id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
29,033 | import dataclasses
from typing import Callable, Optional
from acme.jax import networks as networks_lib
from acme.jax import types
from typing_extensions import Protocol
The provided code snippet includes necessary dependencies for implementing the `identity_sample` function. Write a Python function `def identity_sampl... | Placeholder sampling function for non-distributional networks. |
29,034 | import dataclasses
from typing import Callable, Optional
from acme.jax import networks as networks_lib
from acme.jax import types
from typing_extensions import Protocol
class BCPolicyNetwork:
"""Holds a pair of pure functions defining a policy network for BC.
This is a feed-forward network taking params, obs, is_tr... | Converts a policy network from SAC/TD3/D4PG/.. into a BC policy network. Args: policy_network: FeedForwardNetwork taking the observation as input and returning action representation compatible with one of the BC losses. Returns: The BC policy network taking observation, is_training, key as input. |
29,035 | import dataclasses
from typing import Callable, Optional
from acme.jax import networks as networks_lib
from acme.jax import types
from typing_extensions import Protocol
class BCPolicyNetwork:
"""Holds a pair of pure functions defining a policy network for BC.
This is a feed-forward network taking params, obs, is_tr... | Converts a policy-value network (e.g. from PPO) into a BC policy network. Args: policy_value_network: FeedForwardNetwork taking the observation as input. Returns: The BC policy network taking observation, is_training, key as input. |
29,036 | from typing import Callable, Iterator
from acme import types
from acme.agents.jax.bc import learning
from acme.agents.jax.bc import losses
from acme.agents.jax.bc import networks as bc_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
import jax
import optax
The provided code snippet in... | Trains the given network with BC and returns the params. Args: make_demonstrations: A function (batch_size) -> iterator with demonstrations to be imitated. networks: Network taking (params, obs, is_training, key) as input loss: BC loss to use. num_steps: number of training steps Returns: The trained network params. |
29,037 | import time
from typing import Dict, List, NamedTuple, Optional, Tuple, Union, Iterator
import acme
from acme import types
from acme.agents.jax.bc import losses
from acme.agents.jax.bc import networks as bc_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.utils import counting... | Creates loss metrics for logging. |
29,038 | import dataclasses
from typing import Any, Callable, Optional, Tuple, Union
from acme import types
from acme.agents.jax.mbop import dataset
from acme.jax import networks
import jax
import jax.numpy as jnp
def mse(a: jnp.ndarray, b: jnp.ndarray) -> jnp.ndarray:
"""MSE distance."""
return jnp.mean(jnp.square(a - b))
... | Returns the loss for the world model. Args: apply_fn: applies a transition model (o_t, a_t) -> (o_t+1, r), expects the leading axis to index the batch and the second axis to index the transition triplet (t-1, t, t+1). steps: RLDS dictionary of transition triplets as prepared by `rlds_loader.episode_to_timestep_batch`. ... |
29,039 | import dataclasses
from typing import Any, Callable, Optional, Tuple, Union
from acme import types
from acme.agents.jax.mbop import dataset
from acme.jax import networks
import jax
import jax.numpy as jnp
def mse(a: jnp.ndarray, b: jnp.ndarray) -> jnp.ndarray:
"""MSE distance."""
return jnp.mean(jnp.square(a - b))
... | Returns the loss for the policy prior. Args: apply_fn: applies a policy prior (o_t, a_t) -> a_t+1, expects the leading axis to index the batch and the second axis to index the transition triplet (t-1, t, t+1). steps: RLDS dictionary of transition triplets as prepared by `rlds_loader.episode_to_timestep_batch`. Returns:... |
29,040 | import dataclasses
from typing import Any, Callable, Optional, Tuple, Union
from acme import types
from acme.agents.jax.mbop import dataset
from acme.jax import networks
import jax
import jax.numpy as jnp
def mse(a: jnp.ndarray, b: jnp.ndarray) -> jnp.ndarray:
"""MSE distance."""
return jnp.mean(jnp.square(a - b))
... | Returns the loss for the n-step return model. Args: apply_fn: applies an n-step return model (o_t, a_t) -> r, expects the leading axis to index the batch and the second axis to index the transition triplet (t-1, t, t+1). steps: RLDS dictionary of transition triplets as prepared by `rlds_loader.episode_to_timestep_batch... |
29,041 | import dataclasses
from typing import Any, Tuple
from acme import specs
from acme.jax import networks
from acme.jax import utils
import haiku as hk
import jax.numpy as jnp
import numpy as np
class MBOPNetworks:
"""Container class to hold MBOP networks."""
world_model_network: WorldModelNetwork
policy_prior_networ... | Creates networks used by the agent. |
29,042 | import functools
import itertools
from typing import Iterator, Optional
from acme import types
from acme.jax import running_statistics
import jax
import jax.numpy as jnp
import rlds
import tensorflow as tf
import tree
EPISODE_RETURN: str = 'episode_return'
def episode_to_timestep_batch(
episode: rlds.BatchedStep,
... | Process an existing dataset converting it to episode to 3-transitions. A 3-transition is an Transition with each attribute having an extra dimension of size 3, representing 3 consecutive timesteps. Each 3-step object will be in random order relative to each other. See `episode_to_timestep_batch` for more information. A... |
29,043 | import functools
import itertools
from typing import Iterator, Optional
from acme import types
from acme.jax import running_statistics
import jax
import jax.numpy as jnp
import rlds
import tensorflow as tf
import tree
PREVIOUS: int = 0
The provided code snippet includes necessary dependencies for implementing the `get... | Precomputes normalization statistics over a fixed number of batches. The iterator should contain batches of 3-transitions, i.e. with two leading dimensions, the first one denoting the batch dimension and the second one the previous, current and next timesteps. The statistics are calculated using the data of the previou... |
29,044 | import dataclasses
import functools
import itertools
import time
from typing import Any, Callable, Iterator, List, Optional
from acme import core
from acme import types
from acme.agents.jax import bc
from acme.agents.jax.mbop import ensemble
from acme.agents.jax.mbop import losses as mbop_losses
from acme.agents.jax.mb... | Creates an ensemble regressor learner from the base network. Args: name: Name of the learner used for logging and counters. num_networks: Number of networks in the ensemble. logger_fn: Constructs a logger for a label. counter: Parent counter object. rng_key: Random key. iterator: An iterator of time-batched transitions... |
29,045 | import dataclasses
import functools
from typing import Callable, Optional
from acme import specs
from acme.agents.jax.mbop import models
from acme.jax import networks
import jax
from jax import random
import jax.numpy as jnp
The provided code snippet includes necessary dependencies for implementing the `return_weighte... | r"""Calculates return-weighted average over all trajectories. This will calculate the return-weighted average over a set of trajectories as defined on l.17 of Alg. 2 in the MBOP paper: [https://arxiv.org/abs/2008.05556]. Note: Clipping will be performed for `cum_reward` values > 80 to avoid NaNs. Args: action_trajector... |
29,046 | import dataclasses
import functools
from typing import Callable, Optional
from acme import specs
from acme.agents.jax.mbop import models
from acme.jax import networks
import jax
from jax import random
import jax.numpy as jnp
The provided code snippet includes necessary dependencies for implementing the `return_top_k_a... | r"""Calculates the top-k average over all trajectories. This will calculate the top-k average over a set of trajectories as defined in the POIR Paper: Note: top-k average is more numerically stable than the weighted average. Args: action_trajectories: (n_trajectories, horizon, action_dim) tensor of action trajectories.... |
29,047 | from typing import List, Mapping, Optional, Tuple
from acme import adders
from acme import core
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax import actors
from acme.agents.jax.mbop import models
from acme.agents.jax.mbop import mppi
from acme.agents.jax.mbop impor... | Creates an actor core that uses ensemble models. Args: networks: MBOP networks. mppi_config: Planner hyperparameters. environment_spec: Used to initialize the initial trajectory data structure. mean_std: Used to undo normalization if the networks trained normalized. use_round_robin: Whether to use round robin or mean t... |
29,048 | from typing import List, Mapping, Optional, Tuple
from acme import adders
from acme import core
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax import actors
from acme.agents.jax.mbop import models
from acme.agents.jax.mbop import mppi
from acme.agents.jax.mbop impor... | Creates an MBOP actor from an actor core. Args: actor_core: An MBOP actor core. random_key: JAX Random key. variable_source: The source to get networks parameters from. adder: An adder to add experiences to. The `extras` of the adder holds the state of the recurrent policy. If `has_extras=True` then the `extras` part r... |
29,049 | from typing import Callable
from acme import types
from acme.agents.jax.crr.networks import CRRNetworks
from acme.jax import networks as networks_lib
import jax.numpy as jnp
def _compute_advantage(networks: CRRNetworks,
policy_params: networks_lib.Params,
critic_params: net... | Exponential advantage weigting; see equation (4) in CRR paper. |
29,050 | from typing import Callable
from acme import types
from acme.agents.jax.crr.networks import CRRNetworks
from acme.jax import networks as networks_lib
import jax.numpy as jnp
def _compute_advantage(networks: CRRNetworks,
policy_params: networks_lib.Params,
critic_params: net... | Indicator advantage weighting; see equation (3) in CRR paper. |
29,051 | from typing import Callable
from acme import types
from acme.agents.jax.crr.networks import CRRNetworks
from acme.jax import networks as networks_lib
import jax.numpy as jnp
class CRRNetworks:
"""Network and pure functions for the CRR agent.."""
policy_network: networks_lib.FeedForwardNetwork
critic_network: net... | Constant weights. |
29,052 | import dataclasses
from typing import Callable, Tuple
from acme import specs
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 CRRNetworks:
"""Network and pure functions for the CRR agent.."""
policy_network: netwo... | Creates networks used by the agent. |
29,053 | import dataclasses
from typing import Callable, Optional, Tuple
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
class ValueDiceNetworks:
"""... | Returns a function that computes actions. |
29,054 | import dataclasses
from typing import Callable, Optional, Tuple
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
class ValueDiceNetworks:
"""... | Creates networks used by the agent. |
29,055 | import functools
import time
from typing import Any, Dict, Iterator, List, Mapping, NamedTuple, Optional, Tuple
import acme
from acme import types
from acme.agents.jax.value_dice import networks as value_dice_networks
from acme.jax import networks as networks_lib
from acme.jax import utils
from acme.utils import counti... | Orthogonal regularization. See equation (3) in https://arxiv.org/abs/1809.11096. Args: params: Dictionary of parameters to apply regualization for. Returns: A regularization loss term. |
29,056 | import threading
from typing import Callable, Generic, Iterator, List, Optional, Sequence
from acme import adders
from acme import core
from acme import specs
from acme import types
from acme.agents.jax import builders
from acme.agents.jax.pwil import adder as pwil_adder
from acme.agents.jax.pwil import config as pwil_... | Fill the adder's replay buffer with expert transitions. Assumes that the demonstrations dataset stores transitions in order. Args: adder: the agent which adds the demonstrations. demonstrations: the expert demonstrations to iterate over. reward: if non-None, populates the environment reward entry of transitions. min_nu... |
29,057 | from typing import Callable, Generic, Iterator, List, Optional
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 utils
from acme.jax.imitation_learning_types import DirectPol... | Generator which creates the sample iterator for SQIL. Args: demonstration_iterator: Iterator of demonstrations. replay_iterator: Replay buffer sample iterator. Yields: Samples having a mix of demonstrations with reward 1 and replay samples with reward 0. |
29,058 | from acme import specs
from acme.jax import networks as networks_lib
IMPALANetworks = 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) -> IMPALA... | Builds default IMPALA networks for Atari games. |
29,059 | from typing import Generic, Mapping, Tuple
from acme import specs
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax.impala import networks as impala_networks
from acme.jax import networks as networks_lib
from acme.jax import types as jax_types
import chex
import jax
import jax.numpy as jnp
I... | Creates an Impala ActorCore. |
29,060 | from typing import Dict, Iterator, List, Optional, Tuple
import acme
from acme import adders
from acme import core
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.agents.jax import actor_core as actor_core_lib
from acme.agents.jax import actors
from acme.agents.jax import builders
from ... | Returns a function that computes actions. |
29,061 | from typing import Tuple
from acme import specs
from acme.jax import networks as networks_lib
import jax.numpy as jnp
The provided code snippet includes necessary dependencies for implementing the `make_networks` function. Write a Python function `def make_networks( spec: specs.EnvironmentSpec) -> networks_lib.Fee... | Creates networks used by the agent. The model used by the ARS paper is a simple clipped linear model. Args: spec: an environment spec Returns: A FeedForwardNetwork network. |
29,062 | from typing import Tuple
from acme import specs
from acme.jax import networks as networks_lib
import jax.numpy as jnp
BEHAVIOR_PARAMS_NAME = 'policy'
EVAL_PARAMS_NAME = 'eval'
def make_policy_network(
network: networks_lib.FeedForwardNetwork,
eval_mode: bool = True) -> Tuple[str, networks_lib.FeedForwardNetwor... | null |
29,063 | import math
from typing import List, Optional, Sequence
from acme import core
from acme import types
import dm_env
import numpy as np
import reverb
The provided code snippet includes necessary dependencies for implementing the `_calculate_num_learner_steps` function. Write a Python function `def _calculate_num_learner... | Calculates the number of learner steps to do at step=num_observations. |
29,064 | import copy
import dataclasses
import functools
from typing import Iterator, List, Optional, Tuple, Union, Sequence
from acme import adders
from acme import core
from acme import datasets
from acme import specs
from acme import types
from acme.adders import reverb as reverb_adders
from acme.agents import agent
from acm... | Returns a replicator instance appropriate for the given accelerator. This caches the instance using functools.cache, so that only one replicator is instantiated per process and argument value. Args: accelerator: None, 'TPU', 'GPU', or 'CPU'. If None, the first available accelerator type will be chosen from ('TPU', 'GPU... |
29,065 | from typing import Mapping, Sequence
from acme import specs
from acme import types
from acme.tf import networks
from acme.tf import utils as tf2_utils
import numpy as np
import sonnet as snt
The provided code snippet includes necessary dependencies for implementing the `make_default_networks` function. Write a Python ... | Creates networks used by the agent. |
29,066 | import time
from typing import Dict, Iterator, List, Optional, Union, Sequence
import acme
from acme import types
from acme.tf import losses
from acme.tf import networks as acme_nets
from acme.tf import savers as tf2_savers
from acme.tf import utils as tf2_utils
from acme.utils import counting
from acme.utils import lo... | Computes the average gradient across replicas. This computes the gradient locally on this device, then copies over the gradients computed on the other replicas, and takes the average across replicas. This is faster than copying the gradients from TPU to CPU, and averaging them on the CPU (which is what we do for the lo... |
29,067 | import functools
import time
from typing import Dict, Iterator, List, Mapping, Union, Optional
import acme
from acme import specs
from acme.adders import reverb as adders
from acme.tf import losses
from acme.tf import networks
from acme.tf import savers as tf2_savers
from acme.tf import utils as tf2_utils
from acme.uti... | Compute priority as mixture of max and mean sequence errors. |
29,068 | import dataclasses
import time
from typing import Callable, List, Optional, Sequence
import acme
from acme import types
from acme.tf import losses
from acme.tf import networks
from acme.tf import savers as tf2_savers
from acme.tf import utils as tf2_utils
from acme.utils import counting
from acme.utils import loggers
i... | Compute loss and sampled Q-values for distributional critics. |
29,069 | import dataclasses
import time
from typing import Callable, List, Optional, Sequence
import acme
from acme import types
from acme.tf import losses
from acme.tf import networks
from acme.tf import savers as tf2_savers
from acme.tf import utils as tf2_utils
from acme.utils import counting
from acme.utils import loggers
i... | Compute loss and sampled Q-values for (non-distributional) critics. |
29,070 | import dataclasses
from typing import Callable, Dict
from acme.agents.tf.mcts import models
from acme.agents.tf.mcts import types
import numpy as np
class Node:
"""A MCTS node."""
reward: float = 0.
visit_count: int = 0
terminal: bool = False
prior: float = 1.
total_value: float = 0.
children: Dict[types.... | Does Monte Carlo tree search (MCTS), AlphaZero style. |
29,071 | import dataclasses
from typing import Callable, Dict
from acme.agents.tf.mcts import models
from acme.agents.tf.mcts import types
import numpy as np
class Node:
"""A MCTS node."""
reward: float = 0.
visit_count: int = 0
terminal: bool = False
prior: float = 1.
total_value: float = 0.
children: Dict[types.... | Breadth-first search policy. |
29,072 | import dataclasses
from typing import Callable, Dict
from acme.agents.tf.mcts import models
from acme.agents.tf.mcts import types
import numpy as np
class Node:
"""A MCTS node."""
reward: float = 0.
visit_count: int = 0
terminal: bool = False
prior: float = 1.
total_value: float = 0.
children: Dict[types.... | PUCT search policy, i.e. UCT with 'prior' policy. |
29,073 | import dataclasses
from typing import Callable, Dict
from acme.agents.tf.mcts import models
from acme.agents.tf.mcts import types
import numpy as np
class Node:
"""A MCTS node."""
reward: float = 0.
visit_count: int = 0
terminal: bool = False
prior: float = 1.
total_value: float = 0.
children: Dict[types.... | Probability weighted by visit^{1/temp} of children nodes. |
29,074 | import functools
from typing import Optional
from acme import datasets
from acme import specs
from acme import types as acme_types
from acme.adders import reverb as adders
from acme.agents import agent
from acme.agents.tf import actors
from acme.agents.tf.r2d2 import learning
from acme.tf import savers as tf2_savers
fr... | Produce Reverb-like sequence from a full episode. Observations, actions, rewards and discounts have the same length. This function will ignore the first reward and discount and the last action. This function generates fake (all-zero) extras. See docs for reverb.SequenceAdder() for more details. Args: observations: [L, ... |
29,075 | from typing import Dict, List, Optional, Tuple
from acme import core
from acme import types
from acme.adders import reverb as adders
from acme.tf import losses
from acme.tf import networks
from acme.tf import savers as tf2_savers
from acme.tf import utils as tf2_utils
from acme.utils import counting
from acme.utils imp... | Slice an embedding Tensor with action indices. Take embeddings of the form [batch_size, num_actions, embed_dim] and actions of the form [batch_size], and return the sliced embeddings like embeddings[:, actions, :]. Doing this my way because the comments in the official op are scary. Args: embeddings: Tensor of embeddin... |
29,076 | import collections
from typing import Tuple, Optional, Dict, Iterable
from acme import types
from acme.tf import utils as tf2_utils
import sonnet as snt
import tensorflow as tf
import tree
def _nest_stack(list_of_nests, axis=0):
"""Convert a list of nests to a nest of stacked lists."""
return tree.map_structure(lam... | Unroll core along inputs for unroll_length steps. Note: for time-major input tensors whose leading dimension is less than unroll_length, `None` would be provided instead. Args: core: an instance of snt.Module. inputs: a `nest` of time-major input tensors. unroll_length: number of time steps to unroll. Returns: step_out... |
29,077 | import collections
from typing import Tuple, Optional, Dict, Iterable
from acme import types
from acme.tf import utils as tf2_utils
import sonnet as snt
import tensorflow as tf
import tree
The provided code snippet includes necessary dependencies for implementing the `mask_out_restarting` function. Write a Python func... | Mask out `tensor` taken on the step that resets the environment. Args: tensor: a time-major 2-D `Tensor` of shape [T, B]. start_of_episode: a 2-D `Tensor` of shape [T, B] that contains the points where the episode restarts. Returns: tensor of shape [T, B] with elements are masked out according to step_types, restarting... |
29,078 | import functools
from typing import Mapping, Sequence, Optional
from acme import specs
from acme import types
from acme.agents.tf.svg0_prior import utils as svg0_utils
from acme.tf import networks
from acme.tf import utils as tf2_utils
import numpy as np
import sonnet as snt
The provided code snippet includes necessar... | Creates networks used by the agent. |
29,079 | import functools
from typing import Mapping, Sequence, Optional
from acme import specs
from acme import types
from acme.agents.tf.svg0_prior import utils as svg0_utils
from acme.tf import networks
from acme.tf import utils as tf2_utils
import numpy as np
import sonnet as snt
The provided code snippet includes necessar... | Creates networks used by the agent. |
29,080 | from typing import Mapping, Sequence
from acme import specs
from acme.tf import networks
from acme.tf import utils as tf2_utils
import numpy as np
import sonnet as snt
The provided code snippet includes necessary dependencies for implementing the `make_default_networks` function. Write a Python function `def make_defa... | Creates networks used by the agent. |
29,081 | from typing import Any, List
from absl import flags
from bsuite.environments import deep_sea
import dm_env
import numpy as np
import tensorflow as tf
import tree
The provided code snippet includes necessary dependencies for implementing the `_nested_stack` function. Write a Python function `def _nested_stack(sequence:... | Stack nested elements in a sequence. |
29,082 | import copy
import functools
import operator
from typing import Optional
from acme import datasets
from acme import specs
from acme import types as acme_types
from acme.adders import reverb as adders
from acme.agents import agent
from acme.agents.tf import actors
from acme.agents.tf import dqn
from acme.tf import utils... | Produce Reverb-like N-step transition from a full episode. Observations, actions, rewards and discounts have the same length. This function will ignore the first reward and discount and the last action. Args: observations: [L, ...] Tensor. actions: [L, ...] Tensor. rewards: [L] Tensor. discounts: [L] Tensor. n_step: nu... |
29,083 | import dataclasses
from typing import Any, Callable, Dict, Iterator, Optional
from acme import adders as adders_lib
from acme import datasets
from acme import specs
from acme import types
from acme.adders import reverb as adders
import reverb
class ReverbReplay:
server: reverb.Server
adder: adders_lib.Adder
data_... | Creates a single-process replay infrastructure from an environment spec. |
29,084 | import dataclasses
from typing import Any, Callable, Dict, Iterator, Optional
from acme import adders as adders_lib
from acme import datasets
from acme import specs
from acme import types
from acme.adders import reverb as adders
import reverb
class ReverbReplay:
server: reverb.Server
adder: adders_lib.Adder
data_... | Creates a single process queue from an environment spec and extra_spec. |
29,085 | import dataclasses
from typing import Any, Callable, Dict, Iterator, Optional
from acme import adders as adders_lib
from acme import datasets
from acme import specs
from acme import types
from acme.adders import reverb as adders
import reverb
class ReverbReplay:
server: reverb.Server
adder: adders_lib.Adder
data_... | Single-process replay for sequence data from an environment spec. |
29,086 | from acme import specs
from acme import types
from acme.wrappers import base
import dm_env
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `_convert_spec` function. Write a Python function `def _convert_spec(nested_spec: types.NestedSpec) -> types.NestedSpe... | Convert a nested spec. |
29,087 | from acme import specs
from acme import types
from acme.wrappers import base
import dm_env
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `_convert_value` function. Write a Python function `def _convert_value(nested_value: types.Nest) -> types.Nest` to sol... | Convert a nested value given a desired nested spec. |
29,088 | import os.path
import tempfile
from typing import Callable, Optional, Sequence, Tuple, Union
from acme.utils import paths
from acme.wrappers import base
import dm_env
import matplotlib
import matplotlib.animation as anim
import matplotlib.pyplot as plt
import numpy as np
The provided code snippet includes necessary ... | Generates a matplotlib animation from a stack of frames. |
29,089 | from typing import Any
from acme.wrappers import base
import dm_env
from dm_env import specs
import numpy as np
import tree
def _expand_scalar_spec_shape(spec: specs.Array) -> specs.Array:
if not spec.shape:
# NOTE: This line upcasts the spec to an Array to avoid edge cases (as in
# DiscreteSpec) where we ca... | null |
29,090 | from typing import Any
from acme.wrappers import base
import dm_env
from dm_env import specs
import numpy as np
import tree
def _expand_scalar_array_shape(array: np.ndarray) -> np.ndarray:
return array if array.shape else np.expand_dims(array, axis=-1) | null |
29,091 | from typing import Any, Dict, List, Optional
from acme import specs
from acme import types
import dm_env
import gym
from gym import spaces
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `_convert_to_spec` function. Write a Python function `def _convert_to_... | Converts an OpenAI Gym space to a dm_env spec or nested structure of specs. Box, MultiBinary and MultiDiscrete Gym spaces are converted to BoundedArray specs. Discrete OpenAI spaces are converted to DiscreteArray specs. Tuple and Dict spaces are recursively converted to tuples and dictionaries of specs. Args: space: Th... |
29,092 | from typing import Callable, Sequence
import dm_env
The provided code snippet includes necessary dependencies for implementing the `wrap_all` function. Write a Python function `def wrap_all( environment: dm_env.Environment, wrappers: Sequence[Callable[[dm_env.Environment], dm_env.Environment]], ) -> dm_env.Env... | Given an environment, wrap it in a list of wrappers. |
29,093 | from typing import Sequence, Optional
from acme import types
from acme.wrappers import base
import dm_env
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `_concat` function. Write a Python function `def _concat(values: types.NestedArray) -> np.ndarray` to s... | Concatenates the leaves of `values` along the leading dimension. Treats scalars as 1d arrays and expects that the shapes of all leaves are the same except for the leading dimension. Args: values: the nested arrays to concatenate. Returns: The concatenated array. |
29,094 | from typing import Sequence, Optional
from acme import types
from acme.wrappers import base
import dm_env
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `_zeros_like` function. Write a Python function `def _zeros_like(nest, dtype=None)` to solve the follow... | Generate a nested NumPy array according to spec. |
29,095 | from typing import Any, Dict, List, Optional
import warnings
from acme import specs
from acme import types
from acme import wrappers
from acme.multiagent import types as ma_types
from acme.wrappers import multiagent_dict_key_wrapper
import dm_env
import gym
from gym import spaces
import jax
import numpy as np
import tr... | Converts multigrid Gym space to an Acme multiagent spec. Args: space: The Gym space to convert. num_agents: the number of agents. name: Optional name to apply to all return spec(s). Returns: A dm_env spec or nested structure of specs, corresponding to the input space. |
29,096 | from typing import Any, Dict, List, Optional
import warnings
from acme import specs
from acme import types
from acme import wrappers
from acme.multiagent import types as ma_types
from acme.wrappers import multiagent_dict_key_wrapper
import dm_env
import gym
from gym import spaces
import jax
import numpy as np
import tr... | Returns multigrid observations converted to agent-index-first format. By default, multigrid observations are structured as: observation['image'][agent_index] observation['direction'][agent_index] ... However, multiagent Acme expects observations with agent indices first: observation[agent_index]['image'] observation[ag... |
29,097 | from acme import specs
from acme import types
from acme.wrappers import base
import dm_env
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `_convert_spec` function. Write a Python function `def _convert_spec(nested_spec: types.NestedSpec) -> types.NestedSpe... | Converts all bounded specs in nested spec to the canonical scale. |
29,098 | from acme import specs
from acme import types
from acme.wrappers import base
import dm_env
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `_scale_nested_action` function. Write a Python function `def _scale_nested_action( nested_action: types.NestedArr... | Converts a canonical nested action back to the given nested action spec. |
29,099 | import dataclasses
from typing import Dict, Sequence, Tuple, Union
from acme.tf.losses import mpo
import sonnet as snt
import tensorflow as tf
import tensorflow_probability as tfp
The provided code snippet includes necessary dependencies for implementing the `compute_weights_and_temperature_loss` function. Write a Pyt... | Computes normalized importance weights for the policy optimization. Args: q_values: Q-values associated with the actions sampled from the target policy; expected shape [N, B, K]. epsilons: Desired per-objective constraints on the KL between the target and non-parametric policies; expected shape [K]. temperature: Per-ob... |
29,100 | from typing import Dict, Tuple, Union
import sonnet as snt
import tensorflow as tf
import tensorflow_probability as tfp
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( ... | Computes normalized importance weights for the policy optimization. Args: q_values: Q-values associated with the actions sampled from the target policy; expected shape [N, B]. epsilon: Desired constraint on the KL between the target and non-parametric policies. temperature: Scalar used to temper the Q-values before com... |
29,101 | from typing import Dict, Tuple, Union
import sonnet as snt
import tensorflow as tf
import tensorflow_probability as tfp
The provided code snippet includes necessary dependencies for implementing the `compute_nonparametric_kl_from_normalized_weights` function. Write a Python function `def compute_nonparametric_kl_from_... | Estimate the actualized KL between the non-parametric and target policies. |
29,102 | from typing import Dict, Tuple, Union
import sonnet as snt
import tensorflow as tf
import tensorflow_probability as tfp
The provided code snippet includes necessary dependencies for implementing the `compute_cross_entropy_loss` function. Write a Python function `def compute_cross_entropy_loss( sampled_actions: tf.... | Compute cross-entropy online and the reweighted target policy. Args: sampled_actions: samples used in the Monte Carlo integration in the policy loss. Expected shape is [N, B, ...], where N is the number of sampled actions and B is the number of sampled states. normalized_weights: target policy multiplied by the exponen... |
29,103 | from typing import Dict, Tuple, Union
import sonnet as snt
import tensorflow as tf
import tensorflow_probability as tfp
The provided code snippet includes necessary dependencies for implementing the `compute_parametric_kl_penalty_and_dual_loss` function. Write a Python function `def compute_parametric_kl_penalty_and_d... | Computes the KL cost to be added to the Lagragian and its dual loss. The KL cost is simply the alpha-weighted KL divergence and it is added as a regularizer to the policy loss. The dual variable alpha itself has a loss that can be minimized to adapt the strength of the regularizer to keep the KL between consecutive upd... |
29,104 | from acme.tf import networks
import tensorflow as tf
def l2_project( # pylint: disable=invalid-name
Zp: tf.Tensor,
P: tf.Tensor,
Zq: tf.Tensor,
) -> tf.Tensor:
"""Project distribution (Zp, P) onto support Zq under the L2-metric over CDFs.
This projection works for any support Zq.
Let Kq be len(Zq) an... | Implements the Categorical Distributional TD(0)-learning loss. |
29,105 | from acme.tf import networks
import tensorflow as tf
def multiaxis_l2_project( # pylint: disable=invalid-name
Zp: tf.Tensor,
P: tf.Tensor,
Zq: tf.Tensor,
) -> tf.Tensor:
"""Project distribution (Zp, P) onto support Zq under the L2-metric over CDFs.
Let source support Zp's shape be described as (B, *C, ... | Implements a multi-axis categorical distributional TD(0)-learning loss. All arguments may have a leading batch axis, but q_tm1.logits, and one of r_t or d_t *must* have a leading batch axis. Args: q_tm1: Previous timestep's value distribution. r_t: Reward. d_t: Discount. q_t: Current timestep's value distribution. Retu... |
29,106 | from typing import Iterable, NamedTuple, Sequence
import tensorflow as tf
import trfl
class LossCoreExtra(NamedTuple):
targets: tf.Tensor
errors: tf.Tensor
def _compute_n_step_sequence_targets(
r_t: tf.Tensor,
pcont_t: tf.Tensor,
bootstrap_value: tf.Tensor,
n: int,
) -> tf.Tensor:
"""Computes n-st... | Helper function for computing transformed loss on sequences. Args: qs: 3-D tensor corresponding to the Q-values to be learned. Shape is [T+1, B, A]. targnet_qs: Like `qs`, but in the target network setting, these values should be computed by the target network. Shape is [T+1, B, A]. actions: 2-D tensor holding the indi... |
29,107 | import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `huber` function. Write a Python function `def huber(inputs: tf.Tensor, quadratic_linear_boundary: float) -> tf.Tensor` to solve the following problem:
Calculates huber loss of `inputs`. For each value x in `inputs`,... | Calculates huber loss of `inputs`. For each value x in `inputs`, the following is calculated: ``` 0.5 * x^2 if |x| <= d 0.5 * d^2 + d * (|x| - d) if |x| > d ``` where d is `quadratic_linear_boundary`. Args: inputs: Input Tensor to calculate the huber loss on. quadratic_linear_boundary: The point where the huber loss fu... |
29,108 | from typing import Optional
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `dpg` function. Write a Python function `def dpg( q_max: tf.Tensor, a_max: tf.Tensor, tape: tf.GradientTape, dqda_clipping: Optional[float] = None, clip_norm: bool = Fa... | Deterministic policy gradient loss, similar to trfl.dpg. |
29,109 | import functools
from typing import List, Optional, Union
from acme import types
from acme.utils import tree_utils
import sonnet as snt
import tensorflow as tf
import tree
The provided code snippet includes necessary dependencies for implementing the `batch_to_sequence` function. Write a Python function `def batch_to_... | Converts data between sequence-major and batch-major format. |
29,110 | import functools
from typing import List, Optional, Union
from acme import types
from acme.utils import tree_utils
import sonnet as snt
import tensorflow as tf
import tree
def tile_tensor(tensor: tf.Tensor, multiple: int) -> tf.Tensor:
"""Tiles `multiple` copies of `tensor` along a new leading axis."""
rank = len(t... | Tiles tensors in a nested structure along a new leading axis. |
29,111 | import functools
from typing import List, Optional, Union
from acme import types
from acme.utils import tree_utils
import sonnet as snt
import tensorflow as tf
import tree
The provided code snippet includes necessary dependencies for implementing the `to_numpy` function. Write a Python function `def to_numpy(nest: typ... | Converts a nest of Tensors to a nest of numpy arrays. |
29,112 | import functools
from typing import List, Optional, Union
from acme import types
from acme.utils import tree_utils
import sonnet as snt
import tensorflow as tf
import tree
The provided code snippet includes necessary dependencies for implementing the `to_numpy_squeeze` function. Write a Python function `def to_numpy_s... | Converts a nest of Tensors to a nest of numpy arrays and squeeze axis. |
29,113 | import abc
import datetime
import os
import pickle
import time
from typing import Mapping, Optional, Union
from absl import logging
from acme import core
from acme.utils import signals
from acme.utils import paths
import sonnet as snt
import tensorflow as tf
import tree
from tensorflow.python.saved_model import revived... | Create a thin wrapper around a module to make it snapshottable. |
29,114 | from typing import Callable, Optional, Sequence, Union
import sonnet as snt
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `_preprocess_inputs` function. Write a Python function `def _preprocess_inputs(inputs: tf.Tensor, output_dtype: tf.DType) -> tf.Tensor` to s... | Returns the `Tensor` corresponding to the preprocessed inputs. |
29,115 | from typing import Callable, Optional, Sequence
from acme import types
from acme.tf import utils as tf2_utils
from acme.tf.networks import base
import sonnet as snt
import tensorflow as tf
def _uniform_initializer():
return tf.initializers.VarianceScaling(
distribution='uniform', mode='fan_out', scale=0.333) | null |
29,116 | from typing import Dict, Union
from acme import types
from acme.adders.reverb import base
import jax
import jax.numpy as jnp
import numpy as np
import tree
def zeros_like(x: Union[np.ndarray, int, float, np.number]):
"""Returns a zero-filled object of the same (d)type and shape as the input.
The difference between ... | Return a list of steps with the final step zero-filled. |
29,117 | from typing import Dict, Union
from acme import types
from acme.adders.reverb import base
import jax
import jax.numpy as jnp
import numpy as np
import tree
The provided code snippet includes necessary dependencies for implementing the `calculate_priorities` function. Write a Python function `def calculate_priorities( ... | Helper used to calculate the priority of a Trajectory or Transition. This helper converts the leaves of the Trajectory or Transition from `reverb.TrajectoryColumn` objects into numpy arrays. The converted Trajectory or Transition is then passed into each of the functions in `priority_fns`. Args: priority_fns: a mapping... |
29,118 | import itertools
import time
from typing import Callable, List, Optional, Sequence, Sized
from absl import logging
from acme import specs
from acme import types
from acme.adders import base as adders_base
from acme.adders.reverb import base as reverb_base
from acme.adders.reverb import sequence as sequence_adder
from a... | null |
29,119 | import itertools
import time
from typing import Callable, List, Optional, Sequence, Sized
from absl import logging
from acme import specs
from acme import types
from acme.adders import base as adders_base
from acme.adders.reverb import base as reverb_base
from acme.adders.reverb import sequence as sequence_adder
from a... | Generates configs that replicates the behaviour of NStepTransitionAdder. Please see the docstring of NStepTransitionAdder for more details. NOTE! In contrast to NStepTransitionAdder, the trajectories written by the `StructuredWriter` does not include the precomputed cumulative reward and discounts. Instead the trajecto... |
29,120 | import itertools
import time
from typing import Callable, List, Optional, Sequence, Sized
from absl import logging
from acme import specs
from acme import types
from acme.adders import base as adders_base
from acme.adders.reverb import base as reverb_base
from acme.adders.reverb import sequence as sequence_adder
from a... | Converts an (n+1)-step trajectory into an n-step transition. |
29,121 | import abc
import time
from typing import Callable, Iterable, Mapping, NamedTuple, Optional, Sized, Union, Tuple
from absl import logging
from acme import specs
from acme import types
from acme.adders import base
import dm_env
import numpy as np
import reverb
import tensorflow as tf
import tree
def spec_like_to_tensor... | null |
29,122 | import copy
from typing import Optional, Tuple
from acme import specs
from acme import types
from acme.adders.reverb import base
from acme.adders.reverb import utils
from acme.utils import tree_utils
import numpy as np
import reverb
import tree
The provided code snippet includes necessary dependencies for implementing... | Like np.broadcast, but for specs.Array. Args: *args: one or more specs.Array instances. Returns: A specs.Array with the broadcasted shape and dtype of the specs in *args. |
29,123 | import enum
from acme import types
from acme.datasets import reverb as reverb_dataset
import reverb
import tensorflow as tf
class CropType(enum.Enum):
"""Types of cropping supported by the image aumentation transforms.
BILINEAR: Continuously randomly located then bilinearly interpolated.
ALIGNED: Aligned with inp... | Pad and crop image to mimic a random translation with mirroring at edges. This implements the image augmentation from section 3.1 in (Kostrikov et al.) https://arxiv.org/abs/2004.13649. Args: img: The image to pad and crop. Its dimensions are [..., H, W, C] where ... are batch dimensions (if it has any). pad_size: The ... |
29,124 | import enum
from acme import types
from acme.datasets import reverb as reverb_dataset
import reverb
import tensorflow as tf
import reverb
The provided code snippet includes necessary dependencies for implementing the `make_transform` function. Write a Python function `def make_transform( observation_transform: ty... | Creates the appropriate dataset transform for the given signature. |
29,125 | import logging
from typing import Any, Iterator, Optional, Tuple, Sequence
from acme import specs
from acme import types
from flax import jax_utils
import jax
import jax.numpy as jnp
import numpy as np
import rlds
import tensorflow as tf
import tensorflow_datasets as tfds
def _dataset_size_upperbound(dataset: tf.data.... | null |
29,126 | import logging
from typing import Any, Iterator, Optional, Tuple, Sequence
from acme import specs
from acme import types
from flax import jax_utils
import jax
import jax.numpy as jnp
import numpy as np
import rlds
import tensorflow as tf
import tensorflow_datasets as tfds
_BEST_DIVISOR = 128
def _pad(x: jnp.ndarray) -... | null |
29,127 | import logging
from typing import Any, Iterator, Optional, Tuple, Sequence
from acme import specs
from acme import types
from flax import jax_utils
import jax
import jax.numpy as jnp
import numpy as np
import rlds
import tensorflow as tf
import tensorflow_datasets as tfds
def _unpad(x: jnp.ndarray, shape: Sequence[int... | null |
29,128 | import time
from typing import Sequence
from absl import app
from absl import logging
from acme import adders
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.datasets import reverb as datasets
from acme.testing import fakes
import numpy as np
import reverb
from reverb import rate_limite... | Create tables to insert data into. |
29,129 | import time
from typing import Sequence
from absl import app
from absl import logging
from acme import adders
from acme import specs
from acme.adders import reverb as adders_reverb
from acme.datasets import reverb as datasets
from acme.testing import fakes
import numpy as np
import reverb
from reverb import rate_limite... | null |
29,130 | import collections
import os
from typing import Callable, Mapping, Optional, Union
from acme import specs
from acme import types
from acme.adders import reverb as adders
import reverb
import tensorflow as tf
Transform = Callable[[reverb.ReplaySample], reverb.ReplaySample]
import reverb
The provided code snippet inclu... | Make a TensorFlow dataset backed by a Reverb trajectory replay service. Arguments: server_address: Address of the Reverb server. batch_size: Batch size of the returned dataset. prefetch_size: The number of elements to prefetch from the original dataset. Note that Reverb may do some internal prefetching in addition to t... |
29,131 | import operator
import time
from typing import Optional, Sequence
from acme import core
from acme.utils import counting
from acme.utils import loggers
from acme.wrappers import open_spiel_wrapper
import dm_env
from dm_env import specs
import numpy as np
import tree
import pyspiel
def _generate_zeros_from_spec(spec: sp... | null |
29,132 | from acme import types
import jax
import numpy as np
import reverb
from reverb import item_selectors
from reverb import rate_limiters
from reverb import reverb_types
import tensorflow as tf
import tree
def _make_selector_from_key_distribution_options(
options) -> reverb_types.SelectorType:
"""Returns a Selector f... | Build a replay table out of its specs in a TableInfo. Args: table_info: A TableInfo containing the Table specs. Returns: A reverb replay table matching the info specs. |
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