import theano.tensor as TT
import numpy as np
import theano
from rllab.distributions.categorical import Categorical
from rllab.distributions.base import Distribution
TINY = 1e-8
class RecurrentCategorical(Distribution):
def __init__(self, dim):
self._cat = Categorical(dim)
self._dim = dim
@p... | 2,585 | 33.026316 | 113 | py |
rllab | rllab-master/rllab/distributions/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/distributions/delta.py | from rllab.distributions.base import Distribution
class Delta(Distribution):
@property
def dim(self):
return 0
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
return None
def kl(self, old_dist_info, new_dist_info):
return None
def likelihood_ratio_sym(self, x_va... | 865 | 23.742857 | 82 | py |
rllab | rllab-master/rllab/distributions/diagonal_gaussian.py | import theano.tensor as TT
import numpy as np
from rllab.distributions.base import Distribution
class DiagonalGaussian(Distribution):
def __init__(self, dim):
self._dim = dim
@property
def dim(self):
return self._dim
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
o... | 3,610 | 36.226804 | 82 | py |
rllab | rllab-master/rllab/distributions/bernoulli.py |
from .base import Distribution
import theano.tensor as TT
import numpy as np
TINY = 1e-8
class Bernoulli(Distribution):
def __init__(self, dim):
self._dim = dim
@property
def dim(self):
return self._dim
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
old_p = old_... | 1,891 | 32.192982 | 103 | py |
rllab | rllab-master/rllab/policies/base.py | from rllab.core.parameterized import Parameterized
class Policy(Parameterized):
def __init__(self, env_spec):
Parameterized.__init__(self)
self._env_spec = env_spec
# Should be implemented by all policies
def get_action(self, observation):
raise NotImplementedError
def reset... | 2,093 | 24.536585 | 117 | py |
rllab | rllab-master/rllab/policies/uniform_control_policy.py | from rllab.core.parameterized import Parameterized
from rllab.core.serializable import Serializable
from rllab.distributions.delta import Delta
from rllab.policies.base import Policy
from rllab.misc.overrides import overrides
class UniformControlPolicy(Policy):
def __init__(
self,
env_spec... | 928 | 24.108108 | 69 | py |
rllab | rllab-master/rllab/policies/categorical_gru_policy.py | import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano.tensor as TT
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.network import GRUNetwork
from rllab.core.lasagne_layers import OpLayer
from rllab.core.serializable import Serializable
from rllab.distribu... | 6,528 | 33.544974 | 101 | py |
rllab | rllab-master/rllab/policies/categorical_mlp_policy.py | import lasagne.layers as L
import lasagne.nonlinearities as NL
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.network import MLP
from rllab.core.serializable import Serializable
from rllab.distributions.categorical import Categorical
from rllab.misc import ext
from rllab.misc.overrides import ov... | 3,158 | 35.732558 | 95 | py |
rllab | rllab-master/rllab/policies/gaussian_mlp_policy.py | import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
from rllab.core.lasagne_layers import ParamLayer
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.network import MLP
from rllab.spaces import Box
from rllab.core.serializable import Serializable
from ... | 6,192 | 37.228395 | 117 | py |
rllab | rllab-master/rllab/policies/gaussian_gru_policy.py | import lasagne.layers as L
import lasagne.nonlinearities as NL
import lasagne.init
import numpy as np
import theano.tensor as TT
from rllab.core.lasagne_layers import ParamLayer
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.network import GRUNetwork
from rllab.core.serializable import Serializa... | 5,456 | 33.10625 | 107 | py |
rllab | rllab-master/rllab/policies/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/policies/categorical_conv_policy.py | from rllab.core.lasagne_powered import LasagnePowered
import lasagne.layers as L
from rllab.core.network import ConvNetwork
from rllab.distributions.categorical import Categorical
from rllab.policies.base import StochasticPolicy
from rllab.misc import tensor_utils
from rllab.spaces.discrete import Discrete
from rllab.... | 3,623 | 33.514286 | 93 | py |
rllab | rllab-master/rllab/policies/deterministic_mlp_policy.py | import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import lasagne.init as LI
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.lasagne_layers import batch_norm
from rllab.core.serializable import Serializable
from rllab.misc import ext
from rllab.policies.base import Polic... | 2,408 | 31.554054 | 79 | py |
rllab | rllab-master/rllab/baselines/gaussian_conv_baseline.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.misc.overrides import overrides
from rllab.core.parameterized import Parameterized
from rllab.baselines.base import Baseline
from rllab.regressors.gaussian_conv_regressor import GaussianConvRegressor
class GaussianConvBaseline(Baseline, P... | 1,460 | 30.085106 | 74 | py |
rllab | rllab-master/rllab/baselines/zero_baseline.py | import numpy as np
from rllab.baselines.base import Baseline
from rllab.misc.overrides import overrides
class ZeroBaseline(Baseline):
def __init__(self, env_spec):
pass
@overrides
def get_param_values(self, **kwargs):
return None
@overrides
def set_param_values(self, val, **kwar... | 484 | 17.653846 | 46 | py |
rllab | rllab-master/rllab/baselines/base.py | from rllab.misc import autoargs
class Baseline(object):
def __init__(self, env_spec):
self._mdp_spec = env_spec
@property
def algorithm_parallelized(self):
return False
def get_param_values(self):
raise NotImplementedError
def set_param_values(self, val):
raise ... | 797 | 18.95 | 72 | py |
rllab | rllab-master/rllab/baselines/gaussian_mlp_baseline.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.core.parameterized import Parameterized
from rllab.baselines.base import Baseline
from rllab.misc.overrides import overrides
from rllab.regressors.gaussian_mlp_regressor import GaussianMLPRegressor
class GaussianMLPBaseline(Baseline, Para... | 1,508 | 30.4375 | 80 | py |
rllab | rllab-master/rllab/baselines/linear_feature_baseline.py | from rllab.baselines.base import Baseline
from rllab.misc.overrides import overrides
import numpy as np
class LinearFeatureBaseline(Baseline):
def __init__(self, env_spec, reg_coeff=1e-5):
self._coeffs = None
self._reg_coeff = reg_coeff
@overrides
def get_param_values(self, **tags):
... | 1,403 | 30.909091 | 89 | py |
rllab | rllab-master/rllab/baselines/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/algos/base.py | class Algorithm(object):
pass
class RLAlgorithm(Algorithm):
def train(self):
raise NotImplementedError
| 122 | 12.666667 | 33 | py |
rllab | rllab-master/rllab/algos/npo.py | from rllab.misc import ext
from rllab.misc.overrides import overrides
from rllab.algos.batch_polopt import BatchPolopt
import rllab.misc.logger as logger
import theano
import theano.tensor as TT
from rllab.optimizers.penalty_lbfgs_optimizer import PenaltyLbfgsOptimizer
class NPO(BatchPolopt):
"""
Natural Poli... | 4,746 | 34.691729 | 90 | py |
rllab | rllab-master/rllab/algos/vpg.py | import theano.tensor as TT
import theano
from rllab.misc import logger
from rllab.misc.overrides import overrides
from rllab.misc import ext
from rllab.algos.batch_polopt import BatchPolopt
from rllab.optimizers.first_order_optimizer import FirstOrderOptimizer
from rllab.core.serializable import Serializable
class VP... | 4,777 | 33.128571 | 90 | py |
rllab | rllab-master/rllab/algos/ddpg.py | from rllab.algos.base import RLAlgorithm
from rllab.misc.overrides import overrides
from rllab.misc import special
from rllab.misc import ext
from rllab.sampler import parallel_sampler
from rllab.plotter import plotter
from functools import partial
import rllab.misc.logger as logger
import theano.tensor as TT
import pi... | 17,620 | 37.642544 | 114 | py |
rllab | rllab-master/rllab/algos/erwr.py | from rllab.algos.vpg import VPG
from rllab.optimizers.lbfgs_optimizer import LbfgsOptimizer
from rllab.core.serializable import Serializable
class ERWR(VPG, Serializable):
"""
Episodic Reward Weighted Regression [1]_
Notes
-----
This does not implement the original RwR [2]_ that deals with "immed... | 1,374 | 37.194444 | 250 | py |
rllab | rllab-master/rllab/algos/cma_es.py | from rllab.algos.base import RLAlgorithm
import theano.tensor as TT
import numpy as np
from rllab.misc import ext
from rllab.misc.special import discount_cumsum
from rllab.sampler import parallel_sampler, stateful_pool
from rllab.sampler.utils import rollout
from rllab.core.serializable import Serializable
import rll... | 5,759 | 35.923077 | 103 | py |
rllab | rllab-master/rllab/algos/ppo.py | from rllab.optimizers.penalty_lbfgs_optimizer import PenaltyLbfgsOptimizer
from rllab.algos.npo import NPO
from rllab.core.serializable import Serializable
class PPO(NPO, Serializable):
"""
Penalized Policy Optimization.
"""
def __init__(
self,
optimizer=None,
opti... | 646 | 28.409091 | 74 | py |
rllab | rllab-master/rllab/algos/nop.py | from rllab.algos.batch_polopt import BatchPolopt
from rllab.misc.overrides import overrides
class NOP(BatchPolopt):
"""
NOP (no optimization performed) policy search algorithm
"""
def __init__(
self,
**kwargs):
super(NOP, self).__init__(**kwargs)
@overrides
de... | 519 | 19 | 59 | py |
rllab | rllab-master/rllab/algos/tnpg.py | from rllab.algos.npo import NPO
from rllab.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer
from rllab.misc import ext
class TNPG(NPO):
"""
Truncated Natural Policy Gradient.
"""
def __init__(
self,
optimizer=None,
optimizer_args=None,
... | 727 | 29.333333 | 84 | py |
rllab | rllab-master/rllab/algos/trpo.py | from rllab.algos.npo import NPO
from rllab.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer
from rllab.core.serializable import Serializable
class TRPO(NPO):
"""
Trust Region Policy Optimization
"""
def __init__(
self,
optimizer=None,
optim... | 603 | 27.761905 | 84 | py |
rllab | rllab-master/rllab/algos/util.py | import numpy as np
import time
from rllab.core.serializable import Serializable
from rllab.misc.ext import extract
def center_advantages(advantages):
return (advantages - np.mean(advantages)) / (advantages.std() + 1e-8)
def shift_advantages_to_positive(advantages):
return (advantages - np.min(advantages)) +... | 13,903 | 32.829684 | 79 | py |
rllab | rllab-master/rllab/algos/cma_es_lib.py | """Module cma implements the CMA-ES (Covariance Matrix Adaptation
Evolution Strategy).
CMA-ES is a stochastic optimizer for robust non-linear non-convex
derivative- and function-value-free numerical optimization.
This implementation can be used with Python versions >= 2.6, namely
2.6, 2.7, 3.3, 3.4.
CMA-ES searches ... | 377,327 | 41.878182 | 276 | py |
rllab | rllab-master/rllab/algos/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/algos/cem.py | from itertools import chain, zip_longest
from rllab.algos.base import RLAlgorithm
import numpy as np
from rllab.misc.special import discount_cumsum
from rllab.sampler import parallel_sampler, stateful_pool
from rllab.sampler.utils import rollout
from rllab.core.serializable import Serializable
import rllab.misc.logg... | 7,274 | 39.19337 | 108 | py |
rllab | rllab-master/rllab/algos/reps.py | import theano.tensor as TT
import theano
import scipy.optimize
from rllab.misc import logger
from rllab.misc.overrides import overrides
from rllab.misc import ext
from rllab.algos.batch_polopt import BatchPolopt
from rllab.core.serializable import Serializable
import numpy as np
from rllab.misc import tensor_utils
cl... | 12,115 | 34.323615 | 115 | py |
rllab | rllab-master/rllab/algos/batch_polopt.py | from rllab.algos.base import RLAlgorithm
from rllab.sampler import parallel_sampler
from rllab.sampler.base import BaseSampler
import rllab.misc.logger as logger
import rllab.plotter as plotter
from rllab.policies.base import Policy
class BatchSampler(BaseSampler):
def __init__(self, algo):
"""
:t... | 5,938 | 34.777108 | 111 | py |
rllab | rllab-master/rllab/spaces/box.py | from rllab.core.serializable import Serializable
from .base import Space
import numpy as np
from rllab.misc import ext
import theano
class Box(Space):
"""
A box in R^n.
I.e., each coordinate is bounded.
"""
def __init__(self, low, high, shape=None):
"""
Two kinds of valid input:
... | 2,093 | 25.846154 | 103 | py |
rllab | rllab-master/rllab/spaces/base.py | import numpy as np
class Space(object):
"""
Provides a classification state spaces and action spaces,
so you can write generic code that applies to any Environment.
E.g. to choose a random action.
"""
def sample(self, seed=0):
"""
Uniformly randomly sample a random elemnt of t... | 1,309 | 24.686275 | 85 | py |
rllab | rllab-master/rllab/spaces/discrete.py | from .base import Space
import numpy as np
from rllab.misc import special
from rllab.misc import ext
class Discrete(Space):
"""
{0,1,...,n-1}
"""
def __init__(self, n):
self._n = n
@property
def n(self):
return self._n
def sample(self):
return np.random.randint(s... | 1,835 | 21.666667 | 78 | py |
rllab | rllab-master/rllab/spaces/__init__.py | from .product import Product
from .discrete import Discrete
from .box import Box
__all__ = ["Product", "Discrete", "Box"] | 122 | 23.6 | 40 | py |
rllab | rllab-master/rllab/spaces/product.py | from rllab.spaces.base import Space
import numpy as np
from rllab.misc import ext
class Product(Space):
def __init__(self, *components):
if isinstance(components[0], (list, tuple)):
assert len(components) == 1
components = components[0]
self._components = tuple(components)... | 2,304 | 33.924242 | 97 | py |
rllab | rllab-master/rllab/mujoco_py/glfw.py | '''
Python bindings for GLFW.
'''
__author__ = 'Florian Rhiem (florian.rhiem@gmail.com)'
__copyright__ = 'Copyright (c) 2013 Florian Rhiem'
__license__ = 'MIT'
__version__ = '1.0.1'
import ctypes
import os
import glob
import sys
import subprocess
import textwrap
# Python 3 compatibility:
try:
_getcwd = os.ge... | 54,410 | 32.217949 | 120 | py |
rllab | rllab-master/rllab/mujoco_py/mjviewer.py | import ctypes
from ctypes import pointer, byref
import logging
from threading import Lock
import os
from . import mjcore, mjconstants, glfw
from .mjlib import mjlib
import numpy as np
import OpenGL.GL as gl
logger = logging.getLogger(__name__)
mjCAT_ALL = 7
def _glfw_error_callback(e, d):
logger.error('GLFW er... | 10,788 | 31.893293 | 174 | py |
rllab | rllab-master/rllab/mujoco_py/mjextra.py | def append_objects(cur, extra):
for i in range(cur.ngeom, cur.ngeom + extra.ngeom):
cur.geoms[i] = extra.geoms[i - cur.ngeom]
cur.ngeom = cur.ngeom + extra.ngeom
if cur.ngeom > cur.maxgeom:
raise ValueError("buffer limit exceeded!")
| 262 | 36.571429 | 55 | py |
rllab | rllab-master/rllab/mujoco_py/mjcore.py | from ctypes import create_string_buffer
import ctypes
from . import mjconstants as C
from .mjtypes import * # import all for backwards compatibility
from .mjlib import mjlib
class MjError(Exception):
pass
def register_license(file_path):
"""
activates mujoco with license at `file_path`
this does n... | 5,531 | 33.575 | 159 | py |
rllab | rllab-master/rllab/mujoco_py/mjlib.py | from ctypes import *
import os
from .util import *
from .mjtypes import *
osp = os.path
if sys.platform.startswith("darwin"):
libfile = osp.abspath(osp.join(osp.dirname(__file__),"../../vendor/mujoco/libmujoco131.dylib"))
elif sys.platform.startswith("linux"):
libfile = osp.abspath(osp.join(osp.dirname(__file_... | 22,701 | 54.101942 | 178 | py |
rllab | rllab-master/rllab/mujoco_py/mjtypes.py |
# AUTO GENERATED. DO NOT CHANGE!
from ctypes import *
import numpy as np
class MJCONTACT(Structure):
_fields_ = [
("dist", c_double),
("pos", c_double * 3),
("frame", c_double * 9),
("includemargin", c_double),
("friction", c_double * 5),
("solref", c_double * ... | 224,081 | 35.849531 | 187 | py |
rllab | rllab-master/rllab/mujoco_py/util.py | import ctypes, os, sys
from ctypes import *
import six
# MAXINT on Python 2, undefined on Python 3
MAXINT = 9223372036854775807
class UserString:
def __init__(self, seq):
if isinstance(seq, basestring):
self.data = seq
elif isinstance(seq, UserString):
self.data = seq.data[... | 9,058 | 38.047414 | 80 | py |
rllab | rllab-master/rllab/mujoco_py/__init__.py | from .mjviewer import MjViewer
from .mjcore import MjModel
from .mjcore import register_license
import os
from .mjconstants import *
register_license(os.path.join(os.path.dirname(__file__),
'../../vendor/mujoco/mjkey.txt'))
| 255 | 27.444444 | 63 | py |
rllab | rllab-master/rllab/mujoco_py/mjconstants.py | MOUSE_ROTATE_V = 1
MOUSE_ROTATE_H = 2
MOUSE_MOVE_V = 3
MOUSE_MOVE_H = 4
MOUSE_ZOOM = 5
mjOBJ_BODY = 1
| 103 | 12 | 18 | py |
rllab | rllab-master/rllab/misc/autoargs.py | from rllab.misc.console import colorize
import inspect
# pylint: disable=redefined-builtin
# pylint: disable=protected-access
def arg(name, type=None, help=None, nargs=None, mapper=None, choices=None,
prefix=True):
def wrap(fn):
assert fn.__name__ == '__init__'
if not hasattr(fn, '_autoarg... | 4,540 | 29.072848 | 80 | py |
rllab | rllab-master/rllab/misc/resolve.py | from pydoc import locate
import types
from rllab.misc.ext import iscanr
def classesinmodule(module):
md = module.__dict__
return [
md[c] for c in md if (
isinstance(md[c], type) and md[c].__module__ == module.__name__
)
]
def locate_with_hint(class_path, prefix_hints=[]):
... | 2,123 | 39.075472 | 124 | py |
rllab | rllab-master/rllab/misc/nb_utils.py | import os.path as osp
import numpy as np
import csv
import matplotlib.pyplot as plt
import json
import joblib
from glob import glob
import os
def plot_experiments(name_or_patterns, legend=False, post_processing=None, key='AverageReturn'):
if not isinstance(name_or_patterns, (list, tuple)):
name_or_pattern... | 6,975 | 37.32967 | 110 | py |
rllab | rllab-master/rllab/misc/special.py | import numpy as np
import scipy
import scipy.signal
import theano.tensor.nnet
import theano.tensor as TT
import theano.tensor.extra_ops
from collections import OrderedDict
def weighted_sample(weights, objects):
"""
Return a random item from objects, with the weighting defined by weights
(which must sum to... | 4,895 | 24.633508 | 103 | py |
rllab | rllab-master/rllab/misc/ext.py | from path import Path
import sys
import pickle as pickle
import random
from rllab.misc.console import colorize, Message
from collections import OrderedDict
import numpy as np
import operator
from functools import reduce
sys.setrecursionlimit(50000)
def extract(x, *keys):
if isinstance(x, (dict, lazydict)):
... | 12,215 | 30.163265 | 172 | py |
rllab | rllab-master/rllab/misc/instrument.py | import os
import re
import subprocess
import base64
import os.path as osp
import pickle as pickle
import inspect
import hashlib
import sys
from contextlib import contextmanager
import errno
from rllab.core.serializable import Serializable
from rllab import config
from rllab.misc.console import mkdir_p
from rllab.misc... | 54,609 | 38.658678 | 174 | py |
rllab | rllab-master/rllab/misc/tensor_utils.py | import operator
import numpy as np
def flatten_tensors(tensors):
if len(tensors) > 0:
return np.concatenate([np.reshape(x, [-1]) for x in tensors])
else:
return np.asarray([])
def unflatten_tensors(flattened, tensor_shapes):
tensor_sizes = list(map(np.prod, tensor_shapes))
indices =... | 4,490 | 28.741722 | 108 | py |
rllab | rllab-master/rllab/misc/tabulate.py | # -*- coding: utf-8 -*-
# Taken from John's code
"""Pretty-print tabular data."""
from collections import namedtuple
from platform import python_version_tuple
import re
if python_version_tuple()[0] < "3":
from itertools import izip_longest
from functools import partial
_none_type = type(None)
_int... | 28,995 | 33.072855 | 197 | py |
rllab | rllab-master/rllab/misc/logger.py | from enum import Enum
from rllab.misc.tabulate import tabulate
from rllab.misc.console import mkdir_p, colorize
from rllab.misc.autoargs import get_all_parameters
from contextlib import contextmanager
import numpy as np
import os
import os.path as osp
import sys
import datetime
import dateutil.tz
import csv
import job... | 10,538 | 29.197708 | 93 | py |
rllab | rllab-master/rllab/misc/mako_utils.py |
def compute_rect_vertices(fromp, to, radius):
x1, y1 = fromp
x2, y2 = to
if abs(y1 - y2) < 1e-6:
dx = 0
dy = radius
else:
dx = radius * 1.0 / (((x1 - x2) / (y1 - y2)) ** 2 + 1) ** 0.5
# equivalently dx = radius * (y2-y1).to_f / ((x2-x1)**2 + (y2-y1)**2)**0.5
dy =... | 569 | 26.142857 | 82 | py |
rllab | rllab-master/rllab/misc/viewer2d.py | import pygame
import pygame.gfxdraw
import numpy as np
class Colors(object):
black = (0, 0, 0)
white = (255, 255, 255)
blue = (0, 0, 255)
red = (255, 0, 0)
green = (0, 255, 0)
class Viewer2D(object):
def __init__(self, size=(640, 480), xlim=None, ylim=None):
pygame.init()
scre... | 4,668 | 33.330882 | 111 | py |
rllab | rllab-master/rllab/misc/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/misc/console.py | import sys
import time
import os
import errno
import shlex
import pydoc
import inspect
import collections
color2num = dict(
gray=30,
red=31,
green=32,
yellow=33,
blue=34,
magenta=35,
cyan=36,
white=37,
crimson=38
)
def colorize(string, color, bold=False, highlight=False):
attr... | 6,692 | 28.615044 | 124 | py |
rllab | rllab-master/rllab/misc/overrides.py | #
# Copyright 2015 Mikko Korpela
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | 3,547 | 32.471698 | 120 | py |
rllab | rllab-master/rllab/misc/meta.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/misc/krylov.py | import numpy as np
from rllab.misc.ext import sliced_fun
EPS = np.finfo('float64').tiny
def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10):
"""
Demmel p 312
"""
p = b.copy()
r = b.copy()
x = np.zeros_like(b)
rdotr = r.dot(r)
fmtstr = "%10i %10.3g %10.3g"
... | 5,760 | 24.604444 | 121 | py |
rllab | rllab-master/rllab/regressors/gaussian_mlp_regressor.py | import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano
import theano.tensor as TT
from rllab.core.lasagne_layers import ParamLayer
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.network import MLP
from rllab.core.serializable import Seriali... | 10,913 | 38.258993 | 119 | py |
rllab | rllab-master/rllab/regressors/categorical_mlp_regressor.py | import lasagne.layers as L
import lasagne.nonlinearities as NL
import numpy as np
import theano
import theano.tensor as TT
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.network import MLP
from rllab.core.serializable import Serializable
from rllab.distributions.categorical import Categorical
fr... | 5,876 | 34.403614 | 119 | py |
rllab | rllab-master/rllab/regressors/gaussian_conv_regressor.py | import numpy as np
import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import theano
import theano.tensor as TT
from rllab.misc.ext import compile_function
from rllab.core.lasagne_layers import ParamLayer
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.network import Conv... | 11,624 | 38.675768 | 119 | py |
rllab | rllab-master/rllab/regressors/product_regressor.py |
import numpy as np
from rllab.core.serializable import Serializable
class ProductRegressor(Serializable):
"""
A class for performing MLE regression by fitting a product distribution to the outputs. A separate regressor will
be trained for each individual input distribution.
"""
def __init__(se... | 2,022 | 32.716667 | 117 | py |
rllab | rllab-master/rllab/regressors/__init__.py | __author__ = 'dementrock'
| 26 | 12.5 | 25 | py |
rllab | rllab-master/rllab/q_functions/base.py | from rllab.core.parameterized import Parameterized
class QFunction(Parameterized):
pass
| 94 | 14.833333 | 50 | py |
rllab | rllab-master/rllab/q_functions/continuous_mlp_q_function.py | import lasagne
import lasagne.layers as L
import lasagne.nonlinearities as NL
import lasagne.init
import theano.tensor as TT
from rllab.q_functions.base import QFunction
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.lasagne_layers import batch_norm
from rllab.core.serializable import Serializabl... | 2,914 | 31.752809 | 94 | py |
rllab | rllab-master/rllab/q_functions/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/exploration_strategies/base.py | class ExplorationStrategy(object):
def get_action(self, t, observation, policy, **kwargs):
raise NotImplementedError
def reset(self):
pass
| 164 | 22.571429 | 59 | py |
rllab | rllab-master/rllab/exploration_strategies/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/exploration_strategies/ou_strategy.py | from rllab.misc.overrides import overrides
from rllab.misc.ext import AttrDict
from rllab.core.serializable import Serializable
from rllab.spaces.box import Box
from rllab.exploration_strategies.base import ExplorationStrategy
import numpy as np
import numpy.random as nr
class OUStrategy(ExplorationStrategy, Serializ... | 2,159 | 32.230769 | 113 | py |
rllab | rllab-master/rllab/exploration_strategies/gaussian_strategy.py | from rllab.core.serializable import Serializable
from rllab.spaces.box import Box
from rllab.exploration_strategies.base import ExplorationStrategy
import numpy as np
class GaussianStrategy(ExplorationStrategy, Serializable):
"""
This strategy adds Gaussian noise to the action taken by the deterministic polic... | 1,118 | 42.038462 | 110 | py |
rllab | rllab-master/rllab/optimizers/first_order_optimizer.py | from rllab.misc import ext
from rllab.misc import logger
from rllab.core.serializable import Serializable
# from rllab.algo.first_order_method import parse_update_method
from rllab.optimizers.minibatch_dataset import BatchDataset
from collections import OrderedDict
import time
import lasagne.updates
import theano
impor... | 4,720 | 33.210145 | 112 | py |
rllab | rllab-master/rllab/optimizers/penalty_lbfgs_optimizer.py | from rllab.misc.ext import compile_function, lazydict, flatten_tensor_variables
from rllab.misc import logger
from rllab.core.serializable import Serializable
import theano.tensor as TT
import theano
import numpy as np
import scipy.optimize
class PenaltyLbfgsOptimizer(Serializable):
"""
Performs constrained o... | 6,910 | 41.925466 | 120 | py |
rllab | rllab-master/rllab/optimizers/hessian_free_optimizer.py | from rllab.misc.ext import compile_function, lazydict
from rllab.core.serializable import Serializable
from rllab.optimizers.hf import hf_optimizer
import time
from rllab.optimizers.minibatch_dataset import BatchDataset
class HessianFreeOptimizer(Serializable):
"""
Performs unconstrained optimization via Hess... | 2,936 | 32.758621 | 107 | py |
rllab | rllab-master/rllab/optimizers/hf.py | # Author: Nicolas Boulanger-Lewandowski
# University of Montreal, 2012-2013
import numpy, sys
import theano
import theano.tensor as T
import pickle
import os
from rllab.misc.ext import compile_function
import collections
def gauss_newton_product(cost, p, v, s): # this computes the product Gv = J'HJv (G is the Gaus... | 15,714 | 42.896648 | 138 | py |
rllab | rllab-master/rllab/optimizers/conjugate_gradient_optimizer.py | from rllab.misc import ext
from rllab.misc import krylov
from rllab.misc import logger
from rllab.core.serializable import Serializable
import theano.tensor as TT
import theano
import itertools
import numpy as np
from rllab.misc.ext import sliced_fun
from _ast import Num
class PerlmutterHvp(Serializable):
def __... | 11,870 | 38.969697 | 119 | py |
rllab | rllab-master/rllab/optimizers/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/optimizers/minibatch_dataset.py | import numpy as np
class BatchDataset(object):
def __init__(self, inputs, batch_size, extra_inputs=None):
self._inputs = [
i for i in inputs
]
if extra_inputs is None:
extra_inputs = []
self._extra_inputs = extra_inputs
self._batch_size = batch_size... | 1,233 | 30.641026 | 78 | py |
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