sinergym.utils.wrappers.NormalizeObservation

class sinergym.utils.wrappers.NormalizeObservation(env: EplusEnv, epsilon: float = 1e-08)

__init__(env: EplusEnv, epsilon: float = 1e-08)

This wrapper will normalize observations s.t. each coordinate is centered with unit variance.

Parameters:

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

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

Methods

__init__(env[, epsilon])	This wrapper will normalize observations s.t.
class_name()	Returns the class name of the wrapper.
close()	Closes the wrapper and env.
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_wrapper_attr(name, value, *[, force])	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
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.

logger = <Logger WRAPPER NormalizeObservation (INFO)>

normalize(obs)

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

reset(**kwargs)

Resets the environment and normalizes the observation.

step(action)

Steps through the environment and normalizes the observation.