Memories#

Memories are storage components that allow agents to collect and use/reuse current or past experiences of their interaction with the environment or other types of information.

Memories
Random memory	\(\blacksquare\)	\(\blacksquare\)

Base class#

Note

This is the base class for all the other classes in this module. It provides the basic functionality for the other classes. It is not intended to be used directly.

Basic inheritance usage#

from typing import Union, Tuple, List

import torch

from skrl.memories.torch import Memory


class CustomMemory(Memory):
    def __init__(self, memory_size: int, num_envs: int = 1, device: Union[str, torch.device] = "cuda:0") -> None:
        """Custom memory

        :param memory_size: Maximum number of elements in the first dimension of each internal storage
        :type memory_size: int
        :param num_envs: Number of parallel environments (default: 1)
        :type num_envs: int, optional
        :param device: Device on which a torch tensor is or will be allocated (default: "cuda:0")
        :type device: str or torch.device, optional
        """
        super().__init__(memory_size, num_envs, device)

    def sample(self, names: Tuple[str], batch_size: int, mini_batches: int = 1) -> List[List[torch.Tensor]]:
        """Sample a batch from memory

        :param names: Tensors names from which to obtain the samples
        :type names: tuple or list of strings
        :param batch_size: Number of element to sample
        :type batch_size: int
        :param mini_batches: Number of mini-batches to sample (default: 1)
        :type mini_batches: int, optional

        :return: Sampled data from tensors sorted according to their position in the list of names.
                 The sampled tensors will have the following shape: (batch size, data size)
        :rtype: list of torch.Tensor list
        """
        # ================================
        # - sample a batch from memory.
        #   It is possible to generate only the sampling indexes and call self.sample_by_index(...)
        # ================================

from typing import Optional, Union, Tuple, List

import jaxlib
import jax.numpy as jnp

from skrl.memories.jax import Memory


class CustomMemory(Memory):
    def __init__(self, memory_size: int,
                 num_envs: int = 1,
                 device: Optional[jaxlib.xla_extension.Device] = None) -> None:
        """Custom memory

        :param memory_size: Maximum number of elements in the first dimension of each internal storage
        :type memory_size: int
        :param num_envs: Number of parallel environments (default: 1)
        :type num_envs: int, optional
        :param device: Device on which an array is or will be allocated (default: None)
        :type device: jaxlib.xla_extension.Device, optional
        """
        super().__init__(memory_size, num_envs, device)

    def sample(self, names: Tuple[str], batch_size: int, mini_batches: int = 1) -> List[List[jnp.ndarray]]:
        """Sample a batch from memory

        :param names: Tensors names from which to obtain the samples
        :type names: tuple or list of strings
        :param batch_size: Number of element to sample
        :type batch_size: int
        :param mini_batches: Number of mini-batches to sample (default: 1)
        :type mini_batches: int, optional

        :return: Sampled data from tensors sorted according to their position in the list of names.
                 The sampled tensors will have the following shape: (batch size, data size)
        :rtype: list of jnp.ndarray list
        """
        # ================================
        # - sample a batch from memory.
        #   It is possible to generate only the sampling indexes and call self.sample_by_index(...)
        # ================================

API (PyTorch)#

class skrl.memories.torch.base.Memory(memory_size: int, num_envs: int = 1, device: str | torch.device | None = None, export: bool = False, export_format: str = 'pt', export_directory: str = '')#

Bases: object

__init__(memory_size: int, num_envs: int = 1, device: str | torch.device | None = None, export: bool = False, export_format: str = 'pt', export_directory: str = '') → None#

Base class representing a memory with circular buffers

Buffers are torch tensors with shape (memory size, number of environments, data size). Circular buffers are implemented with two integers: a memory index and an environment index

Parameters:

memory_size (int) – Maximum number of elements in the first dimension of each internal storage
num_envs (int, optional) – Number of parallel environments (default: 1)
device (str or torch.device, optional) – Device on which a tensor/array is or will be allocated (default: None). If None, the device will be either "cuda" if available or "cpu"
export (bool, optional) – Export the memory to a file (default: False). If True, the memory will be exported when the memory is filled
export_format (str, optional) – Export format (default: "pt"). Supported formats: torch (pt), numpy (np), comma separated values (csv)
export_directory (str, optional) – Directory where the memory will be exported (default: ""). If empty, the agent’s experiment directory will be used

Raises:

ValueError – The export format is not supported

__len__() → int#

Compute and return the current (valid) size of the memory

The valid size is calculated as the memory_size * num_envs if the memory is full (filled). Otherwise, the memory_index * num_envs + env_index is returned

Returns:: Valid size
Return type:: int

add_samples(**tensors: torch.Tensor) → None#

Record samples in memory

Samples should be a tensor with 2-components shape (number of environments, data size). All tensors must be of the same shape

According to the number of environments, the following classification is made:

one environment: Store a single sample (tensors with one dimension) and increment the environment index (second index) by one
number of environments less than num_envs: Store the samples and increment the environment index (second index) by the number of the environments
number of environments equals num_envs: Store the samples and increment the memory index (first index) by one

Parameters:: tensors (dict) – Sampled data as key-value arguments where the keys are the names of the tensors to be modified. Non-existing tensors will be skipped
Raises:: ValueError – No tensors were provided or the tensors have incompatible shapes

create_tensor(name: str, size: int | Tuple[int] | gym.Space | gymnasium.Space, dtype: torch.dtype | None = None, keep_dimensions: bool = False) → bool#

Create a new internal tensor in memory

The tensor will have a 3-components shape (memory size, number of environments, size). The internal representation will use _tensor_<name> as the name of the class property

Parameters:

name (str) – Tensor name (the name has to follow the python PEP 8 style)
size (int, tuple or list of integers, gym.Space, or gymnasium.Space) – Number of elements in the last dimension (effective data size). The product of the elements will be computed for sequences or gym/gymnasium spaces
dtype (torch.dtype or None, optional) – Data type (torch.dtype) (default: None). If None, the global default torch data type will be used
keep_dimensions (bool, optional) – Whether or not to keep the dimensions defined through the size parameter (default: False)

Raises:

ValueError – The tensor name exists already but the size or dtype are different

Returns:

True if the tensor was created, otherwise False

Return type:

bool

get_sampling_indexes() → tuple | ndarray | torch.Tensor#

Get the last indexes used for sampling

Returns:: Last sampling indexes
Return type:: tuple or list, numpy.ndarray or torch.Tensor

get_tensor_by_name(name: str, keepdim: bool = True) → torch.Tensor#

Get a tensor by its name

Parameters:

name (str) – Name of the tensor to retrieve
keepdim (bool, optional) – Keep the tensor’s shape (memory size, number of environments, size) (default: True) If False, the returned tensor will have a shape of (memory size * number of environments, size)

Raises:

KeyError – The tensor does not exist

Returns:

Tensor

Return type:

torch.Tensor

get_tensor_names() → Tuple[str]#

Get the name of the internal tensors in alphabetical order

Returns:: Tensor names without internal prefix (_tensor_)
Return type:: tuple of strings

load(path: str) → None#

Load the memory from a file

Supported formats: - PyTorch (pt) - NumPy (npz) - Comma-separated values (csv)

Parameters:: path (str) – Path to the file where the memory will be loaded
Raises:: ValueError – If the format is not supported

reset() → None#

Reset the memory by cleaning internal indexes and flags

Old data will be retained until overwritten, but access through the available methods will not be guaranteed

Default values of the internal indexes and flags

filled: False
env_index: 0
memory_index: 0

sample(names: Tuple[str], batch_size: int, mini_batches: int = 1, sequence_length: int = 1) → List[List[torch.Tensor]]#

Data sampling method to be implemented by the inheriting classes

Parameters:

names (tuple or list of strings) – Tensors names from which to obtain the samples
batch_size (int) – Number of element to sample
mini_batches (int, optional) – Number of mini-batches to sample (default: 1)
sequence_length (int, optional) – Length of each sequence (default: 1)

Raises:

NotImplementedError – The method has not been implemented

Returns:

Sampled data from tensors sorted according to their position in the list of names. The sampled tensors will have the following shape: (batch size, data size)

Return type:

list of torch.Tensor list

sample_all(names: Tuple[str], mini_batches: int = 1, sequence_length: int = 1) → List[List[torch.Tensor]]#

Sample all data from memory

Parameters:

names (tuple or list of strings) – Tensors names from which to obtain the samples
mini_batches (int, optional) – Number of mini-batches to sample (default: 1)
sequence_length (int, optional) – Length of each sequence (default: 1)

Returns:

Sampled data from memory. The sampled tensors will have the following shape: (memory size * number of environments, data size)

Return type:

list of torch.Tensor list

sample_by_index(names: Tuple[str], indexes: tuple | ndarray | torch.Tensor, mini_batches: int = 1) → List[List[torch.Tensor]]#

Sample data from memory according to their indexes

Parameters:

names (tuple or list of strings) – Tensors names from which to obtain the samples
indexes (tuple or list, numpy.ndarray or torch.Tensor) – Indexes used for sampling
mini_batches (int, optional) – Number of mini-batches to sample (default: 1)

Returns:

Sampled data from tensors sorted according to their position in the list of names. The sampled tensors will have the following shape: (number of indexes, data size)

Return type:

list of torch.Tensor list

save(directory: str = '', format: str = 'pt') → None#

Save the memory to a file

Supported formats:

PyTorch (pt)
NumPy (npz)
Comma-separated values (csv)

Parameters:

directory (str) – Path to the folder where the memory will be saved. If not provided, the directory defined in the constructor will be used
format (str, optional) – Format of the file where the memory will be saved (default: "pt")

Raises:

ValueError – If the format is not supported

set_tensor_by_name(name: str, tensor: torch.Tensor) → None#

Set a tensor by its name

Parameters:

name (str) – Name of the tensor to set
tensor (torch.Tensor) – Tensor to set

Raises:

KeyError – The tensor does not exist

share_memory() → None#: Share the tensors between processes

API (JAX)#

class skrl.memories.jax.base.Memory(memory_size: int, num_envs: int = 1, device: jax.Device | None = None, export: bool = False, export_format: str = 'pt', export_directory: str = '')#

Bases: object

__init__(memory_size: int, num_envs: int = 1, device: jax.Device | None = None, export: bool = False, export_format: str = 'pt', export_directory: str = '') → None#

Base class representing a memory with circular buffers

Buffers are jax or numpy arrays with shape (memory size, number of environments, data size). Circular buffers are implemented with two integers: a memory index and an environment index

Parameters:

memory_size (int) – Maximum number of elements in the first dimension of each internal storage
num_envs (int, optional) – Number of parallel environments (default: 1)
device (str or jax.Device, optional) – Device on which a tensor/array is or will be allocated (default: None). If None, the device will be either "cuda" if available or "cpu"
export (bool, optional) – Export the memory to a file (default: False). If True, the memory will be exported when the memory is filled
export_format (str, optional) – Export format (default: "pt"). Supported formats: torch (pt), numpy (np), comma separated values (csv)
export_directory (str, optional) – Directory where the memory will be exported (default: ""). If empty, the agent’s experiment directory will be used

Raises:

ValueError – The export format is not supported

__len__() → int#

Compute and return the current (valid) size of the memory

The valid size is calculated as the memory_size * num_envs if the memory is full (filled). Otherwise, the memory_index * num_envs + env_index is returned

Returns:: Valid size
Return type:: int

add_samples(**tensors: Mapping[str, ndarray | jax.Array]) → None#

Record samples in memory

Samples should be a tensor with 2-components shape (number of environments, data size). All tensors must be of the same shape

According to the number of environments, the following classification is made:

one environment: Store a single sample (tensors with one dimension) and increment the environment index (second index) by one
number of environments less than num_envs: Store the samples and increment the environment index (second index) by the number of the environments
number of environments equals num_envs: Store the samples and increment the memory index (first index) by one

Parameters:: tensors (dict) – Sampled data as key-value arguments where the keys are the names of the tensors to be modified. Non-existing tensors will be skipped
Raises:: ValueError – No tensors were provided or the tensors have incompatible shapes

create_tensor(name: str, size: int | Tuple[int] | gym.Space | gymnasium.Space, dtype: dtype | None = None, keep_dimensions: bool = False) → bool#

Create a new internal tensor in memory

The tensor will have a 3-components shape (memory size, number of environments, size). The internal representation will use _tensor_<name> as the name of the class property

Parameters:

name (str) – Tensor name (the name has to follow the python PEP 8 style)
size (int, tuple or list of integers or gym.Space) – Number of elements in the last dimension (effective data size). The product of the elements will be computed for sequences or gym/gymnasium spaces
dtype (np.dtype or None, optional) – Data type (np.dtype) (default: None). If None, the global default jax.numpy.float32 data type will be used
keep_dimensions (bool, optional) – Whether or not to keep the dimensions defined through the size parameter (default: False)

Raises:

ValueError – The tensor name exists already but the size or dtype are different

Returns:

True if the tensor was created, otherwise False

Return type:

bool

get_sampling_indexes() → tuple | ndarray | jax.Array#

Get the last indexes used for sampling

Returns:: Last sampling indexes
Return type:: tuple or list, np.ndarray or jax.Array

get_tensor_by_name(name: str, keepdim: bool = True) → ndarray | jax.Array#

Get a tensor by its name

Parameters:

name (str) – Name of the tensor to retrieve
keepdim (bool, optional) – Keep the tensor’s shape (memory size, number of environments, size) (default: True) If False, the returned tensor will have a shape of (memory size * number of environments, size)

Raises:

KeyError – The tensor does not exist

Returns:

Tensor

Return type:

np.ndarray or jax.Array

get_tensor_names() → Tuple[str]#

Get the name of the internal tensors in alphabetical order

Returns:: Tensor names without internal prefix (_tensor_)
Return type:: tuple of strings

load(path: str) → None#

Load the memory from a file

Supported formats: - PyTorch (pt) - NumPy (npz) - Comma-separated values (csv)

Parameters:: path (str) – Path to the file where the memory will be loaded
Raises:: ValueError – If the format is not supported

reset() → None#

Reset the memory by cleaning internal indexes and flags

Old data will be retained until overwritten, but access through the available methods will not be guaranteed

Default values of the internal indexes and flags

filled: False
env_index: 0
memory_index: 0

sample(names: Tuple[str], batch_size: int, mini_batches: int = 1, sequence_length: int = 1) → List[List[ndarray | jax.Array]]#

Data sampling method to be implemented by the inheriting classes

Parameters:

names (tuple or list of strings) – Tensors names from which to obtain the samples
batch_size (int) – Number of element to sample
mini_batches (int, optional) – Number of mini-batches to sample (default: 1)
sequence_length (int, optional) – Length of each sequence (default: 1)

Raises:

NotImplementedError – The method has not been implemented

Returns:

Sampled data from tensors sorted according to their position in the list of names. The sampled tensors will have the following shape: (batch size, data size)

Return type:

list of np.ndarray or jax.Array list

sample_all(names: Tuple[str], mini_batches: int = 1, sequence_length: int = 1) → List[List[ndarray | jax.Array]]#

Sample all data from memory

Parameters:

names (tuple or list of strings) – Tensors names from which to obtain the samples
mini_batches (int, optional) – Number of mini-batches to sample (default: 1)
sequence_length (int, optional) – Length of each sequence (default: 1)

Returns:

Sampled data from memory. The sampled tensors will have the following shape: (memory size * number of environments, data size)

Return type:

list of np.ndarray or jax.Array list

sample_by_index(names: Tuple[str], indexes: tuple | ndarray | jax.Array, mini_batches: int = 1) → List[List[ndarray | jax.Array]]#

Sample data from memory according to their indexes

Parameters:

names (tuple or list of strings) – Tensors names from which to obtain the samples
indexes (tuple or list, np.ndarray or jax.Array) – Indexes used for sampling
mini_batches (int, optional) – Number of mini-batches to sample (default: 1)

Returns:

Sampled data from tensors sorted according to their position in the list of names. The sampled tensors will have the following shape: (number of indexes, data size)

Return type:

list of np.ndarray or jax.Array list

save(directory: str = '', format: str = 'pt') → None#

Save the memory to a file

Supported formats:

PyTorch (pt)
NumPy (npz)
Comma-separated values (csv)

Parameters:

directory (str) – Path to the folder where the memory will be saved. If not provided, the directory defined in the constructor will be used
format (str, optional) – Format of the file where the memory will be saved (default: "pt")

Raises:

ValueError – If the format is not supported

set_tensor_by_name(name: str, tensor: ndarray | jax.Array) → None#

Set a tensor by its name

Parameters:

name (str) – Name of the tensor to set
tensor (np.ndarray or jax.Array) – Tensor to set

Raises:

KeyError – The tensor does not exist

share_memory() → None#: Share the tensors between processes