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
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from typing import List
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedder
from rl_coach.base_parameters import EmbedderScheme
from rl_coach.core_types import InputImageEmbeddi... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/embedders/image_embedder.py | 0.947805 | 0.500183 | image_embedder.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.q_head import QHead
from rl_coach.base_parameters import AgentParameters
from rl_coach.spaces import SpacesDefinition
class DuelingQHead(QHead):
def __init__(self... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/dueling_q_head.py | 0.820254 | 0.296024 | dueling_q_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import ActionProbabilities
from rl_coach.spaces import SpacesDefinition
f... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/sac_head.py | 0.847274 | 0.292829 | sac_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import QActionStateValue
from rl_coach.spaces import BoxActionSpace
from ... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/naf_head.py | 0.894011 | 0.314011 | naf_head.py | pypi |
import numpy as np
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import ActionProbabili... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/policy_head.py | 0.855987 | 0.361052 | policy_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import QActionStateValue
from rl_coach.spaces import SpacesDefinition, Bo... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py | 0.877857 | 0.369599 | sac_q_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import VStateValue
from rl_coach.spaces i... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/v_head.py | 0.870515 | 0.251165 | v_head.py | pypi |
import tensorflow as tf
import numpy as np
from rl_coach.architectures.tensorflow_components.heads import QHead
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.base_parameters import AgentParameters
from rl_coach.spaces import SpacesDefinition
class CategoricalQHead(QHead):
d... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/categorical_q_head.py | 0.885786 | 0.367951 | categorical_q_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.q_head import QHead
from rl_coach.base_parameters import AgentParameters
from rl_coach.memories.non_episodic import differentiable_neural_dictionary
from rl_coach.spaces... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/dnd_q_head.py | 0.845209 | 0.378028 | dnd_q_head.py | pypi |
import numpy as np
import tensorflow as tf
from tensorflow.python.ops.losses.losses_impl import Reduction
from rl_coach.architectures.tensorflow_components.layers import Dense, convert_layer_class
from rl_coach.base_parameters import AgentParameters
from rl_coach.spaces import SpacesDefinition
from rl_coach.utils impo... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/head.py | 0.903272 | 0.46132 | head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import ActionProbabilities
from rl_coach.spaces import DiscreteActionSpac... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/acer_policy_head.py | 0.894141 | 0.337558 | acer_policy_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import ActionProbabilities
from rl_coach.sp... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py | 0.901496 | 0.274676 | ddpg_actor_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import Measurements
from rl_coach.spaces import SpacesDefinition
class ... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/measurements_prediction_head.py | 0.903543 | 0.402568 | measurements_prediction_head.py | pypi |
import tensorflow as tf
import numpy as np
from rl_coach.architectures.tensorflow_components.heads import QHead
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.base_parameters import AgentParameters
from rl_coach.spaces import SpacesDefinition
class RainbowQHead(QHead):
def __... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/rainbow_q_head.py | 0.899351 | 0.368207 | rainbow_q_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import ActionProbabilities
from rl_coach.... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/ppo_v_head.py | 0.890485 | 0.274899 | ppo_v_head.py | pypi |
import tensorflow as tf
import numpy as np
from rl_coach.architectures.tensorflow_components.heads import QHead
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.base_parameters import AgentParameters
from rl_coach.spaces import SpacesDefinition
class QuantileRegressionQHead(QHead):... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/quantile_regression_q_head.py | 0.910398 | 0.541045 | quantile_regression_q_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import QActionStateValue
from rl_coach.spaces import SpacesDefinition, Bo... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/q_head.py | 0.892668 | 0.270673 | q_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import VStateValue
from rl_coach.spaces i... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/td3_v_head.py | 0.869008 | 0.284511 | td3_v_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import QActionStateValue
from rl_coach.spaces import SpacesDefinition, Bo... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/cil_head.py | 0.838812 | 0.245571 | cil_head.py | pypi |
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense
from rl_coach.architectures.tensorflow_components.heads.head import Head
from rl_coach.base_parameters import AgentParameters
from rl_coach.core_types import Embedding
from rl_coach.spaces import... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/architectures/tensorflow_components/heads/wolpertinger_actor_head.py | 0.814348 | 0.234461 | wolpertinger_actor_head.py | pypi |
import os
from abc import ABC, abstractmethod
import threading
import pickle
import redis
import numpy as np
from rl_coach.utils import get_latest_checkpoint
class SharedRunningStatsSubscribe(threading.Thread):
def __init__(self, shared_running_stats):
super().__init__()
self.shared_running_stats... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/utilities/shared_running_stats.py | 0.789923 | 0.217379 | shared_running_stats.py | pypi |
import os
import numpy as np
from rl_coach.core_types import RewardType
from rl_coach.filters.reward.reward_filter import RewardFilter
from rl_coach.spaces import RewardSpace
from rl_coach.utilities.shared_running_stats import NumpySharedRunningStats
class RewardNormalizationFilter(RewardFilter):
"""
Normal... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/reward/reward_normalization_filter.py | 0.806319 | 0.414662 | reward_normalization_filter.py | pypi |
import os
import numpy as np
import pickle
from rl_coach.core_types import RewardType
from rl_coach.filters.reward.reward_filter import RewardFilter
from rl_coach.spaces import RewardSpace
from rl_coach.utils import get_latest_checkpoint
class RewardEwmaNormalizationFilter(RewardFilter):
"""
Normalizes the ... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/reward/reward_ewma_normalization_filter.py | 0.770637 | 0.310838 | reward_ewma_normalization_filter.py | pypi |
from typing import Union
import numpy as np
from rl_coach.core_types import ActionType
from rl_coach.filters.action.action_filter import ActionFilter
from rl_coach.spaces import BoxActionSpace
class BoxMasking(ActionFilter):
"""
Masks part of the action space to enforce the agent to work in a defined space... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/action/box_masking.py | 0.93281 | 0.70808 | box_masking.py | pypi |
from rl_coach.core_types import ActionType
from rl_coach.filters.filter import Filter
from rl_coach.spaces import ActionSpace
class ActionFilter(Filter):
def __init__(self, input_action_space: ActionSpace=None):
self.input_action_space = input_action_space
self.output_action_space = None
... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/action/action_filter.py | 0.822653 | 0.531757 | action_filter.py | pypi |
from typing import Union
import numpy as np
from rl_coach.core_types import ActionType
from rl_coach.filters.action.action_filter import ActionFilter
from rl_coach.spaces import BoxActionSpace
class LinearBoxToBoxMap(ActionFilter):
"""
A linear mapping of two box action spaces. For example, if the action s... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/action/linear_box_to_box_map.py | 0.940865 | 0.67145 | linear_box_to_box_map.py | pypi |
from typing import List
from rl_coach.core_types import ActionType
from rl_coach.filters.action.action_filter import ActionFilter
from rl_coach.spaces import DiscreteActionSpace, ActionSpace
class PartialDiscreteActionSpaceMap(ActionFilter):
"""
Partial map of two countable action spaces. For example, consi... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/action/partial_discrete_action_space_map.py | 0.881672 | 0.534309 | partial_discrete_action_space_map.py | pypi |
from typing import Union, List
import numpy as np
from rl_coach.filters.action.box_discretization import BoxDiscretization
from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
from rl_coach.spaces import AttentionActionSpace, BoxActionSpace, DiscreteActionSpace
class ... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/action/attention_discretization.py | 0.850779 | 0.607576 | attention_discretization.py | pypi |
from itertools import product
from typing import Union, List
import numpy as np
from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace
class BoxDiscretization(PartialDiscreteActionSpaceMap):
"""
Dis... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/action/box_discretization.py | 0.847684 | 0.660864 | box_discretization.py | pypi |
import copy
from collections import deque
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace, VectorObservationSpace
class LazyStack(object):
"""
A lazy version of np.s... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_stacking_filter.py | 0.898674 | 0.691172 | observation_stacking_filter.py | pypi |
from skimage.transform import resize
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace
class ObservationRescaleSizeByFactorFilter(ObservationFilter):
"""
Rescales an image observation by... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_rescale_size_by_factor_filter.py | 0.815122 | 0.735452 | observation_rescale_size_by_factor_filter.py | pypi |
import copy
from skimage.transform import resize
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace, PlanarMapsObservationSpace, ImageObservationSpace
class ObservationRescaleTo... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_rescale_to_size_filter.py | 0.866189 | 0.70374 | observation_rescale_to_size_filter.py | pypi |
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace, PlanarMapsObservationSpace
class ObservationMoveAxisFilter(ObservationFilter):
"""
Reorders the axes of the observati... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_move_axis_filter.py | 0.856647 | 0.677047 | observation_move_axis_filter.py | pypi |
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace
class ObservationToUInt8Filter(ObservationFilter):
"""
Converts a floating point observation into an unsigned int 8 bi... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_to_uint8_filter.py | 0.891699 | 0.72167 | observation_to_uint8_filter.py | pypi |
import copy
from enum import Enum
from typing import List
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace, VectorObservationSpace
class ObservationReductionBySubPartsNameFilt... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_reduction_by_sub_parts_name_filter.py | 0.937196 | 0.57529 | observation_reduction_by_sub_parts_name_filter.py | pypi |
from typing import Union, Tuple
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace
class ObservationCropFilter(ObservationFilter):
"""
Crops the size of the observation ... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_crop_filter.py | 0.936605 | 0.777638 | observation_crop_filter.py | pypi |
import os
import pickle
from typing import List
import numpy as np
from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace
from rl_coach.utilities.shared_running_stats import NumpySharedRunningStats, Num... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/filters/observation/observation_normalization_filter.py | 0.918567 | 0.487795 | observation_normalization_filter.py | pypi |
import time
import os
from rl_coach.checkpoint import CheckpointStateReader
from rl_coach.data_stores.data_store import SyncFiles
class CheckpointDataStore(object):
"""
A DataStore which relies on the GraphManager check pointing methods to communicate policies.
"""
def __init__(self, *args, **kwargs)... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/data_stores/checkpoint_data_store.py | 0.465873 | 0.236461 | checkpoint_data_store.py | pypi |
import uuid
from rl_coach.data_stores.data_store import DataStoreParameters
from rl_coach.data_stores.checkpoint_data_store import CheckpointDataStore
class NFSDataStoreParameters(DataStoreParameters):
def __init__(self, ds_params, deployed=False, server=None, path=None, checkpoint_dir: str=""):
super(... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/data_stores/nfs_data_store.py | 0.541409 | 0.150809 | nfs_data_store.py | pypi |
import copy
from typing import Union
from collections import OrderedDict
import numpy as np
from rl_coach.agents.agent import Agent
from rl_coach.agents.ddpg_agent import DDPGAgent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import DDPGAc... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/td3_agent.py | 0.893959 | 0.360883 | td3_agent.py | pypi |
from typing import Union
import numpy as np
from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import ACERPolicyHeadParameters, QHeadParameters
from rl_coach.architectu... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/acer_agent.py | 0.925424 | 0.474266 | acer_agent.py | pypi |
from typing import Union
import numpy as np
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import NAFHeadParameters
from rl_coach.architectures.middleware_parameters... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/naf_agent.py | 0.901252 | 0.275934 | naf_agent.py | pypi |
from typing import Union
from rl_coach.agents.imitation_agent import ImitationAgent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import RegressionHeadParameters
from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/cil_agent.py | 0.927601 | 0.283546 | cil_agent.py | pypi |
import copy
from typing import Union
from collections import OrderedDict
import numpy as np
from rl_coach.agents.actor_critic_agent import ActorCriticAgent
from rl_coach.agents.agent import Agent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameter... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/ddpg_agent.py | 0.894787 | 0.344636 | ddpg_agent.py | pypi |
from typing import Union
import numpy as np
from rl_coach.agents.ddpg_agent import DDPGAgent, DDPGAgentParameters, DDPGAlgorithmParameters
from rl_coach.core_types import RunPhase
from rl_coach.spaces import SpacesDefinition
class HACDDPGAlgorithmParameters(DDPGAlgorithmParameters):
"""
:param time_limit: ... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/hac_ddpg_agent.py | 0.735737 | 0.487673 | hac_ddpg_agent.py | pypi |
import os
import pickle
from typing import Union, List
import numpy as np
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import DNDQHeadParameters
from rl_coach.arch... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/nec_agent.py | 0.903955 | 0.383786 | nec_agent.py | pypi |
from typing import Union
import numpy as np
from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import PolicyHeadParameters
from rl_coach.archit... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/policy_gradients_agent.py | 0.950365 | 0.443058 | policy_gradients_agent.py | pypi |
from typing import Union
import numpy as np
from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_paramet... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/n_step_q_agent.py | 0.924947 | 0.416678 | n_step_q_agent.py | pypi |
from typing import Union, List, Dict
import numpy as np
from rl_coach.core_types import EnvResponse, ActionInfo, RunPhase, PredictionType, ActionType, Transition
from rl_coach.saver import SaverCollection
class AgentInterface(object):
def __init__(self):
self._phase = RunPhase.HEATUP
self._pare... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/agent_interface.py | 0.932538 | 0.649064 | agent_interface.py | pypi |
from typing import Union
import numpy as np
from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
class PALAlgo... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/pal_agent.py | 0.909963 | 0.464719 | pal_agent.py | pypi |
from typing import Union
import numpy as np
from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAgentParameters
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.exploration_policies.bootstrapped import BootstrappedParameters
class BootstrappedDQNNetworkParameters... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/bootstrapped_dqn_agent.py | 0.895651 | 0.238972 | bootstrapped_dqn_agent.py | pypi |
from copy import copy
from typing import Union
import numpy as np
from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.architectures.head_parameters import QuantileRegressionQHea... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/qr_dqn_agent.py | 0.932829 | 0.532911 | qr_dqn_agent.py | pypi |
import copy
from collections import OrderedDict
from typing import Union
import numpy as np
from rl_coach.agents.actor_critic_agent import ActorCriticAgent
from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from ... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/ppo_agent.py | 0.9415 | 0.441914 | ppo_agent.py | pypi |
from typing import Union
import numpy as np
import scipy.signal
from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import PolicyHeadParameters,... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/agents/actor_critic_agent.py | 0.935568 | 0.480052 | actor_critic_agent.py | pypi |
import redis
import pickle
import uuid
import time
from rl_coach.memories.backend.memory import MemoryBackend, MemoryBackendParameters
from rl_coach.core_types import Transition, Episode, EnvironmentSteps, EnvironmentEpisodes
class RedisPubSubMemoryBackendParameters(MemoryBackendParameters):
def __init__(self... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/memories/backend/redis.py | 0.680348 | 0.165189 | redis.py | pypi |
import os
import pickle
import numpy as np
try:
import annoy
from annoy import AnnoyIndex
except ImportError:
from rl_coach.logger import failed_imports
failed_imports.append("annoy")
class AnnoyDictionary(object):
def __init__(self, dict_size, key_width, new_value_shift_coefficient=0.1, batch_s... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py | 0.566498 | 0.233499 | differentiable_neural_dictionary.py | pypi |
import operator
import random
from enum import Enum
from typing import List, Tuple, Any, Union
import numpy as np
from rl_coach.core_types import Transition
from rl_coach.memories.memory import MemoryGranularity
from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters, ExperienceReplay... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/memories/non_episodic/balanced_experience_replay.py | 0.864368 | 0.439326 | balanced_experience_replay.py | pypi |
import operator
import random
from enum import Enum
from typing import List, Tuple, Any
import numpy as np
from rl_coach.core_types import Transition
from rl_coach.memories.memory import MemoryGranularity
from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters, ExperienceReplay
from r... | /rl-coach-1.0.1.tar.gz/rl-coach-1.0.1/rl_coach/memories/non_episodic/prioritized_experience_replay.py | 0.912799 | 0.472379 | prioritized_experience_replay.py | pypi |
import math
import matplotlib.pyplot as plt
import numpy as np
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Udacity Data Science Nano degree class project
Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (... | /rl_distributions-0.1.tar.gz/rl_distributions-0.1/rl_distributions/Gaussiandistribution.py | 0.807916 | 0.842151 | Gaussiandistribution.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Binomial(Distribution):
"""
Udacity Data Science degree class exercise
Binomial distribution class for calculating and
visualizing a Binomial distribution.
Attributes:
mean (float) representing... | /rl_distributions-0.1.tar.gz/rl_distributions-0.1/rl_distributions/Binomialdistribution.py | 0.914577 | 0.901271 | Binomialdistribution.py | pypi |
import argparse
import os
import os.path as osp
import uuid
from typing import Any, Callable, Dict, List, Optional
from omegaconf import OmegaConf
from rl_utils.launcher.run_exp import get_random_id, sub_in_args, sub_in_vars
RUN_DIR = "data/log/runs/"
def change_arg_vals(cmd_parts: List[str], new_arg_values: Dict[... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/launcher/eval_sys.py | 0.468791 | 0.153296 | eval_sys.py | pypi |
from omegaconf import DictConfig, OmegaConf
from rl_utils.logging.base_logger import Logger, LoggerCfgType
try:
import wandb
except ImportError:
wandb = None
class WbLogger(Logger):
"""
Logger for logging to the weights and W&B online service.
"""
def __init__(
self,
wb_proj... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/logging/wb_logger.py | 0.583322 | 0.171981 | wb_logger.py | pypi |
from typing import Dict, List, Union
import numpy as np
import torch
from torch import nn as nn
class SimpleCNN(nn.Module):
"""A Simple 3-Conv CNN followed by a fully connected layer
Takes in observations and produces an embedding of the rgb and/or depth
components. Note the observation spaces shoul... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/models/simple_cnn.py | 0.961061 | 0.70557 | simple_cnn.py | pypi |
import argparse
from typing import Callable, Dict, List, Optional
import pandas as pd
from omegaconf import OmegaConf
from rl_utils.plotting.utils import MISSING_VALUE
from rl_utils.plotting.wb_query import fetch_data_from_cfg
def plot_table(
df: pd.DataFrame,
col_key: str,
row_key: str,
cell_key: s... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/plotting/auto_table.py | 0.836988 | 0.613439 | auto_table.py | pypi |
try:
import wandb
except ImportError:
wandb = None
import os
import os.path as osp
from argparse import ArgumentParser
from collections import defaultdict
from pprint import pprint
from typing import Any, Callable, Dict, List, Optional
import numpy as np
import pandas as pd
from omegaconf import DictConfig, Om... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/plotting/wb_query.py | 0.706798 | 0.19112 | wb_query.py | pypi |
import argparse
from collections import defaultdict
from typing import Dict, List, Optional, Tuple, Union
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from omegaconf import OmegaConf
from rl_utils.plotting.utils import combine_dicts_to_df, fig_save
fro... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/plotting/auto_line.py | 0.894185 | 0.396506 | auto_line.py | pypi |
import argparse
from typing import Dict, Optional, Tuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from omegaconf import OmegaConf
from rl_utils.plotting.utils import fig_save
from rl_utils.plotting.wb_query import batch_query
MISSING_VAL = 0.24444
ERROR_VAL = 0.344... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/plotting/auto_bar.py | 0.849769 | 0.371137 | auto_bar.py | pypi |
import gym
import numpy as np
import torch
import rl_utils.common.core_utils as utils
from rl_utils.envs.vec_env.vec_env import VecEnvWrapper
# Checks whether done was caused my timit limits or not
class TimeLimitMask(gym.Wrapper):
def step(self, action):
obs, rew, done, info = self.env.step(action)
... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/envs/wrappers.py | 0.906044 | 0.469459 | wrappers.py | pypi |
from functools import partial
from typing import Callable, Optional
import gym
import torch
import rl_utils.envs.pointmass # noqa: F401
from rl_utils.envs.registry import full_env_registry
from rl_utils.envs.vec_env.dummy_vec_env import DummyVecEnv
from rl_utils.envs.vec_env.shmem_vec_env import ShmemVecEnv
from rl_... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/envs/env_creator.py | 0.596433 | 0.349297 | env_creator.py | pypi |
from dataclasses import dataclass, field
from typing import List, Optional, Tuple
import numpy as np
import torch
from rl_utils.envs.pointmass.pointmass_env import PointMassEnv, PointMassParams
from rl_utils.envs.registry import full_env_registry
@dataclass(frozen=True)
class SquareObstacle:
"""
* x,y posit... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/envs/pointmass/pointmass_obstacle.py | 0.933104 | 0.558508 | pointmass_obstacle.py | pypi |
from dataclasses import dataclass
from typing import Callable, Optional
import numpy as np
import torch
from gym import spaces
from torch.distributions import Uniform
from rl_utils.envs.registry import full_env_registry
from rl_utils.envs.vec_env.vec_env import FINAL_OBS_KEY, VecEnv
@dataclass(frozen=True)
class Po... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/envs/pointmass/pointmass_env.py | 0.935678 | 0.611382 | pointmass_env.py | pypi |
import ctypes
import multiprocessing as mp
from collections.abc import Iterable
import numpy as np
from rl_utils.common.core_utils import dict_to_obs, obs_space_info, obs_to_dict
from .vec_env import FINAL_OBS_KEY, CloudpickleWrapper, VecEnv, clear_mpi_env_vars
_NP_TO_CT = {
np.float32: ctypes.c_float,
np.f... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/envs/vec_env/shmem_vec_env.py | 0.58676 | 0.253263 | shmem_vec_env.py | pypi |
import contextlib
import os
from abc import ABC, abstractmethod
import cloudpickle
from rl_utils.common.tile_images import tile_images
FINAL_OBS_KEY = "final_obs"
class AlreadySteppingError(Exception):
"""
Raised when an asynchronous step is running while
step_async() is called again.
"""
def ... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/envs/vec_env/vec_env.py | 0.830009 | 0.435361 | vec_env.py | pypi |
import os
import os.path as osp
from collections import defaultdict
from typing import Dict, Optional
import numpy as np
import torch
import torch.nn as nn
from rl_utils.common.core_utils import compress_and_filter_dict
from rl_utils.common.viz_utils import save_mp4
from rl_utils.envs.vec_env.vec_env import VecEnv
fr... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/common/evaluator.py | 0.833562 | 0.245797 | evaluator.py | pypi |
from typing import Dict, List, Optional, Tuple
import torch
from torch.utils.data import Dataset
class DictDataset(Dataset):
def __init__(
self,
load_data: Dict[str, torch.Tensor],
load_keys: Optional[List[str]] = None,
detach_all: bool = True,
):
"""
:paramete... | /rl-exp-utils-0.15.tar.gz/rl-exp-utils-0.15/rl_utils/common/datasets.py | 0.954858 | 0.501892 | datasets.py | pypi |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class NoisyLinear(nn.Linear):
def __init__(self, in_features, out_features, sigma_init=0.017, bias=True):
super(NoisyLinear, self).__init__(in_features, out_features, bias=bias)
self.sigma_weight = n... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/layers.py | 0.924313 | 0.59305 | layers.py | pypi |
from rl_games.common import a2c_common
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd, RunningMeanStdObs
from rl_games.algos_torch import central_value
from rl_games.common import common_losses
from rl_games.common import datasets
from torch import optim
im... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/a2c_discrete.py | 0.819388 | 0.310061 | a2c_discrete.py | pypi |
from rl_games.common import object_factory
import rl_games.algos_torch
from rl_games.algos_torch import network_builder
from rl_games.algos_torch import models
NETWORK_REGISTRY = {}
MODEL_REGISTRY = {}
def register_network(name, target_class):
NETWORK_REGISTRY[name] = lambda **kwargs: target_class()
def register... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/model_builder.py | 0.672869 | 0.187356 | model_builder.py | pypi |
from rl_games.common import a2c_common
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch import central_value
from rl_games.common import common_losses
from rl_games.common import datasets
from torch import optim
import torch
from torch import nn
import numpy as np
import gym
class A2CAgent(a2c_c... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/a2c_continuous.py | 0.770681 | 0.313696 | a2c_continuous.py | pypi |
import torch
from torch import nn
import torch.distributed as dist
import gym
import numpy as np
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd, RunningMeanStdObs
from rl_games.common import common_losses
from rl_games.common import datasets
from rl_games.co... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/central_value.py | 0.894542 | 0.339773 | central_value.py | pypi |
import torch
import torch.nn as nn
import numpy as np
import rl_games.algos_torch.torch_ext as torch_ext
'''
updates moving statistics with momentum
'''
class MovingMeanStd(nn.Module):
def __init__(self, insize, momentum = 0.25, epsilon=1e-05, per_channel=False, norm_only=False):
super(MovingMeanStd, self)... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/moving_mean_std.py | 0.910698 | 0.385808 | moving_mean_std.py | pypi |
from rl_games.algos_torch import torch_ext
import torch
import torch.nn as nn
import numpy as np
'''
updates statistic from a full data
'''
class RunningMeanStd(nn.Module):
def __init__(self, insize, epsilon=1e-05, per_channel=False, norm_only=False):
super(RunningMeanStd, self).__init__()
print('Ru... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/running_mean_std.py | 0.864482 | 0.473536 | running_mean_std.py | pypi |
from rl_games.common.player import BasePlayer
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from rl_games.common.tr_helpers import unsqueeze_obs
import gym
import torch
from torch import nn
import numpy as np
def rescale_actions(low, high, action):
d ... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/algos_torch/players.py | 0.757615 | 0.361559 | players.py | pypi |
import gym
import numpy as np
from pettingzoo.sisl import multiwalker_v6
import yaml
from rl_games.torch_runner import Runner
import os
from collections import deque
import rl_games.envs.connect4_network
class MultiWalker(gym.Env):
def __init__(self, name="multiwalker", **kwargs):
gym.Env.__init__(self)
... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/envs/multiwalker.py | 0.460532 | 0.24608 | multiwalker.py | pypi |
from rl_games.common.ivecenv import IVecEnv
import gym
import numpy as np
import torch.utils.dlpack as tpack
class Envpool(IVecEnv):
def __init__(self, config_name, num_actors, **kwargs):
import envpool
self.batch_size = num_actors
env_name=kwargs.pop('env_name')
self.has_lives = k... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/envs/envpool.py | 0.793546 | 0.182972 | envpool.py | pypi |
import torch
from torch import nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.action_size = 7
self.conv1 = nn.Conv2d(4, 128, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(128)
def forward(self, s):
... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/envs/connect4_network.py | 0.915847 | 0.421909 | connect4_network.py | pypi |
import torch
import rl_games.algos_torch.torch_ext as torch_ext
class DefaultDiagnostics(object):
def __init__(self):
pass
def send_info(self, writter):
pass
def epoch(self, agent, current_epoch):
pass
def mini_epoch(self, agent, miniepoch):
pass
def mini_batch(s... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/diagnostics.py | 0.634317 | 0.234407 | diagnostics.py | pypi |
import time
class IntervalSummaryWriter:
"""
Summary writer wrapper designed to reduce the size of tf.events files.
It will prevent the learner from writing the summaries more often than a specified interval, i.e. if the
current interval is 20 seconds and we wrote our last summary for a particular sum... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/interval_summary_writer.py | 0.730097 | 0.538194 | interval_summary_writer.py | pypi |
from rl_games.algos_torch import torch_ext
import torch
import numpy as np
class AlgoObserver:
def __init__(self):
pass
def before_init(self, base_name, config, experiment_name):
pass
def after_init(self, algo):
pass
def process_infos(self, infos, done_indices):
pass... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/algo_observer.py | 0.783988 | 0.283186 | algo_observer.py | pypi |
from torch import nn
import torch
import math
def critic_loss(value_preds_batch, values, curr_e_clip, return_batch, clip_value):
if clip_value:
value_pred_clipped = value_preds_batch + \
(values - value_preds_batch).clamp(-curr_e_clip, curr_e_clip)
value_losses = (values - return_b... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/common_losses.py | 0.801276 | 0.468608 | common_losses.py | pypi |
import numpy as np
from collections import defaultdict
class LinearValueProcessor:
def __init__(self, start_eps, end_eps, end_eps_frames):
self.start_eps = start_eps
self.end_eps = end_eps
self.end_eps_frames = end_eps_frames
def __call__(self, frame):
if frame >= self.end_... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/tr_helpers.py | 0.569733 | 0.236406 | tr_helpers.py | pypi |
import rl_games.envs.test
from rl_games.common import wrappers
from rl_games.common import tr_helpers
from rl_games.envs.brax import create_brax_env
from rl_games.envs.envpool import create_envpool
import gym
from gym.wrappers import FlattenObservation, FilterObservation
import numpy as np
import math
class HCReward... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/env_configurations.py | 0.520009 | 0.404213 | env_configurations.py | pypi |
import numpy as np
import random
import gym
import torch
from rl_games.common.segment_tree import SumSegmentTree, MinSegmentTree
import torch
from rl_games.algos_torch.torch_ext import numpy_to_torch_dtype_dict
class ReplayBuffer(object):
def __init__(self, size, ob_space):
"""Create Replay buffer.
... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/experience.py | 0.88666 | 0.529081 | experience.py | pypi |
import torch
import copy
from torch.utils.data import Dataset
class PPODataset(Dataset):
def __init__(self, batch_size, minibatch_size, is_discrete, is_rnn, device, seq_len):
self.is_rnn = is_rnn
self.seq_len = seq_len
self.batch_size = batch_size
self.minibatch_size = minibatch_siz... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/datasets.py | 0.635222 | 0.291996 | datasets.py | pypi |
import torch
from torch import nn
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def multiply_hidden(h, mask):
if isinstance(h, to... | /rl_games_y-0.0.8.tar.gz/rl_games_y-0.0.8/rl_games/common/layers/recurrent.py | 0.879244 | 0.37691 | recurrent.py | pypi |
# RL Games: High performance RL library
## Discord Channel Link
* https://discord.gg/hnYRq7DsQh
## Papers and related links
* Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning: https://arxiv.org/abs/2108.10470
* DeXtreme: Transfer of Agile In-Hand Manipulation from Simulation to Reality:... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/README.md | 0.932415 | 0.959687 | README.md | pypi |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class NoisyLinear(nn.Linear):
def __init__(self, in_features, out_features, sigma_init=0.017, bias=True):
super(NoisyLinear, self).__init__(in_features, out_features, bias=bias)
self.sigma_weight = n... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/layers.py | 0.924313 | 0.59305 | layers.py | pypi |
from rl_games.common import a2c_common
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd, RunningMeanStdObs
from rl_games.algos_torch import central_value
from rl_games.common import common_losses
from rl_games.common import datasets
from torch import optim
im... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/a2c_discrete.py | 0.837952 | 0.303545 | a2c_discrete.py | pypi |
from rl_games.common import object_factory
import rl_games.algos_torch
from rl_games.algos_torch import network_builder
from rl_games.algos_torch import models
NETWORK_REGISTRY = {}
MODEL_REGISTRY = {}
def register_network(name, target_class):
NETWORK_REGISTRY[name] = lambda **kwargs: target_class()
def register... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/model_builder.py | 0.676513 | 0.18881 | model_builder.py | pypi |
from rl_games.common import a2c_common
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch import central_value
from rl_games.common import common_losses
from rl_games.common import datasets
from torch import optim
import torch
from torch import nn
import numpy as np
import gym
class A2CAgent(a2c_c... | /rl-games-1.6.0.tar.gz/rl-games-1.6.0/rl_games/algos_torch/a2c_continuous.py | 0.714827 | 0.31781 | a2c_continuous.py | pypi |
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