id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
28,933 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Fetches and converts any DeviceArrays to np.ndarrays. |
28,934 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | null |
28,935 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Tiles tensors in a nested structure along a new leading axis. |
28,936 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Recovers the type hk.LSTMState if LSTMState is in the type name. When the recurrent state of recurrent neural networks (RNN) is deserialized, for example when it is sampled from replay, it is sometimes repacked in a type that is identical to the source type but not the correct type itself. When using this state as the ... |
28,937 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Returns PrefetchingSplit which keeps uint64 reverb key on the host. We want to avoid truncation of the uint64 reverb key by JAX. Args: sample: a sample from a Reverb replay buffer. Returns: PrefetchingSplit with device having the reverb sample, and key on host. |
28,938 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Returns iterator that samples an item and places it on the device. |
28,939 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Returns iterator that, per device, samples an item and places on device. |
28,940 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Replicate array nest in all available devices. |
28,941 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | Gets the first array of a nest of `jax.Array`s. Args: nest: A nest of `jax.Array`s. as_numpy: If `True` then each `DeviceArray` that is retrieved is transformed (and copied if not on the host machine) into a `np.ndarray`. Returns: The first array of a nest of `jax.Array`s. Note that if `as_numpy=False` then the array w... |
28,942 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | The version of 'process_multiple_batches' with stronger typing. |
28,943 | import functools
import itertools
import queue
import threading
from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
from absl import logging
from acme import core
from acme import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as ... | null |
28,944 | import dataclasses
from typing import Any, Optional, Tuple, Union
from acme import types
from acme.utils import tree_utils
import chex
import jax
import jax.numpy as jnp
import numpy as np
import tree
Path = Tuple[Any, ...]
def _is_prefix(a: Path, b: Path) -> bool:
"""Returns whether `a` is a prefix of `b`."""
retu... | Returns the config for a subtree from the leaf defined by the path. |
28,945 | import os
import time
from typing import Callable, Dict, List, Optional, Sequence, Tuple
from absl import logging
from acme import core
from acme.jax import types
from acme.utils import signals
from acme.utils import paths
from jax.experimental import jax2tf
import tensorflow as tf
def model_to_tf_module(model: types.... | null |
28,946 | import datetime
import os
import pickle
from typing import Any
from absl import logging
from acme import core
from acme.tf import savers as tf_savers
import jax
import numpy as np
import tree
CheckpointState = Any
_ARRAY_NAME = 'array_nest'
_EXEMPLAR_NAME = 'nest_exemplar'
The provided code snippet includes necessary ... | Restore the state stored in ckpt_dir. |
28,947 | import datetime
import os
import pickle
from typing import Any
from absl import logging
from acme import core
from acme.tf import savers as tf_savers
import jax
import numpy as np
import tree
CheckpointState = Any
_ARRAY_NAME = 'array_nest'
_EXEMPLAR_NAME = 'nest_exemplar'
The provided code snippet includes necessary ... | Save the state in ckpt_dir. |
28,948 | from typing import Any, Mapping, NamedTuple, Tuple
from acme import specs
from acme import types as acme_types
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.jax import utils as jax_utils
from acme.tf import utils ... | null |
28,949 | from typing import Any, Mapping, NamedTuple, Tuple
from acme import specs
from acme import types as acme_types
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.jax import utils as jax_utils
from acme.tf import utils ... | Returns a spec where the observation spec accounts for stacking. |
28,950 | from typing import Any, Mapping, NamedTuple, Tuple
from acme import specs
from acme import types as acme_types
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.jax import utils as jax_utils
from acme.tf import utils ... | Wraps an actor core so that it performs observation stacking. |
28,951 | from typing import Any, Mapping, NamedTuple, Tuple
from acme import specs
from acme import types as acme_types
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.jax import utils as jax_utils
from acme.tf import utils ... | Stacks observations in a Reverb sample. This function is meant to be used on the dataset creation side as a post-processing function before batching. Warnings! * Only works if SequenceAdder is in end_of_episode_behavior=CONTINUE mode. * Only tested on RGB and scalar (shape = (1,)) observations. * At episode starts, thi... |
28,952 | import acme
from acme import specs
from acme.jax.experiments import config
from acme.tf import savers
from acme.utils import counting
import jax
The provided code snippet includes necessary dependencies for implementing the `run_offline_experiment` function. Write a Python function `def run_offline_experiment(experime... | Runs a simple, single-threaded training loop using the default evaluators. It targets simplicity of the code and so only the basic features of the OfflineExperimentConfig are supported. Arguments: experiment: Definition and configuration of the agent to run. eval_every: After how many learner steps to perform evaluatio... |
28,953 | import dataclasses
import datetime
from typing import Any, Callable, Dict, Generic, Iterator, Optional, Sequence
from acme import core
from acme import environment_loop
from acme import specs
from acme.agents.jax import builders
from acme.jax import types
from acme.jax import utils
from acme.utils import counting
from ... | Returns a default evaluator process. |
28,954 | import sys
import time
from typing import Optional, Sequence, Tuple
import acme
from acme import core
from acme import specs
from acme import types
from acme.jax import utils
from acme.jax.experiments import config
from acme.tf import savers
from acme.utils import counting
import dm_env
import jax
import reverb
class _... | Runs a simple, single-threaded training loop using the default evaluators. It targets simplicity of the code and so only the basic features of the ExperimentConfig are supported. Arguments: experiment: Definition and configuration of the agent to run. eval_every: After how many actor steps to perform evaluation. num_ev... |
28,955 | import itertools
import math
from typing import Any, List, Optional
from acme import core
from acme import environment_loop
from acme import specs
from acme.agents.jax import actor_core
from acme.agents.jax import builders
from acme.jax import inference_server as inference_server_lib
from acme.jax import networks as ne... | Builds a Launchpad program for running the experiment. Args: experiment: configuration of the experiment. num_actors: number of actors to run. inference_server_config: If provided we will attempt to use `num_inference_servers` inference servers for selecting actions. There are two assumptions if this config is provided... |
28,956 | from typing import Any, Optional
from acme import core
from acme import specs
from acme.agents.jax import builders
from acme.jax import networks as networks_lib
from acme.jax import savers
from acme.jax import utils
from acme.jax.experiments import config
from acme.jax import snapshotter
from acme.utils import counting... | Builds a Launchpad program for running the experiment. Args: experiment: configuration for the experiment. make_snapshot_models: a factory that defines what is saved in snapshots. name: name of the constructed program. Ignored if an existing program is passed. program: a program where agent nodes are added to. If None,... |
28,957 | from typing import Any, Optional, Sequence, Tuple
from acme.jax.networks import base
from acme.jax.networks import duelling
from acme.jax.networks import embedding
from acme.jax.networks import policy_value
from acme.jax.networks import resnet
from acme.wrappers import observation_action_reward
import haiku as hk
impor... | A feed-forward network for use with Ape-X DQN. |
28,958 | import dataclasses
from typing import Callable, Optional, Tuple
from acme import specs
from acme import types
from acme.jax import types as jax_types
from acme.jax import utils as jax_utils
import haiku as hk
import jax.numpy as jnp
from typing_extensions import Protocol
PRNGKey = jax_types.PRNGKey
Observation = types.... | Converts non-stochastic FeedForwardNetwork to TypedFeedForwardNetwork. Non-stochastic network is the one that doesn't take a random key as an input for its `apply` method. Arguments: network: non-stochastic feed-forward network. Returns: corresponding TypedFeedForwardNetwork |
28,959 | import dataclasses
from typing import Callable, Optional, Tuple
from acme import specs
from acme import types
from acme.jax import types as jax_types
from acme.jax import utils as jax_utils
import haiku as hk
import jax.numpy as jnp
from typing_extensions import Protocol
PRNGKey = jax_types.PRNGKey
NetworkOutput = type... | Builds an UnrollableNetwork from a hk.Module factory. |
28,960 | import enum
import functools
from typing import Callable, Sequence, Union
import haiku as hk
import jax
import jax.numpy as jnp
class DownsamplingStrategy(enum.Enum):
AVG_POOL = 'avg_pool'
CONV_MAX = 'conv+max' # Used in IMPALA
LAYERNORM_RELU_CONV = 'layernorm+relu+conv' # Used in MuZero
CONV = 'conv'
The pr... | Returns a sequence of modules corresponding to the desired downsampling. |
28,961 | import dataclasses
from typing import Optional, Tuple
from acme import specs
from acme.agents.jax import sac
from acme.jax import networks as networks_lib
import jax
import jax.numpy as jnp
class CQLNetworks:
"""Network and pure functions for the CQL agent."""
policy_network: networks_lib.FeedForwardNetwork
criti... | Applies the policy and samples num_samples actions. |
28,962 | import dataclasses
from typing import Optional, Tuple
from acme import specs
from acme.agents.jax import sac
from acme.jax import networks as networks_lib
import jax
import jax.numpy as jnp
class CQLNetworks:
"""Network and pure functions for the CQL agent."""
policy_network: networks_lib.FeedForwardNetwork
criti... | null |
28,963 | import enum
from typing import Any, Dict, Optional
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.agents.jax import builders as jax_builders
from acme.agents.jax import ppo
from acme.agents.jax import sac
from acme.agents.jax import td3
from acme.multiagent import types as ma_types
fro... | Returns default configs for all agents. Args: agent_types: dict mapping agent IDs to their type. batch_size: shared batch size for all agents. config_overrides: dict mapping (potentially a subset of) agent IDs to their config overrides. This should include any mandatory config parameters for the agents that do not have... |
28,964 | import enum
from typing import Any, Dict, Optional
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.agents.jax import builders as jax_builders
from acme.agents.jax import ppo
from acme.agents.jax import sac
from acme.agents.jax import td3
from acme.multiagent import types as ma_types
fro... | Returns networks for all agents. Args: environment_spec: environment spec. agent_types: dict mapping agent IDs to their type. init_network_fn: optional callable that handles the network initialization for all sub-agents. If this is not supplied, a default network initializer is used (if it is supported for the designat... |
28,965 | import enum
from typing import Any, Dict, Optional
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.agents.jax import builders as jax_builders
from acme.agents.jax import ppo
from acme.agents.jax import sac
from acme.agents.jax import td3
from acme.multiagent import types as ma_types
fro... | Returns default policy networks for all agents. Args: networks: dict mapping agent IDs to their networks. environment_spec: environment spec. agent_types: dict mapping agent IDs to their type. agent_configs: dict mapping agent IDs to their config. eval_mode: whether the policy should be initialized in evaluation mode (... |
28,966 | import enum
from typing import Any, Dict, Optional
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.agents.jax import builders as jax_builders
from acme.agents.jax import ppo
from acme.agents.jax import sac
from acme.agents.jax import td3
from acme.multiagent import types as ma_types
fro... | Returns default policy networks for all agents. |
28,967 | from typing import Callable
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import types as mpo_types
import distrax
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow_probability.substrates.jax as tfp
def _fetch_devicearray(x):
if isinstance(x, jax.Array)... | Gets the first array of a nest of `jax.pxla.ShardedDeviceArray`s. |
28,968 | from typing import Callable
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import types as mpo_types
import distrax
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow_probability.substrates.jax as tfp
The provided code snippet includes necessary dependenci... | Stack the N=T-W+1 length W slices [0:W, 1:W+1, ..., T-W:T] from a tensor. Args: x: The tensor to select rolling slices from (along specified axis), with shape [..., T, ...]; i.e., T = x.shape[axis]. window: The length (W) of the slices to select. axis: The axis to slice from (defaults to 0). time_major: If true, output... |
28,969 | from typing import Callable
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import types as mpo_types
import distrax
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow_probability.substrates.jax as tfp
tfd = tfp.distributions
The provided code snippet inclu... | Apply a jax function to a distribution by treating it as tree. |
28,970 | from typing import Callable
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import types as mpo_types
import distrax
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow_probability.substrates.jax as tfp
The provided code snippet includes necessary dependenci... | Convert a batch of transitions into a batch of 1-step sequences. |
28,971 | import dataclasses
from typing import Callable, Optional, Union
from acme import types
from acme.agents.jax.mpo import types as mpo_types
import numpy as np
import rlax
The provided code snippet includes necessary dependencies for implementing the `_compute_spi_from_replay_fraction` function. Write a Python function `... | Computes an estimated samples_per_insert from a replay_fraction. Assumes actors simultaneously add to both the queue and replay in a mixed replay setup. Since the online queue sets samples_per_insert = 1, then the total SPI can be calculated as: SPI = B / O = O / (1 - f) / O = 1 / (1 - f). Key: B: total batch size O: o... |
28,972 | import dataclasses
from typing import Callable, Optional, Union
from acme import types
from acme.agents.jax.mpo import types as mpo_types
import numpy as np
import rlax
The provided code snippet includes necessary dependencies for implementing the `_compute_num_inserts_per_actor_step` function. Write a Python function... | Estimate the number inserts per actor steps. |
28,973 | import dataclasses
from typing import Callable, NamedTuple, Optional, Sequence, Tuple, Union
from acme import specs
from acme.agents.jax.mpo import types
from acme.jax import networks as networks_lib
from acme.jax import utils
import chex
import haiku as hk
import haiku.initializers as hk_init
import jax
import jax.num... | Initialize the parameters of a MPO network. |
28,974 | import dataclasses
from typing import Callable, NamedTuple, Optional, Sequence, Tuple, Union
from acme import specs
from acme.agents.jax.mpo import types
from acme.jax import networks as networks_lib
from acme.jax import utils
import chex
import haiku as hk
import haiku.initializers as hk_init
import jax
import jax.num... | Creates MPONetworks to be used DM Control suite tasks. |
28,975 | import dataclasses
from typing import Callable, NamedTuple, Optional, Sequence, Tuple, Union
from acme import specs
from acme.agents.jax.mpo import types
from acme.jax import networks as networks_lib
from acme.jax import utils
import chex
import haiku as hk
import haiku.initializers as hk_init
import jax
import jax.num... | Adds a batch dimension at axis 0 to the leaves of a nested structure. |
28,976 | import dataclasses
from typing import Callable, NamedTuple, Optional, Sequence, Tuple, Union
from acme import specs
from acme.agents.jax.mpo import types
from acme.jax import networks as networks_lib
from acme.jax import utils
import chex
import haiku as hk
import haiku.initializers as hk_init
import jax
import jax.num... | null |
28,977 | from typing import NamedTuple, Tuple
import distrax
import jax
import jax.numpy as jnp
The provided code snippet includes necessary dependencies for implementing the `compute_weights_and_temperature_loss` function. Write a Python function `def compute_weights_and_temperature_loss( q_values: jnp.ndarray, logits... | Computes normalized importance weights for the policy optimization. Args: q_values: Q-values associated with the actions sampled from the target policy; expected shape [B, D]. logits: Parameters to the categorical distribution with respect to which the expectations are going to be computed. epsilon: Desired constraint ... |
28,978 | from typing import NamedTuple, Tuple
import distrax
import jax
import jax.numpy as jnp
_MIN_LOG_TEMPERATURE = -18.0
_MIN_LOG_ALPHA = -18.0
class CategoricalMPOParams(NamedTuple):
"""NamedTuple to store trainable loss parameters."""
log_temperature: jnp.ndarray
log_alpha: jnp.ndarray
def clip_categorical_mpo_para... | null |
28,979 | from typing import NamedTuple, Tuple
import distrax
import jax
import jax.numpy as jnp
_MPO_FLOAT_EPSILON = 1e-8
class CategoricalMPOParams(NamedTuple):
"""NamedTuple to store trainable loss parameters."""
log_temperature: jnp.ndarray
log_alpha: jnp.ndarray
def get_temperature_from_params(params: CategoricalMPOP... | null |
28,980 | import dataclasses
import functools
import time
from typing import Any, Dict, Iterator, List, NamedTuple, Optional, Sequence, Tuple, Union
from absl import logging
import acme
from acme import specs
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import categorical_mpo as discre... | Compute cross entropy loss between logits and target probabilities. |
28,981 | import dataclasses
import functools
import time
from typing import Any, Dict, Iterator, List, NamedTuple, Optional, Sequence, Tuple, Union
from absl import logging
import acme
from acme import specs
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import categorical_mpo as discre... | Compute the top-1 accuracy with an argmax of targets (random tie-break). |
28,982 | import dataclasses
import functools
import time
from typing import Any, Dict, Iterator, List, NamedTuple, Optional, Sequence, Tuple, Union
from absl import logging
import acme
from acme import specs
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import categorical_mpo as discre... | null |
28,983 | from typing import Tuple
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import categorical_mpo as discrete_losses
from acme.agents.jax.mpo import networks as mpo_networks
from acme.agents.jax.mpo import types as mpo_types
from acme.agents.jax.mpo import utils as mpo_utils
from ... | Compute cross entropy loss between logits and target probabilities. |
28,984 | from typing import Tuple
from acme import types
from acme.adders import reverb as adders
from acme.agents.jax.mpo import categorical_mpo as discrete_losses
from acme.agents.jax.mpo import networks as mpo_networks
from acme.agents.jax.mpo import types as mpo_types
from acme.agents.jax.mpo import utils as mpo_utils
from ... | Compute the top-1 accuracy with an argmax of targets (random tie-break). |
28,985 | from typing import Mapping, NamedTuple, Tuple, Union
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.mpo import networks
from acme.agents.jax.mpo import types
from acme.jax import types as jax_types
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
class ActorState(N... | Returns a MPO ActorCore from the MPONetworks. |
28,986 | from typing import Iterator, List, Optional
import acme
from acme import adders
from acme import core
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.adders.reverb import base as reverb_base
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax import actors
from... | null |
28,987 | import dataclasses
from typing import Sequence
from acme import specs
from acme import types
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.d4pg import config as d4pg_config
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax.numpy as jnp... | Selects action according to the training policy. |
28,988 | import dataclasses
from typing import Sequence
from acme import specs
from acme import types
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.d4pg import config as d4pg_config
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax.numpy as jnp... | Selects action according to the training policy. |
28,989 | import dataclasses
from typing import Sequence
from acme import specs
from acme import types
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.d4pg import config as d4pg_config
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax.numpy as jnp... | Creates networks used by the agent. |
28,990 | import dataclasses
import functools
from typing import Any, Callable, Generic, Iterator, List, Optional, Tuple
import acme
from acme import adders
from acme import core
from acme import specs
from acme import types
from acme.agents.jax import builders
from acme.jax import networks as networks_lib
from acme.jax import r... | Builder class decorator that adds support for input normalization. |
28,991 | import dataclasses
from typing import Callable, Optional, Sequence, Union
from acme.adders import reverb as adders_reverb
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `logspace_epsilons` function. Write a Python function `def logspace_epsilon... | `num_epsilons` of logspace-distributed values, with median `epsilon`. |
28,992 | from typing import Callable, Sequence
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.dqn import networks as dqn_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
import chex
import jax
import jax.numpy as jnp
EpsilonPolicy = Callable[[
networks_lib.Para... | Returns actor components for alternating epsilon exploration. Args: policy_network: A feedforward action selecting function. epsilons: epsilons to alternate per-episode for epsilon-greedy exploration. Returns: A feedforward policy. |
28,993 | from typing import Callable, Sequence
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.dqn import networks as dqn_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
import chex
import jax
import jax.numpy as jnp
Epsilon = float
EpsilonPolicy = Callable[[
n... | A policy with parameterized epsilon-greedy exploration. |
28,994 | from typing import Callable, Sequence
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.dqn import networks as dqn_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
import chex
import jax
import jax.numpy as jnp
Epsilon = float
EpsilonPolicy = Callable[[
n... | A policy with a fixed-epsilon epsilon-greedy exploration. DEPRECATED: use behavior_policy instead. Args: networks: DQN networks epsilon: sampling parameter that overrides the one in EpsilonPolicy Returns: epsilon-greedy behavior policy with fixed epsilon |
28,995 | import dataclasses
from typing import Callable, Optional
from acme.jax import networks as networks_lib
from acme.jax import types
import rlax
Epsilon = float
def default_sample_fn(action_values: networks_lib.NetworkOutput,
key: types.PRNGKey,
epsilon: Epsilon) -> networks_li... | null |
28,996 | import dataclasses
from typing import Callable
from acme import specs
from acme.agents.jax.dqn import actor as dqn_actor
from acme.agents.jax.dqn import builder
from acme.agents.jax.dqn import config as dqn_config
from acme.agents.jax.dqn import losses
from acme.jax import networks as networks_lib
from acme.jax import ... | Returns a function that computes actions. Note that this differs from default_behavior_policy with that it expects c51-style network head which returns a tuple with the first entry representing q-values. Args: network: A c51-style feedforward network. eval_epsilon: for epsilon-greedy exploration. Returns: A feedforward... |
28,997 | import dataclasses
from typing import Callable
from acme import specs
from acme.agents.jax.dqn import actor as dqn_actor
from acme.agents.jax.dqn import builder
from acme.agents.jax.dqn import config as dqn_config
from acme.agents.jax.dqn import losses
from acme.jax import networks as networks_lib
from acme.jax import ... | Returns a DQNBuilder with a pre-built loss function. |
28,998 | import dataclasses
import functools
from typing import Callable, Generic, Tuple, TypeVar
from acme import specs
from acme import types
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax.numpy as jnp
DirectRLNetworks = TypeVar('DirectRLNetworks')
class RNDNetworks(Gene... | Creates networks used by the agent and returns RNDNetworks. Args: spec: Environment spec. direct_rl_networks: Networks used by a direct rl algorithm. layer_sizes: Layer sizes. intrinsic_reward_coefficient: Multiplier on intrinsic reward. extrinsic_reward_coefficient: Multiplier on extrinsic reward. Returns: The RND net... |
28,999 | import dataclasses
import functools
from typing import Callable, Generic, Tuple, TypeVar
from acme import specs
from acme import types
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax.numpy as jnp
class RNDNetworks(Generic[DirectRLNetworks]):
"""Container of RND n... | Computes the intrinsic RND reward for a given transition. Args: predictor_params: Parameters of the predictor network. target_params: Parameters of the target network. transitions: The sample to compute rewards for. networks: RND networks Returns: The rewards as an ndarray. |
29,000 | import functools
import time
from typing import Any, Callable, Dict, Iterator, List, NamedTuple, Optional, Tuple
import acme
from acme import types
from acme.agents.jax.rnd import networks as rnd_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.utils import counting
from acme.... | Run an update steps on the given transitions. Args: state: The learner state. transitions: Transitions to update on. loss_fn: The loss function. optimizer: The optimizer of the predictor network. Returns: A new state and metrics. |
29,001 | import functools
import time
from typing import Any, Callable, Dict, Iterator, List, NamedTuple, Optional, Tuple
import acme
from acme import types
from acme.agents.jax.rnd import networks as rnd_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.utils import counting
from acme.... | The Random Network Distillation loss. See https://arxiv.org/pdf/1810.12894.pdf A.2 Args: predictor_params: Parameters of the predictor target_params: Parameters of the target transitions: Transitions to compute the loss on. networks: RND networks Returns: The MSE loss as a float. |
29,002 | import dataclasses
from typing import Callable, Generic, Mapping, Tuple, TypeVar, Union
from acme import types
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.jax.types import PRNGKey
import chex
import jax
import jax.numpy as jnp
State = TypeVar('State')
class ActorCore(Generic[State... | Makes an actor core's select_action method expect unbatched arguments. |
29,003 | import dataclasses
from typing import Callable, Generic, Mapping, Tuple, TypeVar, Union
from acme import types
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.jax.types import PRNGKey
import chex
import jax
import jax.numpy as jnp
class ActorCore(Generic[State, Extras]):
"""Pure fun... | A convenience adaptor from FeedForwardPolicy to ActorCore. |
29,004 | import dataclasses
from typing import Any, Callable, Generic, NamedTuple, Optional
from acme import adders
from acme import types
from acme.agents.jax import actor_core
from acme.agents.jax import actors
from acme.jax import networks as network_lib
from acme.jax import running_statistics
from acme.jax import utils
from... | Builds pure functions used for normalizing based on EMA mean and std. The built normalizer functions can be used to normalize nested arrays that have a structure corresponding to nested_spec. Currently only supports nested_spec where all leafs have float dtype. Arguments: nested_spec: A nested spec where all leaves hav... |
29,005 | import dataclasses
from typing import Any, Callable, Generic, NamedTuple, Optional
from acme import adders
from acme import types
from acme.agents.jax import actor_core
from acme.agents.jax import actors
from acme.jax import networks as network_lib
from acme.jax import running_statistics
from acme.jax import utils
from... | Builds pure functions used for normalizing based on mean and std. Arguments: nested_spec: A nested spec where all leaves have float dtype max_abs_value: Normalized nested arrays will be clipped so that all values will be between -max_abs_value and +max_abs_value. Setting to None (default) does not perform this clipping... |
29,006 | import dataclasses
from typing import Callable, NamedTuple, Optional, Sequence
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import tensorfl... | Returns a function to be used for inference by a PPO actor. |
29,007 | import dataclasses
from typing import Callable, NamedTuple, Optional, Sequence
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import tensorfl... | null |
29,008 | import dataclasses
from typing import Callable, NamedTuple, Optional, Sequence
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import tensorfl... | Constructs a PPONetworks instance from the given FeedForwardNetwork. This method assumes that the network returns a tfd.Distribution. Sometimes it may be preferable to have networks that do not return tfd.Distribution objects, for example, due to tfd.Distribution not playing nice with jax.vmap. Please refer to the make... |
29,009 | import dataclasses
from typing import Callable, Sequence
from acme import specs
from acme import types
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
class TD3Netwo... | Selects action according to the policy. |
29,010 | import dataclasses
from typing import Callable, Sequence
from acme import specs
from acme import types
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import utils
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
class TD3Netwo... | Creates networks used by the agent. The networks used are based on LayerNormMLP, which is different than the MLP with relu activation described in TD3 (which empirically performs worse). Args: spec: Environment specs hidden_layer_sizes: list of sizes of hidden layers in actor/critic networks Returns: network: TD3Networ... |
29,011 | from typing import Callable, Generic, Mapping, Optional, Tuple
from acme import types
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.r2d2 import config as r2d2_config
from acme.agents.jax.r2d2 import networks as r2d2_networks
from acme.jax import networks as networks_lib
import chex
impor... | Returns ActorCore for R2D2. |
29,012 | from typing import Callable, Generic, Mapping, Optional, Tuple
from acme import types
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.r2d2 import config as r2d2_config
from acme.agents.jax.r2d2 import networks as r2d2_networks
from acme.jax import networks as networks_lib
import chex
impor... | Selects action according to the policy. |
29,013 | from typing import Generic, Iterator, List, Optional
import acme
from acme import adders
from acme import core
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.adders.reverb import base as reverb_base
from acme.adders.reverb import structured
from acme.agents.jax import actor_core as act... | Adds zero padding to the right so all samples have the same length. |
29,014 | from typing import Generic, Iterator, List, Optional
import acme
from acme import adders
from acme import core
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.adders.reverb import base as reverb_base
from acme.adders.reverb import structured
from acme.agents.jax import actor_core as act... | null |
29,015 | from acme import specs
from acme.jax import networks as networks_lib
R2D2Networks = networks_lib.UnrollableNetwork
The provided code snippet includes necessary dependencies for implementing the `make_atari_networks` function. Write a Python function `def make_atari_networks(env_spec: specs.EnvironmentSpec) -> R2D2Netw... | Builds default R2D2 networks for Atari games. |
29,016 | from typing import Optional
from acme.agents.jax.ail import networks as ail_networks
from acme.jax import networks as networks_lib
import jax
import jax.numpy as jnp
The provided code snippet includes necessary dependencies for implementing the `fairl_reward` function. Write a Python function `def fairl_reward( ma... | The FAIRL reward function (https://arxiv.org/pdf/1911.02256.pdf). Args: max_reward_magnitude: Clipping value for the reward. Returns: The function from logit to imitation reward. |
29,017 | from typing import Optional
from acme.agents.jax.ail import networks as ail_networks
from acme.jax import networks as networks_lib
import jax
import jax.numpy as jnp
The provided code snippet includes necessary dependencies for implementing the `gail_reward` function. Write a Python function `def gail_reward( rewa... | GAIL reward function (https://arxiv.org/pdf/1606.03476.pdf). Args: reward_balance: 1 means log(D) reward, 0 means -log(1-D) and other values mean an average of the two. max_reward_magnitude: Clipping value for the reward. Returns: The function from logit to imitation reward. |
29,018 | import functools
from typing import Callable, Dict, Optional, Tuple
from acme import types
from acme.jax import networks as networks_lib
import jax
import jax.numpy as jnp
import tensorflow_probability as tfp
import tree
State = networks_lib.Params
DiscriminatorFn = Callable[[State, types.Transition], DiscriminatorOutp... | Computes the standard GAIL loss. |
29,019 | import functools
from typing import Callable, Dict, Optional, Tuple
from acme import types
from acme.jax import networks as networks_lib
import jax
import jax.numpy as jnp
import tensorflow_probability as tfp
import tree
State = networks_lib.Params
DiscriminatorFn = Callable[[State, types.Transition], DiscriminatorOutp... | Computes the PUGAIL loss (https://arxiv.org/pdf/1911.00459.pdf). |
29,020 | import functools
from typing import Callable, Dict, Optional, Tuple
from acme import types
from acme.jax import networks as networks_lib
import jax
import jax.numpy as jnp
import tensorflow_probability as tfp
import tree
State = networks_lib.Params
DiscriminatorFn = Callable[[State, types.Transition], DiscriminatorOutp... | Adds a gradient penalty to the base_loss. |
29,021 | import dataclasses
from typing import Optional
import optax
class AILConfig:
"""Configuration options for AIL.
Attributes:
direct_rl_batch_size: Batch size of a direct rl algorithm (measured in
transitions).
is_sequence_based: If True, a direct rl algorithm is using SequenceAdder
data format. Ot... | Returns how many transitions should be sampled per direct learner step. |
29,022 | import functools
import itertools
from typing import Callable, Generic, Iterator, List, Optional, Tuple
from acme import adders
from acme import core
from acme import specs
from acme import types
from acme.adders import reverb as adders_reverb
from acme.agents.jax import builders
from acme.agents.jax.ail import config ... | Generator which creates the sample having demonstrations in them. It takes the demonstrations and replay iterators and generates batches with same size as the replay iterator, such that each batches have the ratio of policy and expert data specified in policy_to_expert_data_ratio on average. There is no constraints on ... |
29,023 | import dataclasses
import functools
from typing import Any, Callable, Generic, Iterable, Optional
from acme import specs
from acme import types
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.jax.imitation_learning_types import DirectRLNetworks
import haiku as hk
import jax
from jax i... | Computes the AIL reward for a given transition. Args: discriminator_params: Parameters of the discriminator network. discriminator_state: State of the discriminator network. policy_params: Parameters of the direct RL policy. transitions: Transitions to compute the reward for. networks: AIL networks. Returns: The reward... |
29,024 | import dataclasses
import functools
from typing import Any, Callable, Generic, Iterable, Optional
from acme import specs
from acme import types
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.jax.imitation_learning_types import DirectRLNetworks
import haiku as hk
import jax
from jax i... | Creates the discriminator network. Args: environment_spec: Environment spec discriminator_transformed: Haiku transformed of the discriminator. logpi_fn: If the policy logpi function is provided, its output will be removed from the discriminator logit. Returns: The network. |
29,025 | import functools
import itertools
import time
from typing import Any, Callable, Iterator, List, NamedTuple, Optional, Tuple
import acme
from acme import types
from acme.agents.jax.ail import losses
from acme.agents.jax.ail import networks as ail_networks
from acme.jax import networks as networks_lib
from acme.jax impor... | Run an update steps on the given transitions. Args: state: The learner state. data: Demo and rb transitions. optimizer: Discriminator optimizer. ail_network: AIL networks. loss_fn: Discriminator loss to minimize. Returns: A new state and metrics. |
29,026 | import dataclasses
from typing import Any, Optional
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.agents.jax import normalization
import numpy as onp
The provided code snippet includes necessary dependencies for implementing the `target_entropy_from_env_spec` function. Write a Python... | A heuristic to determine a target entropy. If target_entropy_per_dimension is not specified, the target entropy is computed as "-num_actions", otherwise it is "target_entropy_per_dimension * num_actions". Args: spec: environment spec target_entropy_per_dimension: None or target entropy per action dimension Returns: tar... |
29,027 | import dataclasses
from typing import Optional, Tuple
from acme import core
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import types
from acme.jax import utils
import haiku as hk
import jax
import jax.numpy as jnp
import num... | Defines default models to be snapshotted. |
29,028 | import dataclasses
from typing import Optional, Tuple
from acme import core
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.jax import networks as networks_lib
from acme.jax import types
from acme.jax import utils
import haiku as hk
import jax
import jax.numpy as jnp
import num... | Creates networks used by the agent. |
29,029 | from typing import Callable, Optional, Tuple, Union
from acme import types
from acme.agents.jax.bc import networks as bc_networks
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.utils import loggers
import jax
import jax.numpy as jnp
BCLossWithoutAux = Callable[loss_args,... | Mean Squared Error loss. |
29,030 | from typing import Callable, Optional, Tuple, Union
from acme import types
from acme.agents.jax.bc import networks as bc_networks
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.utils import loggers
import jax
import jax.numpy as jnp
BCLossWithoutAux = Callable[loss_args,... | Log probability loss. |
29,031 | from typing import Callable, Optional, Tuple, Union
from acme import types
from acme.agents.jax.bc import networks as bc_networks
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.utils import loggers
import jax
import jax.numpy as jnp
BCLossWithoutAux = Callable[loss_args,... | Peer-BC loss from https://arxiv.org/pdf/2010.01748.pdf. Args: base_loss_fn: the base loss to add RCAL on top of. zeta: the weight of the regularization. Returns: The loss. |
29,032 | from typing import Callable, Optional, Tuple, Union
from acme import types
from acme.agents.jax.bc import networks as bc_networks
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
from acme.utils import loggers
import jax
import jax.numpy as jnp
BCLossWithoutAux = Callable[loss_args,... | https://www.cristal.univ-lille.fr/~pietquin/pdf/AAMAS_2014_BPMGOP.pdf. Args: base_loss_fn: the base loss to add RCAL on top of. discount: the gamma discount used in RCAL. alpha: the regularization parameter. num_bins: how many bins were used for discretization. If None the environment was originally discrete already. R... |
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