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 noisesigma (
ndarray
) – Scale of the noise (std here)dtype (
Union
[dtype
,None
,type
,_SupportsDType
[dtype
],str
,Tuple
[Any
,int
],Tuple
[Any
,Union
[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 noisesigma (
ndarray
) – Scale of the noisetheta (
float
) – Rate of mean reversiondt (
float
) – Timestep for the noiseinitial_noise (
Optional
[ndarray
]) – Initial value for the noise output, (if None: 0)dtype (
Union
[dtype
,None
,type
,_SupportsDType
[dtype
],str
,Tuple
[Any
,int
],Tuple
[Any
,Union
[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 usen_envs (
int
) – Number of parallel environments