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
value |
|---|---|---|---|---|---|---|
rllab | rllab-master/sandbox/rocky/tf/misc/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/samplers/batch_sampler.py | from rllab.sampler.base import BaseSampler
from rllab.sampler import parallel_sampler
from rllab.sampler.stateful_pool import singleton_pool
import tensorflow as tf
def worker_init_tf(G):
G.sess = tf.Session()
G.sess.__enter__()
def worker_init_tf_vars(G):
G.sess.run(tf.global_variables_initializer())
... | 1,376 | 31.785714 | 90 | py |
rllab | rllab-master/sandbox/rocky/tf/samplers/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/samplers/vectorized_sampler.py | import pickle
import tensorflow as tf
from rllab.sampler.base import BaseSampler
from sandbox.rocky.tf.envs.parallel_vec_env_executor import ParallelVecEnvExecutor
from sandbox.rocky.tf.envs.vec_env_executor import VecEnvExecutor
from rllab.misc import tensor_utils
import numpy as np
from rllab.sampler.stateful_pool i... | 4,537 | 40.633028 | 118 | py |
rllab | rllab-master/sandbox/rocky/tf/regressors/gaussian_mlp_regressor.py | import numpy as np
import sandbox.rocky.tf.core.layers as L
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import MLP
from sandbox.rocky.tf.misc import tensor_utils
from sandbox.rocky.tf.optimizers.lbfgs_optimizer import LbfgsOptimizer
from sandbox.rocky.tf.optimizers... | 10,577 | 40.810277 | 119 | py |
rllab | rllab-master/sandbox/rocky/tf/regressors/categorical_mlp_regressor.py |
import numpy as np
import tensorflow as tf
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import MLP
from sandbox.rocky.tf.misc import tensor_utils
from sandbox.rocky.tf.distributions.categorical import Categorical
from sandbox.rocky.tf.optimizers.penalty_lbfgs_opt... | 7,049 | 38.830508 | 119 | py |
rllab | rllab-master/sandbox/rocky/tf/regressors/bernoulli_mlp_regressor.py |
import sandbox.rocky.tf.core.layers as L
import numpy as np
import tensorflow as tf
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import MLP
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.distributions.bernoulli import Bernoulli
from sandbo... | 6,158 | 37.735849 | 117 | py |
rllab | rllab-master/sandbox/rocky/tf/regressors/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/regressors/deterministic_mlp_regressor.py |
import numpy as np
import tensorflow as tf
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import MLP
from sandbox.rocky.tf.misc import tensor_utils
from sandbox.rocky.tf.distributions.categorical import Categorical
from sandbox.rocky.tf.optimizers.penalty_lbfgs_... | 4,785 | 32.468531 | 90 | py |
rllab | rllab-master/sandbox/rocky/tf/q_functions/base.py | from sandbox.rocky.tf.core.parameterized import Parameterized
class QFunction(Parameterized):
pass
| 104 | 20 | 61 | py |
rllab | rllab-master/sandbox/rocky/tf/q_functions/continuous_mlp_q_function.py | from sandbox.rocky.tf.q_functions.base import QFunction
from rllab.core.serializable import Serializable
from rllab.misc import ext
from sandbox.rocky.tf.core.layers_powered import LayersPowered
from sandbox.rocky.tf.core.network import MLP
from sandbox.rocky.tf.core.layers import batch_norm
from sandbox.rocky.tf.dist... | 2,759 | 31.857143 | 103 | py |
rllab | rllab-master/sandbox/rocky/tf/q_functions/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/optimizers/first_order_optimizer.py |
from rllab.misc import ext
from rllab.misc import logger
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.misc import tensor_utils
# from rllab.algo.first_order_method import parse_update_method
from rllab.optimizers.minibatch_dataset import BatchDataset
from collections import OrderedDict
impo... | 4,742 | 32.878571 | 112 | py |
rllab | rllab-master/sandbox/rocky/tf/optimizers/penalty_lbfgs_optimizer.py | from sandbox.rocky.tf.misc import tensor_utils
from rllab.misc import logger
from rllab.misc import ext
from rllab.core.serializable import Serializable
import tensorflow as tf
import numpy as np
import scipy.optimize
class PenaltyLbfgsOptimizer(Serializable):
"""
Performs constrained optimization via penaliz... | 6,481 | 40.025316 | 117 | py |
rllab | rllab-master/sandbox/rocky/tf/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
# from rllab.misc.ext import flatten_tensor_variables
import itertools
import numpy as np
import tensorflow as tf
from sandbox.rocky.tf.misc import tensor_utils
from rllab.misc.ext imp... | 12,905 | 41.314754 | 155 | py |
rllab | rllab-master/sandbox/rocky/tf/optimizers/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/sandbox/rocky/tf/optimizers/lbfgs_optimizer.py |
from rllab.misc import ext
from sandbox.rocky.tf.misc import tensor_utils
from rllab.core.serializable import Serializable
import tensorflow as tf
import scipy.optimize
import time
class LbfgsOptimizer(Serializable):
"""
Performs unconstrained optimization via L-BFGS.
"""
def __init__(self, name, m... | 2,938 | 32.397727 | 116 | py |
rllab | rllab-master/scripts/sync_s3.py | import sys
sys.path.append('.')
from rllab import config
import os
import argparse
import ast
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('folder', type=str, default=None, nargs='?')
parser.add_argument('--dry', action='store_true', default=False)
parser.add_argume... | 1,147 | 37.266667 | 122 | py |
rllab | rllab-master/scripts/sim_env.py | import argparse
import sys
import time
import numpy as np
import pygame
from rllab.envs.base import Env
# from rllab.env.base import MDP
from rllab.misc.resolve import load_class
def sample_action(lb, ub):
Du = len(lb)
if np.any(np.isinf(lb)) or np.any(np.isinf(ub)):
raise ValueError('Cannot sample ... | 4,738 | 32.85 | 111 | py |
rllab | rllab-master/scripts/resume_training.py |
from rllab.sampler.utils import rollout
from rllab.algos.batch_polopt import BatchPolopt
import argparse
import joblib
import uuid
import os
import random
import numpy as np
import json
import subprocess
from rllab.misc import logger
from rllab.misc.instrument import to_local_command
filename = str(uuid.uuid4())
i... | 1,902 | 30.716667 | 83 | py |
rllab | rllab-master/scripts/sim_policy.py | import argparse
import joblib
import tensorflow as tf
from rllab.misc.console import query_yes_no
from rllab.sampler.utils import rollout
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('file', type=str,
help='path to the snapshot file')
parser.a... | 1,062 | 31.212121 | 77 | py |
rllab | rllab-master/scripts/run_experiment_lite.py | import sys
sys.path.append(".")
from rllab.misc.ext import is_iterable, set_seed
from rllab.misc.instrument import concretize
from rllab import config
import rllab.misc.logger as logger
import argparse
import os.path as osp
import datetime
import dateutil.tz
import ast
import uuid
import pickle as pickle
import base6... | 5,603 | 39.608696 | 109 | py |
rllab | rllab-master/scripts/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/scripts/submit_gym.py |
import argparse
import os
import os.path as osp
import gym
from rllab.viskit.core import load_params
if __name__ == "__main__":
# rl_gym.api_key = 'g8JOpnNVmcjMShBiFtyji2VWX3P2uCzc'
if 'OPENAI_GYM_API_KEY' not in os.environ:
raise ValueError("OpenAi Gym API key not configured. Please register an acco... | 907 | 38.478261 | 118 | py |
rllab | rllab-master/scripts/setup_ec2_for_rllab.py | import boto3
import re
import sys
import json
import botocore
import os
from rllab.misc import console
from rllab import config
from string import Template
ACCESS_KEY = os.environ["AWS_ACCESS_KEY"]
ACCESS_SECRET = os.environ["AWS_ACCESS_SECRET"]
S3_BUCKET_NAME = os.environ["RLLAB_S3_BUCKET"]
ALL_REGION_AWS_SECURITY_G... | 11,283 | 30.431755 | 118 | py |
rllab | rllab-master/tests/test_spaces.py |
from rllab.spaces import Product, Discrete, Box
import numpy as np
def test_product_space():
_ = Product([Discrete(3), Discrete(2)])
product_space = Product(Discrete(3), Discrete(2))
sample = product_space.sample()
assert product_space.contains(sample)
def test_product_space_unflatten_n():
spac... | 996 | 34.607143 | 88 | py |
rllab | rllab-master/tests/test_serializable.py | import tensorflow as tf
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.core.parameterized import Parameterized, suppress_params_loading
class Simple(Parameterized, Serializable):
def __init__(self, name):
Serializable.quick_init(self, locals())
with tf.variable_scope(name)... | 1,192 | 28.097561 | 86 | py |
rllab | rllab-master/tests/test_instrument.py |
from rllab.misc import instrument
from nose2.tools import such
class TestClass(object):
@property
def arr(self):
return [1, 2, 3]
@property
def compound_arr(self):
return [dict(a=1)]
with such.A("instrument") as it:
@it.should
def test_concretize():
it.assertEqual... | 2,013 | 26.589041 | 87 | py |
rllab | rllab-master/tests/test_networks.py | def test_gru_network():
from rllab.core.network import GRUNetwork
import lasagne.layers as L
from rllab.misc import ext
import numpy as np
network = GRUNetwork(
input_shape=(2, 3),
output_dim=5,
hidden_dim=4,
)
f_output = ext.compile_function(
inputs=[network.... | 464 | 28.0625 | 62 | py |
rllab | rllab-master/tests/test_algos.py | import os
from rllab.algos.cem import CEM
from rllab.algos.cma_es import CMAES
from rllab.algos.erwr import ERWR
os.environ['THEANO_FLAGS'] = 'device=cpu,mode=FAST_COMPILE,optimizer=None'
from rllab.algos.vpg import VPG
from rllab.algos.tnpg import TNPG
from rllab.algos.ppo import PPO
from rllab.algos.trpo import TR... | 3,227 | 27.821429 | 99 | py |
rllab | rllab-master/tests/test_sampler.py |
import numpy as np
def test_truncate_paths():
from rllab.sampler.parallel_sampler import truncate_paths
paths = [
dict(
observations=np.zeros((100, 1)),
actions=np.zeros((100, 1)),
rewards=np.zeros(100),
env_infos=dict(),
agent_infos=dict(... | 880 | 25.69697 | 61 | py |
rllab | rllab-master/tests/test_stateful_pool.py |
def _worker_collect_once(_):
return 'a', 1
def test_stateful_pool():
from rllab.sampler import stateful_pool
stateful_pool.singleton_pool.initialize(n_parallel=3)
results = stateful_pool.singleton_pool.run_collect(_worker_collect_once, 3, show_prog_bar=False)
assert tuple(results) == ('a', 'a'... | 601 | 27.666667 | 100 | py |
rllab | rllab-master/tests/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/tests/test_baselines.py | import os
os.environ['THEANO_FLAGS'] = 'mode=FAST_COMPILE,optimizer=None'
from rllab.algos.vpg import VPG
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.baselines.zero_baseline import ZeroBaseline
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.baselines.gaussian... | 868 | 31.185185 | 73 | py |
rllab | rllab-master/tests/envs/test_envs.py | import numpy as np
from nose2 import tools
from rllab.envs.box2d.car_parking_env import CarParkingEnv
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.box2d.cartpole_swingup_env import CartpoleSwingupEnv
from rllab.envs.box2d.double_pendulum_env import DoublePendulumEnv
from rllab.envs.box2d.mount... | 3,052 | 28.640777 | 88 | py |
rllab | rllab-master/tests/envs/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/tests/envs/test_maze_env.py | import math
from rllab.envs.mujoco.maze.maze_env_utils import line_intersect, ray_segment_intersect
def test_line_intersect():
assert line_intersect((0, 0), (0, 1), (0, 0), (1, 0))[:2] == (0, 0)
assert line_intersect((0, 0), (0, 1), (0, 0), (0, 1))[2] == 0
assert ray_segment_intersect(ray=((0, 0), 0), se... | 446 | 39.636364 | 90 | py |
rllab | rllab-master/tests/algos/test_trpo.py |
from rllab.envs.base import Env, Step
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.baselines.zero_baseline import ZeroBaseline
from rllab.algos.trpo import TRPO
from rllab.spaces.box import Box
import lasagne.nonlinearities
import numpy as np
import theano.tensor as TT
class DummyEnv(... | 1,651 | 26.081967 | 98 | py |
rllab | rllab-master/tests/algos/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/tests/regression_tests/test_issue_3.py |
from nose2.tools import such
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.algos.trpo import TRPO
from rllab.baselines.zero_baseline import ZeroBaseline
with such.A("Issue #3") as it:
@it.should("be fixed")
def test_issue_3(... | 854 | 25.71875 | 119 | py |
rllab | rllab-master/tests/regression_tests/__init__.py | 1 | 0 | 0 | py | |
rllab | rllab-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# rllab documentation build configuration file, created by
# sphinx-quickstart on Mon Feb 15 20:07:12 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All... | 9,550 | 31.050336 | 79 | py |
rllab | rllab-master/rllab/config_personal_template.py | import os
USE_GPU = False
DOCKER_IMAGE = "dementrock/rllab3-shared"
KUBE_PREFIX = "template_"
DOCKER_LOG_DIR = "/tmp/expt"
AWS_IMAGE_ID = "ami-67c5d00d"
if USE_GPU:
AWS_INSTANCE_TYPE = "g2.2xlarge"
else:
AWS_INSTANCE_TYPE = "c4.2xlarge"
AWS_KEY_NAME = "research_virginia"
AWS_SPOT = True
AWS_SPOT_PRICE ... | 919 | 19 | 78 | py |
rllab | rllab-master/rllab/config.py | import os.path as osp
import os
PROJECT_PATH = osp.abspath(osp.join(osp.dirname(__file__), '..'))
LOG_DIR = PROJECT_PATH + "/data"
USE_TF = False
DOCKER_IMAGE = "DOCKER_IMAGE"
DOCKERFILE_PATH = "/path/to/Dockerfile"
KUBE_PREFIX = "rllab_"
DOCKER_LOG_DIR = "/tmp/expt"
POD_DIR = PROJECT_PATH + "/.pods"
AWS_S3_PA... | 1,899 | 20.111111 | 121 | py |
rllab | rllab-master/rllab/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/sampler/base.py |
import numpy as np
from rllab.misc import special
from rllab.misc import tensor_utils
from rllab.algos import util
import rllab.misc.logger as logger
class Sampler(object):
def start_worker(self):
"""
Initialize the sampler, e.g. launching parallel workers if necessary.
"""
raise... | 7,078 | 37.68306 | 115 | py |
rllab | rllab-master/rllab/sampler/utils.py | import numpy as np
from rllab.misc import tensor_utils
import time
def rollout(env, agent, max_path_length=np.inf, animated=False, speedup=1,
always_return_paths=False):
observations = []
actions = []
rewards = []
agent_infos = []
env_infos = []
o = env.reset()
agent.reset()
... | 1,314 | 28.886364 | 74 | py |
rllab | rllab-master/rllab/sampler/parallel_sampler.py | from rllab.sampler.utils import rollout
from rllab.sampler.stateful_pool import singleton_pool, SharedGlobal
from rllab.misc import ext
from rllab.misc import logger
from rllab.misc import tensor_utils
import pickle
import numpy as np
def _worker_init(G, id):
if singleton_pool.n_parallel > 1:
import os
... | 5,045 | 31.346154 | 115 | py |
rllab | rllab-master/rllab/sampler/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/sampler/stateful_pool.py |
from joblib.pool import MemmapingPool
import multiprocessing as mp
from rllab.misc import logger
import pyprind
import time
import traceback
import sys
class ProgBarCounter(object):
def __init__(self, total_count):
self.total_count = total_count
self.max_progress = 1000000
self.cur_progr... | 6,578 | 32.060302 | 109 | py |
rllab | rllab-master/rllab/core/network.py |
import lasagne.layers as L
import lasagne.nonlinearities as LN
import lasagne.init as LI
import theano.tensor as TT
import theano
from rllab.misc import ext
from rllab.core.lasagne_layers import OpLayer
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.serializable import Serializable
import nump... | 12,163 | 34.054755 | 103 | py |
rllab | rllab-master/rllab/core/lasagne_powered.py | from rllab.core.parameterized import Parameterized
from rllab.misc.overrides import overrides
import lasagne.layers as L
class LasagnePowered(Parameterized):
def __init__(self, output_layers):
self._output_layers = output_layers
super(LasagnePowered, self).__init__()
@property
def output_... | 654 | 30.190476 | 102 | py |
rllab | rllab-master/rllab/core/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/core/serializable.py | import inspect
import sys
class Serializable(object):
def __init__(self, *args, **kwargs):
self.__args = args
self.__kwargs = kwargs
def quick_init(self, locals_):
if getattr(self, "_serializable_initialized", False):
return
if sys.version_info >= (3, 0):
... | 2,077 | 30.484848 | 78 | py |
rllab | rllab-master/rllab/core/lasagne_layers.py | # encoding: utf-8
import lasagne.layers as L
import lasagne
import theano
import theano.tensor as TT
class ParamLayer(L.Layer):
def __init__(self, incoming, num_units, param=lasagne.init.Constant(0.),
trainable=True, **kwargs):
super(ParamLayer, self).__init__(incoming, **kwargs)
... | 13,436 | 43.939799 | 95 | py |
rllab | rllab-master/rllab/core/parameterized.py | from contextlib import contextmanager
from rllab.core.serializable import Serializable
from rllab.misc.tensor_utils import flatten_tensors, unflatten_tensors
load_params = True
@contextmanager
def suppress_params_loading():
global load_params
load_params = False
yield
load_params = True
class Parame... | 3,042 | 34.383721 | 103 | py |
rllab | rllab-master/rllab/core/lasagne_helpers.py | from lasagne.layers import get_all_layers
from lasagne import utils
def get_full_output(layer_or_layers, inputs=None, **kwargs):
"""
Computes the output of the network at one or more given layers.
Optionally, you can define the input(s) to propagate through the network
instead of using the input varia... | 4,719 | 46.676768 | 81 | py |
rllab | rllab-master/rllab/envs/base.py | from .env_spec import EnvSpec
import collections
from cached_property import cached_property
class Env(object):
def step(self, action):
"""
Run one timestep of the environment's dynamics. When end of episode
is reached, reset() should be called to reset the environment's internal state.
... | 2,624 | 24.990099 | 96 | py |
rllab | rllab-master/rllab/envs/grid_world_env.py | import numpy as np
from .base import Env
from rllab.spaces import Discrete
from rllab.envs.base import Step
from rllab.core.serializable import Serializable
MAPS = {
"chain": [
"GFFFFFFFFFFFFFSFFFFFFFFFFFFFG"
],
"4x4_safe": [
"SFFF",
"FWFW",
"FFFW",
"WFFG"
],
... | 4,094 | 26.119205 | 115 | py |
rllab | rllab-master/rllab/envs/normalized_env.py | import numpy as np
from rllab import spaces
from rllab.core.serializable import Serializable
from rllab.envs.proxy_env import ProxyEnv
from rllab.spaces.box import Box
from rllab.misc.overrides import overrides
from rllab.envs.base import Step
class NormalizedEnv(ProxyEnv, Serializable):
def __init__(
... | 3,871 | 36.230769 | 122 | py |
rllab | rllab-master/rllab/envs/proxy_env.py | from rllab.core.serializable import Serializable
from .base import Env
class ProxyEnv(Env, Serializable):
def __init__(self, wrapped_env):
Serializable.quick_init(self, locals())
self._wrapped_env = wrapped_env
@property
def wrapped_env(self):
return self._wrapped_env
def res... | 1,191 | 24.913043 | 65 | py |
rllab | rllab-master/rllab/envs/noisy_env.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.envs.proxy_env import ProxyEnv
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
class NoisyObservationEnv(ProxyEnv, Serializable):
@autoargs.arg('obs_noise', type=float,
... | 2,575 | 31.2 | 79 | py |
rllab | rllab-master/rllab/envs/gym_env.py | import gym
import gym.wrappers
import gym.envs
import gym.spaces
import traceback
import logging
try:
from gym.wrappers.monitoring import logger as monitor_logger
monitor_logger.setLevel(logging.WARNING)
except Exception as e:
traceback.print_exc()
import os
import os.path as osp
from rllab.envs.base imp... | 4,134 | 29.858209 | 109 | py |
rllab | rllab-master/rllab/envs/occlusion_env.py | import numpy as np
from cached_property import cached_property
from rllab import spaces
from rllab.core.serializable import Serializable
from rllab.envs.proxy_env import ProxyEnv
from rllab.misc.overrides import overrides
from rllab.envs.base import Step
from rllab.envs.mujoco.mujoco_env import MujocoEnv
BIG = 1e6
c... | 2,572 | 33.77027 | 136 | py |
rllab | rllab-master/rllab/envs/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/envs/sliding_mem_env.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.envs.proxy_env import ProxyEnv
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
from rllab.spaces import Box
class SlidingMemEnv(ProxyEnv, Serializable):
def __init__(
... | 1,475 | 24.894737 | 78 | py |
rllab | rllab-master/rllab/envs/identification_env.py | from rllab.core.serializable import Serializable
from rllab.envs.proxy_env import ProxyEnv
from rllab.misc.overrides import overrides
class IdentificationEnv(ProxyEnv, Serializable):
def __init__(self, mdp_cls, mdp_args):
Serializable.quick_init(self, locals())
self.mdp_cls = mdp_cls
self... | 829 | 29.740741 | 57 | py |
rllab | rllab-master/rllab/envs/env_spec.py | from rllab.core.serializable import Serializable
from rllab.spaces.base import Space
class EnvSpec(Serializable):
def __init__(
self,
observation_space,
action_space):
"""
:type observation_space: Space
:type action_space: Space
"""
Seri... | 614 | 22.653846 | 51 | py |
rllab | rllab-master/rllab/envs/mujoco/simple_humanoid_env.py | from rllab.envs.base import Step
from .mujoco_env import MujocoEnv
import numpy as np
from rllab.core.serializable import Serializable
from rllab.misc.overrides import overrides
from rllab.misc import logger
from rllab.misc import autoargs
class SimpleHumanoidEnv(MujocoEnv, Serializable):
FILE = 'simple_humanoid... | 3,040 | 34.776471 | 74 | py |
rllab | rllab-master/rllab/envs/mujoco/ant_env.py | from rllab.envs.mujoco.mujoco_env import MujocoEnv
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.misc.overrides import overrides
from rllab.misc import logger
from rllab.envs.mujoco.mujoco_env import q_mult, q_inv
import numpy as np
import math
class AntEnv(MujocoEnv, S... | 2,342 | 33.970149 | 108 | py |
rllab | rllab-master/rllab/envs/mujoco/inverted_double_pendulum_env.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.envs.mujoco.mujoco_env import MujocoEnv
from rllab.misc import autoargs
from rllab.misc.overrides import overrides
class InvertedDoublePendulumEnv(MujocoEnv, Serializable):
FILE = 'inverted_double_pend... | 2,077 | 35.45614 | 77 | py |
rllab | rllab-master/rllab/envs/mujoco/mujoco_env.py | import numpy as np
import os.path as osp
from cached_property import cached_property
from rllab import spaces
from rllab.envs.base import Env
from rllab.misc.overrides import overrides
from rllab.mujoco_py import MjModel, MjViewer
from rllab.misc import autoargs
from rllab.misc import logger
import theano
import tempf... | 8,368 | 33.020325 | 89 | py |
rllab | rllab-master/rllab/envs/mujoco/swimmer3d_env.py | from .swimmer_env import SwimmerEnv
class Swimmer3DEnv(SwimmerEnv):
FILE = 'swimmer3d.xml' | 95 | 23 | 35 | py |
rllab | rllab-master/rllab/envs/mujoco/half_cheetah_env.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.envs.mujoco.mujoco_env import MujocoEnv
from rllab.misc import logger
from rllab.misc.overrides import overrides
def smooth_abs(x, param):
return np.sqrt(np.square(x) + np.square(param)) - param
clas... | 1,909 | 31.931034 | 71 | py |
rllab | rllab-master/rllab/envs/mujoco/swimmer_env.py | from rllab.envs.base import Step
from rllab.misc.overrides import overrides
from .mujoco_env import MujocoEnv
import numpy as np
from rllab.core.serializable import Serializable
from rllab.misc import logger
from rllab.misc import autoargs
class SwimmerEnv(MujocoEnv, Serializable):
FILE = 'swimmer.xml'
ORI_I... | 2,213 | 34.142857 | 75 | py |
rllab | rllab-master/rllab/envs/mujoco/hopper_env.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.envs.mujoco.mujoco_env import MujocoEnv
from rllab.misc import autoargs
from rllab.misc import logger
from rllab.misc.overrides import overrides
# states: [
# 0: z-coord,
# 1: x-coord (forward distance),
#... | 2,412 | 32.054795 | 76 | py |
rllab | rllab-master/rllab/envs/mujoco/point_env.py | from rllab.envs.base import Step
from .mujoco_env import MujocoEnv
from rllab.core.serializable import Serializable
from rllab.misc.overrides import overrides
import numpy as np
import math
from rllab.mujoco_py import glfw
class PointEnv(MujocoEnv, Serializable):
"""
Use Left, Right, Up, Down, A (steer left)... | 1,815 | 28.290323 | 62 | py |
rllab | rllab-master/rllab/envs/mujoco/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/envs/mujoco/humanoid_env.py | from .simple_humanoid_env import SimpleHumanoidEnv
# Taken from Wojciech's code
class HumanoidEnv(SimpleHumanoidEnv):
FILE = 'humanoid.xml'
| 147 | 17.5 | 50 | py |
rllab | rllab-master/rllab/envs/mujoco/walker2d_env.py | import numpy as np
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.envs.mujoco.mujoco_env import MujocoEnv
from rllab.misc import autoargs
from rllab.misc import logger
from rllab.misc.overrides import overrides
def smooth_abs(x, param):
return np.sqrt(np.square(x) + ... | 2,058 | 32.209677 | 71 | py |
rllab | rllab-master/rllab/envs/mujoco/gather/point_gather_env.py | from rllab.envs.mujoco.gather.gather_env import GatherEnv
from rllab.envs.mujoco.point_env import PointEnv
class PointGatherEnv(GatherEnv):
MODEL_CLASS = PointEnv
ORI_IND = 2
| 186 | 19.777778 | 57 | py |
rllab | rllab-master/rllab/envs/mujoco/gather/swimmer_gather_env.py | from rllab.envs.mujoco.gather.gather_env import GatherEnv
from rllab.envs.mujoco.swimmer_env import SwimmerEnv
class SwimmerGatherEnv(GatherEnv):
MODEL_CLASS = SwimmerEnv
ORI_IND = 2
| 194 | 20.666667 | 57 | py |
rllab | rllab-master/rllab/envs/mujoco/gather/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/envs/mujoco/gather/gather_env.py | import math
import os.path as osp
import tempfile
import xml.etree.ElementTree as ET
from ctypes import byref
import numpy as np
from rllab.misc import logger
from rllab import spaces
from rllab.core.serializable import Serializable
from rllab.envs.proxy_env import ProxyEnv
from rllab.envs.base import Step
from rllab.... | 16,731 | 37.731481 | 118 | py |
rllab | rllab-master/rllab/envs/mujoco/gather/embedded_viewer.py | from rllab.mujoco_py import glfw, mjcore
import rllab.mujoco_py.mjconstants as C
from rllab.mujoco_py.mjlib import mjlib
from ctypes import byref
import ctypes
from threading import Lock
mjCAT_ALL = 7
class EmbeddedViewer(object):
def __init__(self):
self.last_render_time = 0
self.objects = mjco... | 6,754 | 30.713615 | 79 | py |
rllab | rllab-master/rllab/envs/mujoco/gather/ant_gather_env.py | from rllab.envs.mujoco.gather.gather_env import GatherEnv
from rllab.envs.mujoco.ant_env import AntEnv
class AntGatherEnv(GatherEnv):
MODEL_CLASS = AntEnv
ORI_IND = 6
| 178 | 18.888889 | 57 | py |
rllab | rllab-master/rllab/envs/mujoco/maze/point_maze_env.py | from rllab.envs.mujoco.maze.maze_env import MazeEnv
from rllab.envs.mujoco.point_env import PointEnv
class PointMazeEnv(MazeEnv):
MODEL_CLASS = PointEnv
ORI_IND = 2
MAZE_HEIGHT = 2
MAZE_SIZE_SCALING = 3.0
MANUAL_COLLISION = True
| 254 | 17.214286 | 51 | py |
rllab | rllab-master/rllab/envs/mujoco/maze/maze_env.py | import os.path as osp
import tempfile
import xml.etree.ElementTree as ET
import math
import numpy as np
from rllab import spaces
from rllab.envs.base import Step
from rllab.envs.proxy_env import ProxyEnv
from rllab.envs.mujoco.maze.maze_env_utils import construct_maze
from rllab.envs.mujoco.mujoco_env import MODEL_DIR... | 13,398 | 39.975535 | 120 | py |
rllab | rllab-master/rllab/envs/mujoco/maze/maze_env_utils.py | from rllab.misc import logger
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import os.path as osp
import numpy as np
import math
def line_intersect(pt1, pt2, ptA, ptB):
"""
Taken from https://www.cs.hmc.edu/ACM/lectures/intersections.html
this returns the intersection of Line(pt... | 12,779 | 35.618911 | 116 | py |
rllab | rllab-master/rllab/envs/mujoco/maze/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/envs/mujoco/maze/swimmer_maze_env.py | from rllab.envs.mujoco.maze.maze_env import MazeEnv
from rllab.envs.mujoco.swimmer_env import SwimmerEnv
class SwimmerMazeEnv(MazeEnv):
MODEL_CLASS = SwimmerEnv
ORI_IND = 2
MAZE_HEIGHT = 0.5
MAZE_SIZE_SCALING = 4
MAZE_MAKE_CONTACTS = True
| 264 | 17.928571 | 52 | py |
rllab | rllab-master/rllab/envs/mujoco/maze/ant_maze_env.py | from rllab.envs.mujoco.maze.maze_env import MazeEnv
from rllab.envs.mujoco.ant_env import AntEnv
class AntMazeEnv(MazeEnv):
MODEL_CLASS = AntEnv
ORI_IND = 6
MAZE_HEIGHT = 2
MAZE_SIZE_SCALING = 3.0
| 218 | 15.846154 | 51 | py |
rllab | rllab-master/rllab/envs/mujoco/hill/walker2d_hill_env.py | import numpy as np
from rllab.envs.mujoco.hill.hill_env import HillEnv
from rllab.envs.mujoco.walker2d_env import Walker2DEnv
from rllab.misc.overrides import overrides
import rllab.envs.mujoco.hill.terrain as terrain
from rllab.spaces import Box
class Walker2DHillEnv(HillEnv):
MODEL_CLASS = Walker2DEnv
... | 523 | 31.75 | 95 | py |
rllab | rllab-master/rllab/envs/mujoco/hill/swimmer3d_hill_env.py | import numpy as np
from rllab.envs.mujoco.hill.hill_env import HillEnv
from rllab.envs.mujoco.swimmer3d_env import Swimmer3DEnv
from rllab.misc.overrides import overrides
import rllab.envs.mujoco.hill.terrain as terrain
from rllab.spaces import Box
class Swimmer3DHillEnv(HillEnv):
MODEL_CLASS = Swimmer3DEnv
... | 526 | 31.9375 | 94 | py |
rllab | rllab-master/rllab/envs/mujoco/hill/hopper_hill_env.py | import numpy as np
from rllab.envs.mujoco.hill.hill_env import HillEnv
from rllab.envs.mujoco.hopper_env import HopperEnv
from rllab.misc.overrides import overrides
import rllab.envs.mujoco.hill.terrain as terrain
from rllab.spaces import Box
class HopperHillEnv(HillEnv):
MODEL_CLASS = HopperEnv
@overri... | 515 | 31.25 | 95 | py |
rllab | rllab-master/rllab/envs/mujoco/hill/terrain.py | from scipy.stats import multivariate_normal
from scipy.signal import convolve2d
import matplotlib
try:
matplotlib.pyplot.figure()
matplotlib.pyplot.close()
except Exception:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import os
# the colormap should assign light colors to low ... | 3,671 | 35.72 | 125 | py |
rllab | rllab-master/rllab/envs/mujoco/hill/half_cheetah_hill_env.py | import numpy as np
from rllab.envs.mujoco.hill.hill_env import HillEnv
from rllab.envs.mujoco.half_cheetah_env import HalfCheetahEnv
from rllab.misc.overrides import overrides
import rllab.envs.mujoco.hill.terrain as terrain
from rllab.spaces import Box
class HalfCheetahHillEnv(HillEnv):
MODEL_CLASS = HalfCheeta... | 535 | 32.5 | 94 | py |
rllab | rllab-master/rllab/envs/mujoco/hill/hill_env.py | import tempfile
import os
import time
import mako.template
import mako.lookup
from rllab.envs.proxy_env import ProxyEnv
from rllab.core.serializable import Serializable
import rllab.envs.mujoco.mujoco_env as mujoco_env
import rllab.envs.mujoco.hill.terrain as terrain
from rllab.misc import logger
MODEL_DIR = mujoco_... | 4,488 | 39.809091 | 128 | py |
rllab | rllab-master/rllab/envs/mujoco/hill/__init__.py | 0 | 0 | 0 | py | |
rllab | rllab-master/rllab/envs/mujoco/hill/ant_hill_env.py | import numpy as np
from rllab.envs.mujoco.hill.hill_env import HillEnv
from rllab.envs.mujoco.ant_env import AntEnv
from rllab.misc.overrides import overrides
import rllab.envs.mujoco.hill.terrain as terrain
from rllab.spaces import Box
class AntHillEnv(HillEnv):
MODEL_CLASS = AntEnv
@overrides
def ... | 501 | 30.375 | 93 | py |
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