Ornstein-Uhlenbeck noise¶

Noise generated by a stochastic process that is characterized by its mean-reverting behavior.

Usage¶

The noise usage is defined in each agent’s configuration dictionary. A noise instance is set under the "noise" sub-key. The following examples show how to set the noise for an agent:

from skrl.resources.noises.torch import OrnsteinUhlenbeckNoise

cfg = DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.2, base_scale=1.0, device="cuda:0")

from skrl.resources.noises.jax import OrnsteinUhlenbeckNoise

cfg = DEFAULT_CONFIG.copy()
cfg["exploration"]["noise"] = OrnsteinUhlenbeckNoise(theta=0.15, sigma=0.2, base_scale=1.0)

API (PyTorch)¶

class skrl.resources.noises.torch.ornstein_uhlenbeck.OrnsteinUhlenbeckNoise(theta: float, sigma: float, base_scale: float, mean: float = 0, std: float = 1, device: str | torch.device | None = None)¶

Bases: Noise

__init__(theta: float, sigma: float, base_scale: float, mean: float = 0, std: float = 1, device: str | torch.device | None = None) → None¶

Class representing an Ornstein-Uhlenbeck noise

Parameters:

theta (float) – Factor to apply to current internal state
sigma (float) – Factor to apply to the normal distribution
base_scale (float) – Factor to apply to returned noise
mean (float, optional) – Mean of the normal distribution (default: 0.0)
std (float, optional) – Standard deviation of the normal distribution (default: 1.0)
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"

Example:

>>> noise = OrnsteinUhlenbeckNoise(theta=0.1, sigma=0.2, base_scale=0.5)

sample(size: Tuple[int] | torch.Size) → torch.Tensor¶

Sample an Ornstein-Uhlenbeck noise

Parameters:: size (tuple or list of int, or torch.Size) – Shape of the sampled tensor
Returns:: Sampled noise
Return type:: torch.Tensor

Example:

>>> noise.sample((3, 2))
tensor([[-0.0452,  0.0162],
        [ 0.0649, -0.0708],
        [-0.0211,  0.0066]], device='cuda:0')

>>> x = torch.rand(3, 2, device="cuda:0")
>>> noise.sample(x.shape)
tensor([[-0.0540,  0.0461],
        [ 0.1117, -0.1157],
        [-0.0074,  0.0420]], device='cuda:0')

sample_like(tensor: torch.Tensor) → torch.Tensor¶

Sample a noise with the same size (shape) as the input tensor

This method will call the sampling method as follows .sample(tensor.shape)

Parameters:: tensor (torch.Tensor) – Input tensor used to determine output tensor size (shape)
Returns:: Sampled noise
Return type:: torch.Tensor

Example:

>>> x = torch.rand(3, 2, device="cuda:0")
>>> noise.sample_like(x)
tensor([[-0.0423, -0.1325],
        [-0.0639, -0.0957],
        [-0.1367,  0.1031]], device='cuda:0')

API (JAX)¶

class skrl.resources.noises.jax.ornstein_uhlenbeck.OrnsteinUhlenbeckNoise(theta: float, sigma: float, base_scale: float, mean: float = 0, std: float = 1, device: str | jax.Device | None = None)¶

Bases: Noise

__init__(theta: float, sigma: float, base_scale: float, mean: float = 0, std: float = 1, device: str | jax.Device | None = None) → None¶

Class representing an Ornstein-Uhlenbeck noise

Parameters:

theta (float) – Factor to apply to current internal state
sigma (float) – Factor to apply to the normal distribution
base_scale (float) – Factor to apply to returned noise
mean (float, optional) – Mean of the normal distribution (default: 0.0)
std (float, optional) – Standard deviation of the normal distribution (default: 1.0)
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"

Example:

>>> noise = OrnsteinUhlenbeckNoise(theta=0.1, sigma=0.2, base_scale=0.5)

sample(size: Tuple[int]) → ndarray | jax.Array¶

Sample an Ornstein-Uhlenbeck noise

Parameters:: size (tuple or list of int) – Shape of the sampled tensor
Returns:: Sampled noise
Return type:: np.ndarray or jax.Array

Example:

>>> noise.sample((3, 2))
Array([[ 0.01878439, -0.12833427],
       [ 0.06494182,  0.12490594],
       [ 0.024447  , -0.01174496]], dtype=float32)

>>> x = jax.random.uniform(jax.random.PRNGKey(0), (3, 2))
>>> noise.sample(x.shape)
Array([[ 0.17988093, -1.2289404 ],
       [ 0.6218886 ,  1.1961104 ],
       [ 0.23410667, -0.11247082]], dtype=float32)

sample_like(tensor: ndarray | jax.Array) → ndarray | jax.Array¶

Sample a noise with the same size (shape) as the input tensor

This method will call the sampling method as follows .sample(tensor.shape)

Parameters:: tensor (np.ndarray or jax.Array) – Input tensor used to determine output tensor size (shape)
Returns:: Sampled noise
Return type:: np.ndarray or jax.Array

Example:

>>> x = jax.random.uniform(jax.random.PRNGKey(0), (3, 2))
>>> noise.sample_like(x)
Array([[0.57450044, 0.09968603],
       [0.7419659 , 0.8941783 ],
       [0.59656656, 0.45325184]], dtype=float32)