repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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ray | ray-master/rllib/algorithms/apex_dqn/tests/test_apex_dqn.py | import pytest
import unittest
import ray
import ray.rllib.algorithms.apex_dqn.apex_dqn as apex_dqn
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.metrics.learner_info import LEARNER_INFO, LEARNER_STATS_KEY
from ray.rllib.utils.test_utils import (
check,
check_compute_single_ac... | 5,540 | 33.203704 | 87 | py |
ray | ray-master/rllib/algorithms/marwil/marwil_tf_policy.py | import logging
from typing import Any, Dict, List, Optional, Type, Union
from ray.rllib.evaluation.episode import Episode
from ray.rllib.evaluation.postprocessing import compute_advantages, Postprocessing
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.... | 9,289 | 35.719368 | 88 | py |
ray | ray-master/rllib/algorithms/marwil/marwil.py | from typing import Callable, Optional, Type, Union
from ray.rllib.algorithms.algorithm import Algorithm
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.execution.rollout_ops import (
synchronous_parallel_sample,
)
from ray.rllib.execution.train_ops import (
multi_g... | 11,236 | 38.989324 | 88 | py |
ray | ray-master/rllib/algorithms/marwil/__init__.py | from ray.rllib.algorithms.marwil.marwil import (
MARWIL,
MARWILConfig,
)
from ray.rllib.algorithms.marwil.marwil_tf_policy import (
MARWILTF1Policy,
MARWILTF2Policy,
)
from ray.rllib.algorithms.marwil.marwil_torch_policy import MARWILTorchPolicy
__all__ = [
"MARWIL",
"MARWILConfig",
"MARWIL... | 382 | 20.277778 | 77 | py |
ray | ray-master/rllib/algorithms/marwil/marwil_torch_policy.py | from typing import Dict, List, Type, Union
from ray.rllib.algorithms.marwil.marwil_tf_policy import PostprocessAdvantages
from ray.rllib.evaluation.postprocessing import Postprocessing
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rl... | 5,350 | 39.233083 | 86 | py |
ray | ray-master/rllib/algorithms/marwil/tests/test_marwil.py | import numpy as np
import os
from pathlib import Path
import unittest
import ray
import ray.rllib.algorithms.marwil as marwil
from ray.rllib.algorithms.marwil.marwil_tf_policy import MARWILTF2Policy
from ray.rllib.algorithms.marwil.marwil_torch_policy import MARWILTorchPolicy
from ray.rllib.evaluation.postprocessing i... | 9,219 | 37.577406 | 86 | py |
ray | ray-master/rllib/algorithms/tests/test_algorithm.py | import gymnasium as gym
import numpy as np
import os
from pathlib import Path
from random import choice
import time
import unittest
import ray
import ray.rllib.algorithms.a3c as a3c
import ray.rllib.algorithms.dqn as dqn
from ray.rllib.algorithms.bc import BCConfig
import ray.rllib.algorithms.pg as pg
from ray.rllib.e... | 17,731 | 38.404444 | 88 | py |
ray | ray-master/rllib/algorithms/tests/test_worker_failures.py | from collections import defaultdict
import gymnasium as gym
import numpy as np
import time
import unittest
import ray
from ray.util.state import list_actors
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
from ray.rllib.algorithms.a3c import A3CConfig
from ray.rllib.algorithms.apex_dqn import ApexDQN... | 41,612 | 36.38814 | 88 | py |
ray | ray-master/rllib/algorithms/tests/test_algorithm_config.py | import gym
from typing import Type
import unittest
import ray
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
from ray.rllib.algorithms.callbacks import make_multi_callbacks
from ray.rllib.algorithms.ppo import PPO, PPOConfig
from ray.rllib.algorithms.ppo.tf.ppo_tf_learner import PPOTfLearner
from ra... | 21,050 | 38.128253 | 88 | py |
ray | ray-master/rllib/algorithms/tests/test_algorithm_export_checkpoint.py | import numpy as np
import os
import shutil
import unittest
import ray
from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.test_utils import framework_iter... | 3,809 | 31.288136 | 86 | py |
ray | ray-master/rllib/algorithms/tests/test_callbacks.py | from collections import Counter
import unittest
import ray
from ray.rllib.algorithms.callbacks import DefaultCallbacks, make_multi_callbacks
import ray.rllib.algorithms.dqn as dqn
from ray.rllib.algorithms.pg import PGConfig
from ray.rllib.evaluation.episode import Episode
from ray.rllib.examples.env.random_env import... | 7,958 | 36.542453 | 83 | py |
ray | ray-master/rllib/algorithms/ddpg/ddpg.py | import logging
from typing import List, Optional, Type
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.algorithms.simple_q.simple_q import SimpleQ, SimpleQConfig
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import override
from ray.rllib.util... | 13,701 | 41.290123 | 87 | py |
ray | ray-master/rllib/algorithms/ddpg/ddpg_tf_model.py | import numpy as np
import gymnasium as gym
from typing import List, Optional
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.typing import ModelConfigDict, TensorType
tf1, tf, tfv = try_import_tf()
class DDPGTFModel(TFModelV2):
"""Ext... | 7,518 | 34.635071 | 87 | py |
ray | ray-master/rllib/algorithms/ddpg/utils.py | import gymnasium as gym
import numpy as np
from ray.rllib import Policy
from ray.rllib.algorithms.ddpg.ddpg_tf_model import DDPGTFModel
from ray.rllib.algorithms.ddpg.ddpg_torch_model import DDPGTorchModel
from ray.rllib.algorithms.ddpg.noop_model import NoopModel, TorchNoopModel
from ray.rllib.models import ModelV2
f... | 3,322 | 37.639535 | 84 | py |
ray | ray-master/rllib/algorithms/ddpg/ddpg_torch_policy.py | import logging
import gymnasium as gym
from typing import Dict, Tuple, List, Optional, Any, Type
import ray
from ray.rllib.algorithms.dqn.dqn_tf_policy import (
postprocess_nstep_and_prio,
PRIO_WEIGHTS,
)
from ray.rllib.evaluation import Episode
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.model... | 13,123 | 36.073446 | 88 | py |
ray | ray-master/rllib/algorithms/ddpg/ddpg_torch_model.py | import numpy as np
import gymnasium as gym
from typing import List, Dict, Union, Optional
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.utils import get_activation_fn
from ray.rllib.utils.framework import try_import_torch
from ray.rll... | 8,600 | 35.444915 | 87 | py |
ray | ray-master/rllib/algorithms/ddpg/ddpg_tf_policy.py | from functools import partial
import logging
import gymnasium as gym
from typing import Dict, Tuple, List, Type, Union, Optional, Any
from ray.rllib.algorithms.ddpg.utils import make_ddpg_models, validate_spaces
from ray.rllib.algorithms.dqn.dqn_tf_policy import (
postprocess_nstep_and_prio,
PRIO_WEIGHTS,
)
fr... | 16,714 | 39.277108 | 88 | py |
ray | ray-master/rllib/algorithms/ddpg/noop_model.py | from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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
_, tf, _ = try_import_tf()
class NoopModel(TFModelV2):
... | 872 | 30.178571 | 65 | py |
ray | ray-master/rllib/algorithms/ddpg/tests/test_ddpg.py | import copy
import re
import unittest
import numpy as np
import ray
import ray.rllib.algorithms.ddpg as ddpg
from ray.rllib.algorithms.sac.tests.test_sac import SimpleEnv
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.nu... | 24,149 | 39.317195 | 88 | py |
ray | ray-master/rllib/algorithms/slateq/slateq_torch_model.py | from typing import List, Sequence
import gymnasium as gym
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import ModelConfigDict, TensorType
torch, nn = try_import_torch()
F ... | 6,840 | 35.77957 | 88 | py |
ray | ray-master/rllib/algorithms/slateq/slateq_torch_policy.py | """PyTorch policy class used for SlateQ."""
import gymnasium as gym
import logging
import numpy as np
from typing import Dict, Tuple, Type
import ray
from ray.rllib.algorithms.slateq.slateq_torch_model import SlateQTorchModel
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist i... | 16,150 | 35.706818 | 88 | py |
ray | ray-master/rllib/algorithms/slateq/slateq.py | """
SlateQ (Reinforcement Learning for Recommendation)
==================================================
This file defines the algorithm class for the SlateQ algorithm from the
`"Reinforcement Learning for Slate-based Recommender Systems: A Tractable
Decomposition and Practical Methodology" <https://arxiv.org/abs/190... | 11,101 | 42.708661 | 88 | py |
ray | ray-master/rllib/algorithms/slateq/slateq_tf_policy.py | """TensorFlow policy class used for SlateQ."""
import functools
import gymnasium as gym
import logging
import numpy as np
from typing import Dict
import ray
from ray.rllib.algorithms.dqn.dqn_tf_policy import clip_gradients
from ray.rllib.algorithms.slateq.slateq_tf_model import SlateQTFModel
from ray.rllib.models.mod... | 13,114 | 34.160858 | 88 | py |
ray | ray-master/rllib/algorithms/slateq/slateq_tf_model.py | """Tensorflow model for SlateQ"""
from typing import List
import gymnasium as gym
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.typing import ModelConfigDict, TensorType
tf1, tf, tfv = try_import_tf()
class SlateQTFModel(TFModelV2):
... | 2,877 | 35.897436 | 87 | py |
ray | ray-master/rllib/algorithms/slateq/__init__.py | from ray.rllib.algorithms.slateq.slateq import (
SlateQ,
SlateQConfig,
)
from ray.rllib.algorithms.slateq.slateq_tf_policy import SlateQTFPolicy
from ray.rllib.algorithms.slateq.slateq_torch_policy import SlateQTorchPolicy
__all__ = [
"SlateQ",
"SlateQConfig",
"SlateQTFPolicy",
"SlateQTorchPoli... | 327 | 22.428571 | 77 | py |
ray | ray-master/rllib/algorithms/ppo/ppo_catalog.py | import gymnasium as gym
from ray.rllib.core.models.catalog import Catalog
from ray.rllib.core.models.configs import (
ActorCriticEncoderConfig,
MLPHeadConfig,
FreeLogStdMLPHeadConfig,
)
from ray.rllib.core.models.base import Encoder, ActorCriticEncoder, Model
from ray.rllib.utils import override
def _che... | 6,856 | 38.635838 | 88 | py |
ray | ray-master/rllib/algorithms/ppo/ppo_tf_policy.py | """
TensorFlow policy class used for PPO.
"""
import logging
from typing import Dict, List, Type, Union
from ray.rllib.evaluation.postprocessing import (
Postprocessing,
compute_gae_for_sample_batch,
)
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_action_dist import TFActionDistribu... | 8,775 | 36.186441 | 87 | py |
ray | ray-master/rllib/algorithms/ppo/ppo.py | """
Proximal Policy Optimization (PPO)
==================================
This file defines the distributed Algorithm class for proximal policy
optimization.
See `ppo_[tf|torch]_policy.py` for the definition of the policy loss.
Detailed documentation: https://docs.ray.io/en/master/rllib-algorithms.html#ppo
"""
impor... | 24,150 | 41.44464 | 88 | py |
ray | ray-master/rllib/algorithms/ppo/ppo_torch_policy.py | import logging
from typing import Dict, List, Type, Union
import ray
from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
from ray.rllib.evaluation.postprocessing import (
Postprocessing,
compute_gae_for_sample_batch,
)
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.mo... | 7,784 | 34.711009 | 88 | py |
ray | ray-master/rllib/algorithms/ppo/__init__.py | from ray.rllib.algorithms.ppo.ppo import PPOConfig, PPO
from ray.rllib.algorithms.ppo.ppo_tf_policy import PPOTF1Policy, PPOTF2Policy
from ray.rllib.algorithms.ppo.ppo_torch_policy import PPOTorchPolicy
__all__ = [
"PPOConfig",
"PPOTF1Policy",
"PPOTF2Policy",
"PPOTorchPolicy",
"PPO",
]
| 308 | 24.75 | 77 | py |
ray | ray-master/rllib/algorithms/ppo/torch/ppo_torch_learner.py | import logging
from typing import Any, Dict, Mapping
from ray.rllib.algorithms.ppo.ppo_learner import (
LEARNER_RESULTS_KL_KEY,
LEARNER_RESULTS_CURR_KL_COEFF_KEY,
LEARNER_RESULTS_VF_EXPLAINED_VAR_KEY,
LEARNER_RESULTS_VF_LOSS_UNCLIPPED_KEY,
PPOLearner,
PPOLearnerHyperparameters,
)
from ray.rllib... | 6,613 | 36.794286 | 88 | py |
ray | ray-master/rllib/algorithms/ppo/torch/ppo_torch_rl_module.py | from typing import Mapping, Any
from ray.rllib.algorithms.ppo.ppo_rl_module import PPORLModule
from ray.rllib.core.models.base import ACTOR, CRITIC, ENCODER_OUT, STATE_OUT
from ray.rllib.core.rl_module.rl_module import RLModule
from ray.rllib.core.rl_module.torch import TorchRLModule
from ray.rllib.policy.sample_batc... | 2,513 | 32.52 | 87 | py |
ray | ray-master/rllib/algorithms/ppo/tests/test_repro_ppo.py | import unittest
import pytest
import ray
from ray.tune import register_env
import ray.rllib.algorithms.ppo as ppo
from ray.rllib.examples.env.deterministic_envs import (
create_cartpole_deterministic,
create_pendulum_deterministic,
)
from ray.rllib.utils.test_utils import check_reproducibilty
class TestRepro... | 1,981 | 29.96875 | 81 | py |
ray | ray-master/rllib/algorithms/ppo/tests/test_ppo_rl_module.py | import itertools
import unittest
import gymnasium as gym
import numpy as np
import tree
import ray
from ray.rllib import SampleBatch
from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
from ray.rllib.algorithms.ppo.tf.ppo_tf_rl_module import (
PPOTfRLModule,
)
from ray.rllib.algorithms.ppo.torch.ppo_torch... | 11,477 | 35.208202 | 85 | py |
ray | ray-master/rllib/algorithms/ppo/tests/test_ppo_with_rl_module.py | import unittest
import numpy as np
import ray
import ray.rllib.algorithms.ppo as ppo
from ray.rllib.algorithms.ppo.ppo_learner import (
LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY,
)
from ray.rllib.algorithms.callbacks import DefaultCallbacks
from ray.rllib.algorithms.ppo.tests.test_ppo import PENDULUM_FAKE_BATCH
from... | 8,725 | 34.471545 | 88 | py |
ray | ray-master/rllib/algorithms/ppo/tests/test_ppo_learner.py | import ray
import unittest
import numpy as np
import torch
import tempfile
import tensorflow as tf
import tree # pip install dm-tree
import ray.rllib.algorithms.ppo as ppo
from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
from ray.rllib.algorithms.ppo.ppo_learner import LEARNER_RESULTS_CURR_KL_COEFF_KEY
fr... | 8,339 | 35.26087 | 85 | py |
ray | ray-master/rllib/algorithms/ppo/tests/test_ppo.py | import unittest
import numpy as np
import ray
from ray.rllib.algorithms.callbacks import DefaultCallbacks
import ray.rllib.algorithms.ppo as ppo
from ray.rllib.algorithms.ppo.ppo_tf_policy import PPOTF2Policy
from ray.rllib.algorithms.ppo.ppo_torch_policy import PPOTorchPolicy
from ray.rllib.evaluation.postprocessing... | 22,989 | 37.444816 | 88 | py |
ray | ray-master/rllib/algorithms/simple_q/simple_q_torch_policy.py | """PyTorch policy class used for Simple Q-Learning"""
import logging
from typing import Any, Dict, List, Tuple, Type, Union
from ray.rllib.algorithms.simple_q.utils import make_q_models
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist import (
TorchCategorical,
TorchD... | 6,577 | 35.142857 | 87 | py |
ray | ray-master/rllib/algorithms/simple_q/simple_q.py | """
Simple Q-Learning
=================
This module provides a basic implementation of the DQN algorithm without any
optimizations.
This file defines the distributed Algorithm class for the Simple Q algorithm.
See `simple_q_[tf|torch]_policy.py` for the definition of the policy loss.
"""
import logging
from typing i... | 16,298 | 39.952261 | 88 | py |
ray | ray-master/rllib/algorithms/simple_q/simple_q_tf_policy.py | """TensorFlow policy class used for Simple Q-Learning"""
import logging
from typing import Dict, List, Tuple, Type, Union
from ray.rllib.algorithms.simple_q.utils import make_q_models
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_action_dist import Categorical, TFActionDistribution
from ray... | 8,378 | 35.58952 | 88 | py |
ray | ray-master/rllib/algorithms/simple_q/__init__.py | from ray.rllib.algorithms.simple_q.simple_q import (
SimpleQ,
SimpleQConfig,
)
from ray.rllib.algorithms.simple_q.simple_q_tf_policy import (
SimpleQTF1Policy,
SimpleQTF2Policy,
)
from ray.rllib.algorithms.simple_q.simple_q_torch_policy import SimpleQTorchPolicy
__all__ = [
"SimpleQ",
"SimpleQC... | 404 | 21.5 | 82 | py |
ray | ray-master/rllib/algorithms/simple_q/tests/test_simple_q.py | import unittest
import numpy as np
import ray
import ray.rllib.algorithms.simple_q as simple_q
from ray.rllib.algorithms.simple_q.simple_q_tf_policy import SimpleQTF2Policy
from ray.rllib.algorithms.simple_q.simple_q_torch_policy import SimpleQTorchPolicy
from ray.rllib.policy.sample_batch import SampleBatch
from ray... | 7,404 | 34.946602 | 87 | py |
ray | ray-master/rllib/algorithms/simple_q/tests/test_repro_simple_q.py | import unittest
import ray
from ray.tune import register_env
import ray.rllib.algorithms.simple_q as simple_q
from ray.rllib.examples.env.deterministic_envs import create_cartpole_deterministic
from ray.rllib.utils.test_utils import check_reproducibilty
class TestReproSimpleQ(unittest.TestCase):
@classmethod
... | 1,115 | 26.9 | 83 | py |
ray | ray-master/rllib/algorithms/maml/maml.py | import logging
import numpy as np
from typing import Optional, Type
from ray.rllib.algorithms.algorithm import Algorithm
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.evaluation.worker_set import WorkerSet
from r... | 14,885 | 36.215 | 88 | py |
ray | ray-master/rllib/algorithms/maml/maml_tf_policy.py | import logging
from typing import Dict, List, Type, Union
from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
from ray.rllib.evaluation.postprocessing import (
Postprocessing,
compute_gae_for_sample_batch,
)
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_action_dist imp... | 19,314 | 36.001916 | 88 | py |
ray | ray-master/rllib/algorithms/maml/maml_torch_policy.py | import logging
from typing import Dict, List, Type, Union
import ray
from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
from ray.rllib.evaluation.postprocessing import (
Postprocessing,
compute_gae_for_sample_batch,
)
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torc... | 16,282 | 35.184444 | 88 | py |
ray | ray-master/rllib/algorithms/maml/tests/test_maml.py | from gymnasium.wrappers import TimeLimit
import unittest
import ray
import ray.rllib.algorithms.maml as maml
from ray.rllib.examples.env.cartpole_mass import CartPoleMassEnv
from ray.rllib.examples.env.pendulum_mass import PendulumMassEnv
from ray.rllib.utils.test_utils import (
check_compute_single_action,
ch... | 1,839 | 29.666667 | 82 | py |
ray | ray-master/rllib/algorithms/dqn/dqn.py | """
Deep Q-Networks (DQN, Rainbow, Parametric DQN)
==============================================
This file defines the distributed Algorithm class for the Deep Q-Networks
algorithm. See `dqn_[tf|torch]_policy.py` for the definition of the policies.
Detailed documentation:
https://docs.ray.io/en/master/rllib-algorith... | 20,241 | 41.614737 | 94 | py |
ray | ray-master/rllib/algorithms/dqn/dqn_torch_policy.py | """PyTorch policy class used for DQN"""
from typing import Dict, List, Tuple
import gymnasium as gym
import ray
from ray.rllib.algorithms.dqn.dqn_tf_policy import (
PRIO_WEIGHTS,
Q_SCOPE,
Q_TARGET_SCOPE,
postprocess_nstep_and_prio,
)
from ray.rllib.algorithms.dqn.dqn_torch_model import DQNTorchModel
f... | 17,063 | 32.656805 | 88 | py |
ray | ray-master/rllib/algorithms/dqn/learner_thread.py | import queue
import threading
from ray.util.timer import _Timer
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.metrics.learner_info import LearnerInfoBuilder
from ray.rllib.utils.metrics.window_stat import WindowStat
LEARNER_QUEUE_MAX_SIZE = 16
tf1, tf, tfv = try_import_tf()
class Learner... | 3,678 | 43.325301 | 85 | py |
ray | ray-master/rllib/algorithms/dqn/__init__.py | from ray.rllib.algorithms.dqn.dqn import DQN, DQNConfig
from ray.rllib.algorithms.dqn.dqn_tf_policy import DQNTFPolicy
from ray.rllib.algorithms.dqn.dqn_torch_policy import DQNTorchPolicy
__all__ = [
"DQN",
"DQNConfig",
"DQNTFPolicy",
"DQNTorchPolicy",
]
| 272 | 23.818182 | 68 | py |
ray | ray-master/rllib/algorithms/dqn/dqn_torch_model.py | """PyTorch model for DQN"""
from typing import Sequence
import gymnasium as gym
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.modules.noisy_layer import NoisyLayer
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.framework import try_import_torch
from ray.... | 6,798 | 38.074713 | 88 | py |
ray | ray-master/rllib/algorithms/dqn/dqn_tf_policy.py | """TensorFlow policy class used for DQN"""
from typing import Dict
import gymnasium as gym
import numpy as np
import ray
from ray.rllib.algorithms.dqn.distributional_q_tf_model import DistributionalQTFModel
from ray.rllib.algorithms.simple_q.utils import Q_SCOPE, Q_TARGET_SCOPE
from ray.rllib.evaluation.postprocessi... | 17,499 | 34 | 87 | py |
ray | ray-master/rllib/algorithms/dqn/distributional_q_tf_model.py | """Tensorflow model for DQN"""
from typing import List
import gymnasium as gym
from ray.rllib.models.tf.layers import NoisyLayer
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.typing import ModelConfigDict, TensorType
tf1, tf, tfv = try_i... | 7,954 | 41.089947 | 88 | py |
ray | ray-master/rllib/algorithms/dqn/tests/test_repro_dqn.py | import unittest
import os
import ray
from ray.tune import register_env
import ray.rllib.algorithms.dqn as dqn
from ray.rllib.examples.env.deterministic_envs import create_cartpole_deterministic
from ray.rllib.utils.test_utils import check_reproducibilty
class TestReproDQN(unittest.TestCase):
@classmethod
de... | 1,394 | 27.469388 | 83 | py |
ray | ray-master/rllib/algorithms/crr/crr.py | import logging
from typing import List, Optional, Type
from ray.rllib.algorithms.algorithm import Algorithm
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.execution import synchronous_parallel_sample
from ray.rllib.execution.train_ops import multi_gpu_train_one_step, trai... | 11,648 | 40.455516 | 87 | py |
ray | ray-master/rllib/algorithms/crr/torch/crr_torch_model.py | import gymnasium as gym
import numpy as np
from typing import Union, List, Dict
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.utils import get_activation_fn
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.t... | 6,584 | 32.090452 | 88 | py |
ray | ray-master/rllib/algorithms/crr/torch/crr_torch_policy.py | import gymnasium as gym
import numpy as np
from typing import (
cast,
Dict,
List,
Tuple,
Type,
Union,
)
from ray.rllib.algorithms import AlgorithmConfig
from ray.rllib.algorithms.crr.torch import CRRModel
from ray.rllib.algorithms.ddpg.noop_model import TorchNoopModel
from ray.rllib.models.cat... | 16,214 | 36.448037 | 88 | py |
ray | ray-master/rllib/algorithms/crr/torch/__init__.py | from .crr_torch_model import CRRModel
from .crr_torch_policy import CRRTorchPolicy
__all__ = ["CRRModel", "CRRTorchPolicy"]
| 125 | 24.2 | 44 | py |
ray | ray-master/rllib/algorithms/crr/tests/test_crr.py | from pathlib import Path
import os
import unittest
import ray
from ray.rllib.algorithms.crr import CRRConfig
from ray.rllib.offline.json_reader import JsonReader
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.test_utils import (
check_compute_single_action,
check_tra... | 3,354 | 31.892157 | 87 | py |
ray | ray-master/rllib/algorithms/dreamerv3/dreamerv3.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
[2] Mastering Atari with Discrete World Models - 2021
D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
https://arxiv.org/pdf/2010.02193.pdf
"""
import dataclasses
import gc
import... | 30,743 | 45.511346 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/dreamerv3_tf_learner.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
[2] Mastering Atari with Discrete World Models - 2021
D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
https://arxiv.org/pdf/2010.02193.pdf
"""
from typing import Any, Dict, Mappi... | 37,408 | 41.222348 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/dreamerv3_tf_rl_module.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
[2] Mastering Atari with Discrete World Models - 2021
D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
https://arxiv.org/pdf/2010.02193.pdf
"""
from typing import Mapping, Any
fr... | 2,332 | 37.883333 | 87 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/actor_network.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
import gymnasium as gym
from gymnasium.spaces import Box, Discrete
import numpy as np
from ray.rllib.algorithms.dreamerv3.tf.models.componen... | 7,370 | 40.644068 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/disagree_networks.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
from ray.rllib.algorithms.dreamerv3.tf.models.components.representation_layer import (
Repres... | 3,672 | 36.479592 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/dreamer_model.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
import re
import gymnasium as gym
import numpy as np
from ray.rllib.algorithms.dreamerv3.tf.models.disagree_networks import DisagreeNetworks
from ray.rllib.algorithms.d... | 24,330 | 41.536713 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/critic_network.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
from ray.rllib.algorithms.dreamerv3.tf.models.components.reward_predi... | 6,567 | 43.080537 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/world_model.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
import gymnasium as gym
import tree # pip install dm_tree
from ray.rllib.algorithms.dreamerv3.tf.models.components.continue_predictor impor... | 18,063 | 44.501259 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/continue_predictor.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
from ray.rllib.utils.framework import try_import_tf, try_import_tfp
... | 2,910 | 40 | 87 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/reward_predictor_layer.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
[2] Mastering Atari with Discrete World Models - 2021
D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
https://arxiv.org/pdf/2010.02193.pdf
"""
from ray.rllib.utils.framework impo... | 4,961 | 42.147826 | 87 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/representation_layer.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
[2] Mastering Atari with Discrete World Models - 2021
D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
https://arxiv.org/pdf/2010.02193.pdf
"""
from typing import Optional
from r... | 5,670 | 42.623077 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/dynamics_predictor.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
from ray.rllib.algorithms.dreamerv3.tf.models.components.representati... | 2,884 | 37.986486 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/reward_predictor.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
from ray.rllib.algorithms.dreamerv3.tf.models.components.reward_predi... | 3,394 | 38.941176 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/conv_transpose_atari.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
[2] Mastering Atari with Discrete World Models - 2021
D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
https://arxiv.org/pdf/2010.02193.pdf
"""
from typing import Optional
import... | 6,265 | 39.166667 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/mlp.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
[2] Mastering Atari with Discrete World Models - 2021
D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
https://arxiv.org/pdf/2010.02193.pdf
"""
from typing import Optional
from r... | 3,647 | 33.742857 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/cnn_atari.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
from ray.rllib.algorithms.dreamerv3.utils import get_cnn_multiplier
from ray.rllib.utils.framework import try_import_tf
_, tf, _ = try_impor... | 3,951 | 37 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/sequence_model.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
import gymnasium as gym
from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
from ray.rllib.algorithms.dreamerv3.utils im... | 4,070 | 39.306931 | 88 | py |
ray | ray-master/rllib/algorithms/dreamerv3/tf/models/components/vector_decoder.py | """
[1] Mastering Diverse Domains through World Models - 2023
D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
https://arxiv.org/pdf/2301.04104v1.pdf
"""
from typing import Optional
import gymnasium as gym
from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
from ray.rllib.utils.framework import try_im... | 2,216 | 31.602941 | 83 | py |
ray | ray-master/rllib/algorithms/bandit/bandit.py | import logging
from typing import Optional, Type, Union
from ray.rllib.algorithms.algorithm import Algorithm
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
from ray.rllib.algorithms.bandit.bandit_tf_policy import BanditTFPolicy
from ray.rllib.algorithms.bandit.bandit_torch_policy import BanditTorchP... | 5,044 | 36.095588 | 88 | py |
ray | ray-master/rllib/algorithms/bandit/bandit_torch_policy.py | import logging
import time
from gymnasium import spaces
from ray.rllib.algorithms.bandit.bandit_torch_model import (
DiscreteLinearModelThompsonSampling,
DiscreteLinearModelUCB,
DiscreteLinearModel,
ParametricLinearModelThompsonSampling,
ParametricLinearModelUCB,
)
from ray.rllib.models.catalog imp... | 3,893 | 35.735849 | 86 | py |
ray | ray-master/rllib/algorithms/bandit/bandit_torch_model.py | import gymnasium as gym
from ray.rllib.models.modelv2 import ModelV2
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.rllib.utils.typing import TensorType
torch, nn = try_import_torch()
clas... | 10,300 | 34.643599 | 87 | py |
ray | ray-master/rllib/algorithms/bandit/tests/test_bandits.py | import gymnasium as gym
from gymnasium.spaces import Discrete, Box
import numpy as np
import unittest
import ray
from ray.rllib.algorithms.bandit.bandit import BanditLinTSConfig, BanditLinUCBConfig
from ray.rllib.examples.env.bandit_envs_discrete import SimpleContextualBandit
from ray.rllib.env import EnvContext
from ... | 5,352 | 37.235714 | 88 | py |
ray | ray-master/rllib/algorithms/ars/ars.py | # Code in this file is copied and adapted from
# https://github.com/openai/evolution-strategies-starter and from
# https://github.com/modestyachts/ARS
from collections import namedtuple
import logging
import numpy as np
import random
import time
from typing import Optional
import ray
from ray.rllib.algorithms import ... | 23,220 | 35.800317 | 87 | py |
ray | ray-master/rllib/algorithms/ars/ars_torch_policy.py | # Code in this file is adapted from:
# https://github.com/openai/evolution-strategies-starter.
import ray
from ray.rllib.algorithms.es.es_torch_policy import (
after_init,
before_init,
make_model_and_action_dist,
)
from ray.rllib.policy.policy_template import build_policy_class
ARSTorchPolicy = build_poli... | 588 | 27.047619 | 72 | py |
ray | ray-master/rllib/algorithms/ars/__init__.py | from ray.rllib.algorithms.ars.ars import ARS, ARSConfig
from ray.rllib.algorithms.ars.ars_tf_policy import ARSTFPolicy
from ray.rllib.algorithms.ars.ars_torch_policy import ARSTorchPolicy
__all__ = [
"ARS",
"ARSConfig",
"ARSTFPolicy",
"ARSTorchPolicy",
]
| 272 | 23.818182 | 68 | py |
ray | ray-master/rllib/algorithms/dt/dt.py | import logging
import math
from typing import List, Optional, Type, Tuple, Dict, Any, Union
from ray.rllib import SampleBatch
from ray.rllib.algorithms.algorithm import Algorithm
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.algorithms.dt.segmentation_buffer import Multi... | 18,182 | 39.227876 | 88 | py |
ray | ray-master/rllib/algorithms/dt/dt_torch_model.py | import gymnasium as gym
from gymnasium.spaces import Discrete, Box
import numpy as np
from typing import Dict, List
from ray.rllib import SampleBatch
from ray.rllib.models import ModelV2
from ray.rllib.models.torch.mingpt import (
GPTConfig,
GPT,
)
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
... | 9,238 | 37.020576 | 87 | py |
ray | ray-master/rllib/algorithms/dt/dt_torch_policy.py | import gymnasium as gym
import numpy as np
from typing import (
Dict,
List,
Tuple,
Type,
Union,
Optional,
Any,
TYPE_CHECKING,
)
import tree
from gymnasium.spaces import Discrete, Box
from ray.rllib.algorithms.dt.dt_torch_model import DTTorchModel
from ray.rllib.models.catalog import M... | 21,142 | 35.018739 | 87 | py |
ray | ray-master/rllib/algorithms/dt/tests/test_dt.py | from pathlib import Path
import os
import unittest
from typing import Dict
import gymnasium as gym
import numpy as np
import ray
from ray.rllib import SampleBatch
from ray.rllib.algorithms.dt import DTConfig
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.test_utils import c... | 8,528 | 30.127737 | 86 | py |
ray | ray-master/rllib/algorithms/dt/tests/test_dt_policy.py | import unittest
from typing import Dict
import gymnasium as gym
import numpy as np
import ray
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.algorithms.dt.dt_torch_policy import DTTorchPolicy
tf1, tf, tfv = try_import_tf()
to... | 19,560 | 37.130604 | 88 | py |
ray | ray-master/rllib/algorithms/dt/tests/test_segmentation_buffer.py | import unittest
from typing import Union, List
import numpy as np
import ray
from ray.rllib.algorithms.dt.segmentation_buffer import (
SegmentationBuffer,
MultiAgentSegmentationBuffer,
)
from ray.rllib.policy.sample_batch import (
SampleBatch,
MultiAgentBatch,
concat_samples,
DEFAULT_POLICY_ID... | 15,613 | 36.087886 | 87 | py |
ray | ray-master/rllib/algorithms/dt/tests/test_dt_model.py | import unittest
import gymnasium as gym
import numpy as np
import ray
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.torch_utils import convert_to_torch_tensor
from ray.... | 10,457 | 33.629139 | 87 | py |
ray | ray-master/rllib/algorithms/qmix/qmix.py | from typing import Optional, Type
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.algorithms.simple_q.simple_q import SimpleQ, SimpleQConfig
from ray.rllib.algorithms.qmix.qmix_policy import QMixTorchPolicy
from ray.rllib.utils.replay_buffers.utils import update_priorities... | 13,196 | 38.044379 | 88 | py |
ray | ray-master/rllib/algorithms/qmix/model.py | from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.preprocessors import get_preprocessor
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
torch, nn = try_import_torch()
class RNNModel(... | 1,580 | 35.767442 | 86 | py |
ray | ray-master/rllib/algorithms/qmix/qmix_policy.py | import gymnasium as gym
import logging
import numpy as np
import tree # pip install dm_tree
from typing import Dict, List, Optional, Tuple
from ray.rllib.algorithms.qmix.mixers import VDNMixer, QMixer
from ray.rllib.algorithms.qmix.model import RNNModel, _get_size
from ray.rllib.env.multi_agent_env import ENV_STATE
f... | 23,840 | 35.122727 | 88 | py |
ray | ray-master/rllib/algorithms/qmix/mixers.py | import numpy as np
from ray.rllib.utils.framework import try_import_torch
torch, nn = try_import_torch()
class VDNMixer(nn.Module):
def __init__(self):
super(VDNMixer, self).__init__()
def forward(self, agent_qs, batch):
return torch.sum(agent_qs, dim=2, keepdim=True)
class QMixer(nn.Modu... | 2,042 | 31.428571 | 82 | py |
ray | ray-master/rllib/algorithms/qmix/tests/test_qmix.py | from gymnasium.spaces import Box, Dict, Discrete, MultiDiscrete, Tuple
import numpy as np
import unittest
import ray
from ray.tune import register_env
from ray.rllib.algorithms.qmix import QMixConfig
from ray.rllib.env.multi_agent_env import MultiAgentEnv
class AvailActionsTestEnv(MultiAgentEnv):
num_actions = 1... | 3,626 | 28.25 | 84 | py |
ray | ray-master/rllib/algorithms/alpha_zero/alpha_zero.py | import logging
from typing import List, Optional, Type, Union
from ray.rllib.algorithms.callbacks import DefaultCallbacks
from ray.rllib.algorithms.algorithm import Algorithm
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.execution.rollout_ops import (
synchronous_par... | 17,158 | 39.75772 | 88 | py |
ray | ray-master/rllib/algorithms/alpha_zero/alpha_zero_policy.py | import numpy as np
from ray.rllib.algorithms.alpha_zero.mcts import Node, RootParentNode
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.torch_policy import TorchPolicy
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.metrics.... | 5,760 | 35.232704 | 85 | py |
ray | ray-master/rllib/algorithms/alpha_zero/models/custom_torch_models.py | from abc import ABC
import numpy as np
from ray.rllib.models.modelv2 import restore_original_dimensions
from ray.rllib.models.preprocessors import get_preprocessor
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.framework import try_import_torch
torch, nn = try_import_torch()
def ... | 3,744 | 31.284483 | 86 | py |
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