code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
import argparse
from gym.spaces import Discrete
import os
import random
from ray import tune
from ray.rllib.agents.pg import PGTrainer, PGTFPolicy, PGTorchPolicy
from ray.rllib.agents.registry import get_trainer_class
from ray.rllib.examples.env.rock_paper_scissors import RockPaperScissors
from ray.rllib.examples.poli... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/rock_paper_scissors_multiagent.py | 0.75274 | 0.265822 | rock_paper_scissors_multiagent.py | pypi |
from gym.spaces import Discrete, Tuple
from ray.rllib.models.tf.misc import normc_initializer
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.torch.misc import normc_initializer as normc_init_torch
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 impor... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/autoregressive_action_model.py | 0.944651 | 0.374562 | autoregressive_action_model.py | pypi |
from gym.spaces import Box
from ray.rllib.agents.dqn.distributional_q_tf_model import \
DistributionalQTFModel
from ray.rllib.agents.dqn.dqn_torch_model import \
DQNTorchModel
from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
f... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/parametric_actions_model.py | 0.950709 | 0.425187 | parametric_actions_model.py | pypi |
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.tf.fcnet import FullyConnectedNetwork as TFFCNet
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFCNet
from ray.rllib.utils.framework import try_import_tf, try_... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/simple_rpg_model.py | 0.954116 | 0.330012 | simple_rpg_model.py | pypi |
import numpy as np
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_im... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/shared_weights_model.py | 0.904745 | 0.317373 | shared_weights_model.py | pypi |
import random
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_tf
tf1, tf, tfv = try_import_tf()
class EagerM... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/eager_model.py | 0.89546 | 0.361813 | eager_model.py | pypi |
from ray.rllib.models.tf.tf_action_dist import Categorical, ActionDistribution
from ray.rllib.models.torch.torch_action_dist import TorchCategorical, \
TorchDistributionWrapper
from ray.rllib.utils.framework import try_import_tf, try_import_torch
tf1, tf, tfv = try_import_tf()
torch, nn = try_import_torch()
clas... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/autoregressive_action_dist.py | 0.943295 | 0.479747 | autoregressive_action_dist.py | pypi |
import numpy as np
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.recurrent_net import RecurrentNetwork
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.recurrent_net import RecurrentNetwork as TorchRNN
from ray.rllib.utils.annotations import override
from ray.rllib.uti... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/mobilenet_v2_with_lstm_models.py | 0.902544 | 0.394872 | mobilenet_v2_with_lstm_models.py | pypi |
import numpy as np
import pickle
import ray
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.misc import normc_initializer
from ray.rllib.models.tf.recurrent_net import RecurrentNetwork
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_tf
tf1, tf, t... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/rnn_spy_model.py | 0.920231 | 0.310178 | rnn_spy_model.py | pypi |
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.policy.view_requirement import ViewRequirement
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.tf_op... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/trajectory_view_utilizing_models.py | 0.959592 | 0.390272 | trajectory_view_utilizing_models.py | pypi |
import numpy as np
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.preprocessors import get_preprocessor
from ray.rllib.models.tf.recurrent_net import RecurrentNetwork
from ray.rllib.models.torch.recurrent_net import RecurrentNetwork as TorchRNN
from ray.rllib.utils.annotations import override
from ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/rnn_model.py | 0.913252 | 0.455017 | rnn_model.py | pypi |
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_tf, try_import_... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/models/fast_model.py | 0.94545 | 0.38549 | fast_model.py | pypi |
import gym
import numpy as np
import random
from ray.rllib.examples.env.rock_paper_scissors import RockPaperScissors
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.view_requirement import ViewRequirement
class AlwaysSameHeuristic(Policy):
"""Pick a random move and stick with it for the entire e... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/policy/rock_paper_scissors_dummies.py | 0.836688 | 0.24638 | rock_paper_scissors_dummies.py | pypi |
from gym.spaces import Box
import numpy as np
from ray.rllib.examples.policy.random_policy import RandomPolicy
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.view_requirement import ViewRequirement
from ray.rllib.utils.annotations import override
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/policy/episode_env_aware_policy.py | 0.917935 | 0.226153 | episode_env_aware_policy.py | pypi |
import random
import numpy as np
from gym.spaces import Box
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import override
from ray.rllib.utils.typing import ModelWeights
class RandomPolicy(Policy):
"""Hand-coded policy that returns random actions."""
def __init__(self, *args, ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/policy/random_policy.py | 0.861407 | 0.221319 | random_policy.py | pypi |
from ray.rllib.env.wrappers.dm_control_wrapper import DMCEnv
"""
8 Environments from Deepmind Control Suite
"""
def acrobot_swingup(from_pixels=True,
height=64,
width=64,
frame_skip=2,
channels_first=True):
return DMCEnv(
"acr... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/dm_control_suite.py | 0.893623 | 0.169612 | dm_control_suite.py | pypi |
import gym
from gym import spaces
from gym.utils import seeding
import math
import numpy as np
class StatelessCartPole(gym.Env):
"""Partially observable variant of the CartPole gym environment.
https://github.com/openai/gym/blob/master/gym/envs/classic_control/
cartpole.py
We delete the velocity com... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/stateless_cartpole.py | 0.88799 | 0.67108 | stateless_cartpole.py | pypi |
from gym.envs.classic_control import PendulumEnv, CartPoleEnv
import numpy as np
# MuJoCo may not be installed.
HalfCheetahEnv = HopperEnv = None
try:
from gym.envs.mujoco import HalfCheetahEnv, HopperEnv
except Exception:
pass
class CartPoleWrapper(CartPoleEnv):
"""Wrapper for the Cartpole-v0 environme... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/mbmpo_env.py | 0.858289 | 0.665166 | mbmpo_env.py | pypi |
import logging
from abc import ABC
from collections import Iterable
from typing import Dict
import numpy as np
from gym.spaces import Discrete
from gym.utils import seeding
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.examples.env.utils.interfaces import InfoAccumulationInterface
from ray.rl... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/matrix_sequential_social_dilemma.py | 0.904537 | 0.45744 | matrix_sequential_social_dilemma.py | pypi |
import numpy as np
import gym
from gym.envs.mujoco.mujoco_env import MujocoEnv
from ray.rllib.env.meta_env import MetaEnv
class AntRandGoalEnv(gym.utils.EzPickle, MujocoEnv, MetaEnv):
"""Ant Environment that randomizes goals as tasks
Goals are randomly sampled 2D positions
"""
def __init__(self):
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/ant_rand_goal.py | 0.819424 | 0.421969 | ant_rand_goal.py | pypi |
import gym
from gym.spaces import Box, Dict, Discrete
import numpy as np
import random
class ParametricActionsCartPole(gym.Env):
"""Parametric action version of CartPole.
In this env there are only ever two valid actions, but we pretend there are
actually up to `max_avail_actions` actions that can be tak... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/parametric_actions_cartpole.py | 0.902516 | 0.656988 | parametric_actions_cartpole.py | pypi |
from gym.spaces import MultiDiscrete, Dict, Discrete
import numpy as np
from ray.rllib.env.multi_agent_env import MultiAgentEnv, ENV_STATE
class TwoStepGame(MultiAgentEnv):
action_space = Discrete(2)
def __init__(self, env_config):
self.state = None
self.agent_1 = 0
self.agent_2 = 1
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/two_step_game.py | 0.749087 | 0.372534 | two_step_game.py | pypi |
import numpy as np
import gym
from gym.envs.mujoco.mujoco_env import MujocoEnv
from ray.rllib.env.meta_env import MetaEnv
class HalfCheetahRandDirecEnv(MujocoEnv, gym.utils.EzPickle, MetaEnv):
"""HalfCheetah Environment with two diff tasks, moving forwards or backwards
Direction is defined as a scalar: +1.0 ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/halfcheetah_rand_direc.py | 0.876039 | 0.373219 | halfcheetah_rand_direc.py | pypi |
import gym
import numpy as np
from ray.rllib.env.multi_agent_env import MultiAgentEnv
class DebugCounterEnv(gym.Env):
"""Simple Env that yields a ts counter as observation (0-based).
Actions have no effect.
The episode length is always 15.
Reward is always: current ts % 3.
"""
def __init__(... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/debug_counter_env.py | 0.697197 | 0.362405 | debug_counter_env.py | pypi |
import copy
from collections import Iterable
import gym
import logging
import numpy as np
from gym.spaces import Discrete
from gym.utils import seeding
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils import override
from typing import Dict
from ray.rllib.examples.env.utils.interfaces imp... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/coin_game_non_vectorized_env.py | 0.83498 | 0.366193 | coin_game_non_vectorized_env.py | pypi |
import gym
import random
from ray.rllib.env.multi_agent_env import MultiAgentEnv, make_multi_agent
from ray.rllib.examples.env.mock_env import MockEnv, MockEnv2
from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
from ray.rllib.utils.deprecation import deprecation_warning
def make_multiagent(env_... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/multi_agent.py | 0.81549 | 0.341583 | multi_agent.py | pypi |
from gym.spaces import Discrete
from ray.rllib.env.multi_agent_env import MultiAgentEnv
class RockPaperScissors(MultiAgentEnv):
"""Two-player environment for the famous rock paper scissors game.
The observation is simply the last opponent action."""
ROCK = 0
PAPER = 1
SCISSORS = 2
LIZARD = ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/rock_paper_scissors.py | 0.739328 | 0.437884 | rock_paper_scissors.py | pypi |
import gym
import numpy as np
class LookAndPush(gym.Env):
"""Memory-requiring Env: Best sequence of actions depends on prev. states.
Optimal behavior:
0) a=0 -> observe next state (s'), which is the "hidden" state.
If a=1 here, the hidden state is not observed.
1) a=1 to always ju... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/look_and_push.py | 0.793826 | 0.570391 | look_and_push.py | pypi |
import copy
from collections import Iterable
import numpy as np
from numba import jit, prange
from numba.typed import List
from ray.rllib.examples.env.coin_game_non_vectorized_env import CoinGame
from ray.rllib.utils import override
class VectorizedCoinGame(CoinGame):
"""
Vectorized Coin Game environment.
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/coin_game_vectorized_env.py | 0.816406 | 0.450964 | coin_game_vectorized_env.py | pypi |
from abc import ABC
from ray.rllib.examples.env.utils.interfaces import InfoAccumulationInterface
class TwoPlayersTwoActionsInfoMixin(InfoAccumulationInterface, ABC):
"""
Mixin class to add logging capability in a two player discrete game.
Logs the frequency of each state.
"""
def _init_info(sel... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/examples/env/utils/mixins.py | 0.760117 | 0.32822 | mixins.py | pypi |
import numpy as np
import gym
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.typing import TensorType, List, Union, ModelConfigDict
@DeveloperAPI
class ActionDistribution:
"""The policy action distribution of an agent.
Attributes:
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/action_dist.py | 0.910443 | 0.707758 | action_dist.py | pypi |
from functools import partial
import gym
from gym.spaces import Box, Dict, Discrete, MultiDiscrete, Tuple
import logging
import numpy as np
import tree
from typing import List, Optional, Type, Union
from ray.tune.registry import RLLIB_MODEL, RLLIB_PREPROCESSOR, \
RLLIB_ACTION_DIST, _global_registry
from ray.rllib.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/catalog.py | 0.940497 | 0.40251 | catalog.py | pypi |
from typing import Optional
from ray.rllib.utils.framework import try_import_jax, try_import_tf, \
try_import_torch
def get_activation_fn(name: Optional[str] = None, framework: str = "tf"):
"""Returns a framework specific activation function, given a name string.
Args:
name (Optional[str]): One ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/utils.py | 0.949106 | 0.611933 | utils.py | pypi |
from typing import List
from ray.rllib.utils.annotations import PublicAPI
from ray.rllib.utils.framework import TensorType, TensorStructType
@PublicAPI
class RepeatedValues:
"""Represents a variable-length list of items from spaces.Repeated.
RepeatedValues are created when you use spaces.Repeated, and are
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/repeated_values.py | 0.865082 | 0.659378 | repeated_values.py | pypi |
from collections import OrderedDict
import cv2
import logging
import numpy as np
import gym
from typing import Any, List
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.spaces.repeated import Repeated
from ray.rllib.utils.typing import TensorType
ATARI_OBS_SHAPE = (210, 160, 3)
ATARI_... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/preprocessors.py | 0.945513 | 0.406685 | preprocessors.py | pypi |
from collections import OrderedDict
import contextlib
import gym
import numpy as np
from typing import Dict, List, Any, Union
from ray.rllib.models.preprocessors import get_preprocessor, \
RepeatedValuesPreprocessor
from ray.rllib.models.repeated_values import RepeatedValues
from ray.rllib.policy.sample_batch impo... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/modelv2.py | 0.949832 | 0.479686 | modelv2.py | pypi |
from math import log
import numpy as np
import functools
import tree
import gym
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils import MIN_LOG_NN_OUTPUT, MAX_LOG_NN_OUTPUT, \
SMALL_NUMBER
from ray.rllib.utils.annotations import override,... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/tf_action_dist.py | 0.929136 | 0.438304 | tf_action_dist.py | pypi |
from gym.spaces import Box, Discrete, Tuple
import numpy as np
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.models.modelv2 import ModelV2, restore_original_dimensions
from ray.rllib.models.tf.misc import normc_initializer
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.ut... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/complex_input_net.py | 0.736116 | 0.452838 | complex_input_net.py | pypi |
import numpy as np
from typing import Tuple, Any, Optional
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.typing import TensorType
tf1, tf, tfv = try_import_tf()
def normc_initializer(std: float = 1.0) -> Any:
def _initializer(shape, dtype=None, partition_info=None):
out = np.r... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/misc.py | 0.933922 | 0.537163 | misc.py | pypi |
import contextlib
import gym
import re
from typing import Dict, List, Union
from ray.util import log_once
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.deprecation import deprecation_warning
from ray.rllib.utils.framework import try_import... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/tf_modelv2.py | 0.900301 | 0.276764 | tf_modelv2.py | pypi |
from gym.spaces import Box, Discrete, MultiDiscrete
import numpy as np
import gym
from typing import Any, Dict, Optional, Union
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.layers import GRUGate, RelativeMultiHeadAttention, \
SkipConnection
from ray.rllib.models.tf.tf_modelv2 import TFMode... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/attention_net.py | 0.955178 | 0.476762 | attention_net.py | pypi |
import numpy as np
import gym
from ray.rllib.models.tf.misc import normc_initializer
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.utils import get_activation_fn
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.typing import Dict, TensorType, List, ModelConfigDict
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/fcnet.py | 0.877424 | 0.453322 | fcnet.py | pypi |
from typing import Dict, List
import gym
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.tf.misc import normc_initializer
from ray.rllib.models.utils import get_activation_fn, get_filter_config
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.view_requirement impor... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/visionnet.py | 0.943841 | 0.377168 | visionnet.py | pypi |
import numpy as np
from ray.rllib.models.utils import get_activation_fn
from ray.rllib.utils.framework import get_variable, try_import_tf, \
TensorType, TensorShape
tf1, tf, tfv = try_import_tf()
class NoisyLayer(tf.keras.layers.Layer if tf else object):
"""A Layer that adds learnable Noise to some previous... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/layers/noisy_layer.py | 0.885953 | 0.70531 | noisy_layer.py | pypi |
from typing import Optional
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.typing import TensorType
tf1, tf, tfv = try_import_tf()
class RelativeMultiHeadAttention(tf.keras.layers.Layer if tf else object):
"""A RelativeMultiHeadAttention layer as described in [3].
Uses segment lev... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/tf/layers/relative_multi_head_attention.py | 0.959602 | 0.547343 | relative_multi_head_attention.py | pypi |
import time
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_jax, try_import_tfp
from ray.rllib.utils.typing import TensorType, List
jax, flax = try_import_jax()
tf... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/jax/jax_action_dist.py | 0.920994 | 0.475544 | jax_action_dist.py | pypi |
import time
from typing import Callable, Optional
from ray.rllib.utils.framework import get_activation_fn, try_import_jax
jax, flax = try_import_jax()
nn = np = None
if flax:
import flax.linen as nn
import jax.numpy as np
class SlimFC:
"""Simple JAX version of a fully connected layer."""
def __init... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/jax/misc.py | 0.936139 | 0.282474 | misc.py | pypi |
import logging
import numpy as np
import time
from ray.rllib.models.jax.jax_modelv2 import JAXModelV2
from ray.rllib.models.jax.misc import SlimFC
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_jax
jax, flax = try_import_jax()
logger = logging.getLogger(__name__)
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/jax/fcnet.py | 0.846483 | 0.302584 | fcnet.py | pypi |
import numpy as np
from typing import Union, Tuple, Any, List
from ray.rllib.models.utils import get_activation_fn
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import TensorType
torch, nn = try_import_torch()
def normc_initializer(std: float = 1.0) -> Any:
def initializer(t... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/misc.py | 0.965037 | 0.576125 | misc.py | pypi |
import gym
from gym.spaces import Box, Discrete, MultiDiscrete
import numpy as np
from typing import Dict, Optional, Union
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.modules import GRUGate, \
RelativeMultiHeadAttention, SkipConnection
fro... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/attention_net.py | 0.965988 | 0.457016 | attention_net.py | pypi |
import logging
import numpy as np
import gym
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.misc import SlimFC, AppendBiasLayer, \
normc_initializer
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.util... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/fcnet.py | 0.912998 | 0.294437 | fcnet.py | pypi |
import functools
from math import log
import numpy as np
import tree
import gym
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_torch
from ray.rlli... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/torch_action_dist.py | 0.886789 | 0.433172 | torch_action_dist.py | pypi |
import gym
from typing import Dict, List, Union
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import ModelConfigDict, TensorType
_, nn = try_import_torch()
@PublicAPI
class T... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/torch_modelv2.py | 0.922439 | 0.274254 | torch_modelv2.py | pypi |
import numpy as np
from typing import Dict, List
import gym
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.misc import normc_initializer, same_padding, \
SlimConv2d, SlimFC
from ray.rllib.models.utils import get_activation_fn, get_filter_config
from ray.rllib.policy.sampl... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/visionnet.py | 0.936735 | 0.33334 | visionnet.py | pypi |
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.utils.torch_ops import sequence_mask
from ray.rllib.utils.framework import TensorType
torch, nn = try_import_torch()
class MultiHeadAttention(nn.Module):
"""A multi-head attention layer described ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/modules/multi_head_attention.py | 0.91967 | 0.447883 | multi_head_attention.py | pypi |
import numpy as np
from ray.rllib.models.utils import get_activation_fn
from ray.rllib.utils.framework import try_import_torch, TensorType
torch, nn = try_import_torch()
class NoisyLayer(nn.Module):
"""A Layer that adds learnable Noise to some previous layer's outputs.
Consists of:
- a common dense lay... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/modules/noisy_layer.py | 0.947962 | 0.669762 | noisy_layer.py | pypi |
from typing import Union
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.utils.torch_ops import sequence_mask
from ray.rllib.utils.typing import TensorType
torch, nn = try_import_torch()
class RelativePositionEmbedding(nn.Module):
"""Creates a... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/modules/relative_multi_head_attention.py | 0.970562 | 0.567277 | relative_multi_head_attention.py | pypi |
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.framework import TensorType
torch, nn = try_import_torch()
class GRUGate(nn.Module):
"""Implements a gated recurrent unit for use in AttentionNet"""
def __init__(self, dim: int, init_bias: int = 0., **kwargs):
"""
in... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/modules/gru_gate.py | 0.946262 | 0.448909 | gru_gate.py | pypi |
from typing import Tuple
from ray.rllib.models.torch.misc import Reshape
from ray.rllib.models.utils import get_activation_fn, get_initializer
from ray.rllib.utils.framework import try_import_torch
torch, nn = try_import_torch()
if torch:
import torch.distributions as td
class ConvTranspose2DStack(nn.Module):
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/models/torch/modules/convtranspose2d_stack.py | 0.960156 | 0.490053 | convtranspose2d_stack.py | pypi |
from collections import namedtuple
import logging
from ray.util.debug import log_once
from ray.rllib.utils.debug import summarize
from ray.rllib.utils.framework import try_import_tf
tf1, tf, tfv = try_import_tf()
# Variable scope in which created variables will be placed under
TOWER_SCOPE_NAME = "tower"
logger = lo... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/multi_gpu_impl.py | 0.915259 | 0.489992 | multi_gpu_impl.py | pypi |
import logging
import numpy as np
import math
import tree
from typing import List, Tuple, Any
import ray
from ray.rllib.evaluation.metrics import extract_stats, get_learner_stats, \
LEARNER_STATS_KEY
from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.execution.common import \
AGENT_STEPS_TRAI... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/train_ops.py | 0.699357 | 0.199269 | train_ops.py | pypi |
import collections
import logging
import numpy as np
import platform
import random
from typing import List, Dict
# Import ray before psutil will make sure we use psutil's bundled version
import ray # noqa F401
import psutil # noqa E402
from ray.rllib.execution.segment_tree import SumSegmentTree, MinSegmentTree
from... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/replay_buffer.py | 0.841533 | 0.261876 | replay_buffer.py | pypi |
import copy
from six.moves import queue
import threading
from typing import Dict
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.execution.minibatch_buffer import MinibatchBuffer
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.timer import TimerStat
from ray.rllib.uti... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/learner_thread.py | 0.870597 | 0.167797 | learner_thread.py | pypi |
import logging
import threading
import math
from six.moves import queue
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.execution.learner_thread import LearnerThread
from ray.rllib.execution.minibatch_buffer import MinibatchBuffer
f... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/multi_gpu_learner.py | 0.717804 | 0.190216 | multi_gpu_learner.py | pypi |
from typing import List, Any, Optional
import random
from ray.util.iter import from_actors, LocalIterator, _NextValueNotReady
from ray.util.iter_metrics import SharedMetrics
from ray.rllib.execution.replay_buffer import LocalReplayBuffer, \
warn_replay_buffer_size
from ray.rllib.execution.common import \
STEPS... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/replay_ops.py | 0.914806 | 0.320515 | replay_ops.py | pypi |
import logging
from typing import List, Tuple
import time
from ray.util.iter import from_actors, LocalIterator
from ray.util.iter_metrics import SharedMetrics
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.evaluation.rollout_worker import get_global_worker
from ray.rllib.evaluation.worker_se... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/rollout_ops.py | 0.878007 | 0.246069 | rollout_ops.py | pypi |
from typing import Any, List, Dict
import time
from ray.util.iter import LocalIterator
from ray.rllib.evaluation.metrics import collect_episodes, summarize_episodes
from ray.rllib.execution.common import AGENT_STEPS_SAMPLED_COUNTER, \
STEPS_SAMPLED_COUNTER, _get_shared_metrics
from ray.rllib.evaluation.worker_set ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/metric_ops.py | 0.936648 | 0.398172 | metric_ops.py | pypi |
from typing import List, Optional, Any
import queue
from ray.util.iter import LocalIterator, _NextValueNotReady
from ray.util.iter_metrics import SharedMetrics
from ray.rllib.utils.typing import SampleBatchType
def Concurrently(ops: List[LocalIterator],
*,
mode: str = "round_robin",... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/concurrency_ops.py | 0.941318 | 0.420778 | concurrency_ops.py | pypi |
import logging
import platform
from typing import List, Dict, Any
import ray
from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.execution.common import AGENT_STEPS_SAMPLED_COUNTER, \
STEPS_SAMPLED_COUNTER, _get_shared_metrics
from ray.rllib.execution.replay_ops import MixInReplay
from ray.rllib.e... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/tree_agg.py | 0.855957 | 0.179028 | tree_agg.py | pypi |
import operator
from typing import Any, Optional
class SegmentTree:
"""A Segment Tree data structure.
https://en.wikipedia.org/wiki/Segment_tree
Can be used as regular array, but with two important differences:
a) Setting an item's value is slightly slower. It is O(lg capacity),
instead ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/execution/segment_tree.py | 0.936489 | 0.834744 | segment_tree.py | pypi |
from collections import namedtuple
import logging
import numpy as np
from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
from ray.rllib.policy import Policy
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.offline.io_context import IOContext
from ray.rllib.utils.numpy import conv... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/offline/off_policy_estimator.py | 0.961831 | 0.321487 | off_policy_estimator.py | pypi |
from abc import ABCMeta, abstractmethod
import logging
import numpy as np
import threading
from ray.rllib.policy.sample_batch import MultiAgentBatch
from ray.rllib.utils.annotations import PublicAPI
from ray.rllib.utils.framework import try_import_tf
from typing import Dict, List
from ray.rllib.utils.typing import Ten... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/offline/input_reader.py | 0.938604 | 0.248022 | input_reader.py | pypi |
from collections import OrderedDict
import gym
import logging
import numpy as np
import re
from typing import Callable, Dict, List, Optional, Tuple, Type
from ray.util.debug import log_once
from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.p... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/policy/dynamic_tf_policy.py | 0.951538 | 0.339663 | dynamic_tf_policy.py | pypi |
import gym
import numpy as np
from typing import List, Optional, Union
from ray.rllib.utils.framework import try_import_torch
torch, _ = try_import_torch()
class ViewRequirement:
"""Single view requirement (for one column in an SampleBatch/input_dict).
Policies and ModelV2s return a Dict[str, ViewRequireme... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/policy/view_requirement.py | 0.947878 | 0.591104 | view_requirement.py | pypi |
import gym
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
from ray.util import log_once
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.policy_template... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/policy/torch_policy_template.py | 0.890247 | 0.332229 | torch_policy_template.py | pypi |
import functools
import logging
import threading
from typing import Dict, List, Optional, Tuple
from ray.util.debug import log_once
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.models.repeated_values import RepeatedValues
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY
from ray.rllib.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/policy/eager_tf_policy.py | 0.788949 | 0.211743 | eager_tf_policy.py | pypi |
import gym
from typing import Callable, Dict, List, Optional, Tuple, Type, Union
from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.policy.dynamic_tf_policy import DynamicTFPolicy
from ray.rllib.policy import eager_tf_policy
from ray.rllib.po... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/policy/tf_policy_template.py | 0.949424 | 0.382516 | tf_policy_template.py | pypi |
from abc import ABCMeta, abstractmethod
import gym
from gym.spaces import Box
import logging
import numpy as np
import tree
from typing import Dict, List, Optional
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.view_requirement import ViewR... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/policy/policy.py | 0.952618 | 0.427636 | policy.py | pypi |
from typing import Dict
from ray.rllib.env import BaseEnv
from ray.rllib.policy import Policy
from ray.rllib.evaluation import MultiAgentEpisode, RolloutWorker
from ray.rllib.utils.framework import TensorType
from ray.rllib.utils.typing import AgentID, PolicyID
class ObservationFunction:
"""Interceptor function ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/evaluation/observation_function.py | 0.87864 | 0.513973 | observation_function.py | pypi |
import logging
import numpy as np
import collections
from typing import Any, Dict, List, Optional, Tuple, Union
import ray
from ray.rllib.evaluation.rollout_metrics import RolloutMetrics
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.offline.off_policy_estimator import OffPolicyEstimate
fro... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/evaluation/metrics.py | 0.924908 | 0.238351 | metrics.py | pypi |
import collections
import logging
import numpy as np
from typing import List, Any, Dict, Optional, TYPE_CHECKING
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.evaluation.episode import MultiAgentEpisode
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/evaluation/sample_batch_builder.py | 0.926003 | 0.337367 | sample_batch_builder.py | pypi |
import gym
import logging
from types import FunctionType
from typing import Callable, Dict, List, Optional, Tuple, Type, TypeVar, Union
import ray
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.evaluation.rollout_worker import RolloutWorker, \
_validate_multiagent_config
from ray.rllib.offline... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/evaluation/worker_set.py | 0.90938 | 0.174059 | worker_set.py | pypi |
import numpy as np
import scipy.signal
from typing import Dict, Optional
from ray.rllib.evaluation.episode import MultiAgentEpisode
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.typing import Ag... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/evaluation/postprocessing.py | 0.956472 | 0.506591 | postprocessing.py | pypi |
from collections import defaultdict
import numpy as np
import random
from typing import List, Dict, Callable, Any, TYPE_CHECKING
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.spaces.space_utils imp... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/evaluation/episode.py | 0.924039 | 0.323567 | episode.py | pypi |
from abc import abstractmethod, ABCMeta
import logging
from typing import Dict, List, Optional, TYPE_CHECKING, Union
from ray.rllib.evaluation.episode import MultiAgentEpisode
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
from ray.rllib.utils.typing i... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/evaluation/collectors/sample_collector.py | 0.949891 | 0.306057 | sample_collector.py | pypi |
from six.moves import queue
import gym
import threading
import uuid
from typing import Optional
from ray.rllib.utils.annotations import PublicAPI
from ray.rllib.utils.typing import EnvActionType, EnvObsType, EnvInfoDict
@PublicAPI
class ExternalEnv(threading.Thread):
"""An environment that interfaces with extern... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/external_env.py | 0.944766 | 0.395718 | external_env.py | pypi |
from typing import Tuple, Dict, List
import gym
from ray.rllib.utils.annotations import PublicAPI
from ray.rllib.utils.typing import MultiAgentDict, AgentID
# If the obs space is Dict type, look for the global state under this key.
ENV_STATE = "state"
@PublicAPI
class MultiAgentEnv:
"""An environment that hosts... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/multi_agent_env.py | 0.931283 | 0.461805 | multi_agent_env.py | pypi |
import logging
from typing import Tuple, Callable, Optional
import ray
from ray.rllib.env.base_env import BaseEnv, _DUMMY_AGENT_ID, ASYNC_RESET_RETURN
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.typing import MultiEnvDict, EnvType, EnvID, MultiAgentDict
logger = logging.getLogger(... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/remote_vector_env.py | 0.862974 | 0.241445 | remote_vector_env.py | pypi |
import logging
import threading
import time
from typing import Union, Optional
import ray.cloudpickle as pickle
from ray.rllib.env import ExternalEnv, MultiAgentEnv, ExternalMultiAgentEnv
from ray.rllib.policy.sample_batch import MultiAgentBatch
from ray.rllib.utils.annotations import PublicAPI
from ray.rllib.utils.ty... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/policy_client.py | 0.929456 | 0.167389 | policy_client.py | pypi |
import logging
import gym
import numpy as np
from typing import Callable, List, Optional, Tuple
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.typing import EnvActionType, EnvConfigDict, EnvInfoDict, \
EnvObsType, EnvType, PartialTrainerConfigDict
logger = logging.getLogger(__nam... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/vector_env.py | 0.93196 | 0.593433 | vector_env.py | pypi |
from typing import Callable, Tuple, Optional, List, Dict, Any, TYPE_CHECKING
from ray.rllib.env.external_env import ExternalEnv
from ray.rllib.env.external_multi_agent_env import ExternalMultiAgentEnv
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.env.vector_env import VectorEnv
from ray.rllib.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/base_env.py | 0.9069 | 0.356951 | base_env.py | pypi |
import gym
from gym import spaces
import numpy as np
try:
from dm_env import specs
except ImportError:
specs = None
def _convert_spec_to_space(spec):
if isinstance(spec, dict):
return spaces.Dict(
{k: _convert_spec_to_space(v)
for k, v in spec.items()})
if isinstance... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/dm_env_wrapper.py | 0.855066 | 0.359308 | dm_env_wrapper.py | pypi |
from copy import deepcopy
from typing import Any, Dict, Optional, Tuple
try:
import kaggle_environments
except (ImportError, ModuleNotFoundError):
pass
import numpy as np
from gym.spaces import Box
from gym.spaces import Dict as DictSpace
from gym.spaces import Discrete, MultiBinary, MultiDiscrete, Space
from g... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/kaggle_wrapper.py | 0.917575 | 0.25689 | kaggle_wrapper.py | pypi |
from collections import OrderedDict
from ray.rllib.env.multi_agent_env import MultiAgentEnv
# info key for the individual rewards of an agent, for example:
# info: {
# group_1: {
# _group_rewards: [5, -1, 1], # 3 agents in this group
# }
# }
GROUP_REWARDS = "_group_rewards"
# info key for the individual in... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/group_agents_wrapper.py | 0.868241 | 0.360151 | group_agents_wrapper.py | pypi |
from gym import core, spaces
try:
from dm_env import specs
except ImportError:
specs = None
try:
from dm_control import suite
except (ImportError, OSError):
suite = None
import numpy as np
def _spec_to_box(spec):
def extract_min_max(s):
assert s.dtype == np.float64 or s.dtype == np.float32... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/dm_control_wrapper.py | 0.855066 | 0.361475 | dm_control_wrapper.py | pypi |
import logging
import numpy as np
from gym.spaces import Discrete
from ray.rllib.utils.annotations import override
from ray.rllib.env.vector_env import VectorEnv
from ray.rllib.evaluation.rollout_worker import get_global_worker
from ray.rllib.env.base_env import BaseEnv
from ray.rllib.utils.typing import EnvType
logge... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/model_vector_env.py | 0.912694 | 0.487429 | model_vector_env.py | pypi |
from ray.rllib.env.multi_agent_env import MultiAgentEnv
class PettingZooEnv(MultiAgentEnv):
"""An interface to the PettingZoo MARL environment library.
See: https://github.com/PettingZoo-Team/PettingZoo
Inherits from MultiAgentEnv and exposes a given AEC
(actor-environment-cycle) game from the Petti... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/pettingzoo_env.py | 0.912816 | 0.603231 | pettingzoo_env.py | pypi |
from collections import OrderedDict
import gym
from gym import spaces
import numpy as np
from recsim.environments import interest_evolution
from typing import List
from ray.rllib.utils.error import UnsupportedSpaceException
from ray.tune.registry import register_env
class RecSimObservationSpaceWrapper(gym.Observatio... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/recsim_wrapper.py | 0.932222 | 0.463019 | recsim_wrapper.py | pypi |
from gym.spaces import Box, MultiDiscrete, Tuple as TupleSpace
import logging
import numpy as np
import random
import time
from typing import Callable, Optional, Tuple
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils.annotations import override
from ray.rllib.utils.typing import MultiAgentD... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/env/wrappers/unity3d_env.py | 0.926769 | 0.340362 | unity3d_env.py | pypi |
from ray.rllib.utils.annotations import DeveloperAPI
import logging
import time
import base64
import numpy as np
from ray import cloudpickle as pickle
from six import string_types
logger = logging.getLogger(__name__)
try:
import lz4.frame
LZ4_ENABLED = True
except ImportError:
logger.warning("lz4 not ava... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/rllib/utils/compression.py | 0.47098 | 0.177063 | compression.py | pypi |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.