Action Noise
- class stable_baselines3.common.noise.NormalActionNoise(mean, sigma, dtype=<class 'numpy.float32'>)[source]
A Gaussian action noise.
- Parameters:
mean (ndarray) – Mean value of the noise
sigma (ndarray) – Scale of the noise (std here)
dtype (dtype[Any] | None | Type[Any] | _SupportsDType[dtype[Any]] | str | Tuple[Any, int] | Tuple[Any, SupportsIndex | Sequence[SupportsIndex]] | List[Any] | _DTypeDict | Tuple[Any, Any]) – Type of the output noise
- class stable_baselines3.common.noise.OrnsteinUhlenbeckActionNoise(mean, sigma, theta=0.15, dt=0.01, initial_noise=None, dtype=<class 'numpy.float32'>)[source]
An Ornstein Uhlenbeck action noise, this is designed to approximate Brownian motion with friction.
Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
- Parameters:
mean (ndarray) – Mean of the noise
sigma (ndarray) – Scale of the noise
theta (float) – Rate of mean reversion
dt (float) – Timestep for the noise
initial_noise (ndarray | None) – Initial value for the noise output, (if None: 0)
dtype (dtype[Any] | None | Type[Any] | _SupportsDType[dtype[Any]] | str | Tuple[Any, int] | Tuple[Any, SupportsIndex | Sequence[SupportsIndex]] | List[Any] | _DTypeDict | Tuple[Any, Any]) – Type of the output noise
- class stable_baselines3.common.noise.VectorizedActionNoise(base_noise, n_envs)[source]
A Vectorized action noise for parallel environments.
- Parameters:
base_noise (ActionNoise) – Noise generator to use
n_envs (int) – Number of parallel environments