sinergym.utils.wrappers.NormalizeObservation

class sinergym.utils.wrappers.NormalizeObservation(env: Env, automatic_update: bool = True, epsilon: float = 1e-08, mean: list | float64 | str = None, var: list | float64 | str = None)
__init__(env: Env, automatic_update: bool = True, epsilon: float = 1e-08, mean: list | float64 | str = None, var: list | float64 | str = None)

Initializes the NormalizationWrapper. Mean and var values can be None and being updated during interaction with environment.

Parameters:
  • env (Env) – The environment to apply the wrapper.

  • automatic_update (bool, optional) – Whether or not to update the mean and variance values automatically. Defaults to True.

  • epsilon (float, optional) – A stability parameter used when scaling the observations. Defaults to 1e-8.

  • mean (list, np.float64, str, optional) – The mean value used for normalization. It can be a mean.txt path too. Defaults to None.

  • var (list, np.float64, str, optional) – The variance value used for normalization. It can be a var.txt path too. Defaults to None.

Methods

__init__(env[, automatic_update, epsilon, ...])

Initializes the NormalizationWrapper.

activate_update()

Activates the automatic update of the normalization wrapper.

class_name()

Returns the class name of the wrapper.

close()

Close the environment and save normalization calibration.

deactivate_update()

Deactivates the automatic update of the normalization wrapper.

get_wrapper_attr(name)

Gets an attribute from the wrapper and lower environments if name doesn't exist in this object.

has_wrapper_attr(name)

Checks if the given attribute is within the wrapper or its environment.

normalize(obs)

Normalizes the observation using the running mean and variance of the observations.

render()

Uses the render() of the env that can be overwritten to change the returned data.

reset(**kwargs)

Resets the environment and normalizes the observation.

set_mean(mean)

Sets the mean value of the observations.

set_var(var)

Sets the variance value of the observations.

set_wrapper_attr(name, value)

Sets an attribute on this wrapper or lower environment if name is already defined.

step(action)

Steps through the environment and normalizes the observation.

wrapper_spec(**kwargs)

Generates a WrapperSpec for the wrappers.

Attributes

action_space

Return the Env action_space unless overwritten then the wrapper action_space is used.

logger

mean

Returns the mean value of the observations.

metadata

Returns the Env metadata.

np_random

Returns the Env np_random attribute.

np_random_seed

Returns the base environment's np_random_seed.

observation_space

Return the Env observation_space unless overwritten then the wrapper observation_space is used.

render_mode

Returns the Env render_mode.

spec

Returns the Env spec attribute with the WrapperSpec if the wrapper inherits from EzPickle.

unwrapped

Returns the base environment of the wrapper.

var

Returns the variance value of the observations.

activate_update()

Activates the automatic update of the normalization wrapper. After calling this method, the normalization wrapper will update its calibration automatically.

close()

Close the environment and save normalization calibration.

deactivate_update()

Deactivates the automatic update of the normalization wrapper. After calling this method, the normalization wrapper will not update its calibration automatically.

logger = <Logger WRAPPER NormalizeObservation (INFO)>
property mean: float64 | None

Returns the mean value of the observations.

normalize(obs)

Normalizes the observation using the running mean and variance of the observations. If automatic_update is enabled, the running mean and variance will be updated too.

reset(**kwargs)

Resets the environment and normalizes the observation.

set_mean(mean: list | float64 | str)

Sets the mean value of the observations.

set_var(var: list | float64 | str)

Sets the variance value of the observations.

step(action: int | ndarray) Tuple[ndarray, float, bool, bool, Dict[str, Any]]

Steps through the environment and normalizes the observation.

property var: float64 | None

Returns the variance value of the observations.