sinergym.utils.wrappers.NormalizeAction

class sinergym.utils.wrappers.NormalizeAction(env: EplusEnv, normalize_range: Tuple[float, float] = (-1.0, 1.0))

Wrapper to normalize action space.

__init__(env: EplusEnv, normalize_range: Tuple[float, float] = (-1.0, 1.0))

Wrapper to normalize action space in default continuous environment (not to combine with discrete environments). The action will be parsed to real action space before to send to the simulator (very useful ion DRL algorithms)

Parameters:

env (EplusEnv) – Original environment.
normalize_range (Tuple[float,float]) – Range to normalize action variable values. Defaults to values between [-1.0,1.0].

Methods

`__init__`(env[, normalize_range])	Wrapper to normalize action space in default continuous environment (not to combine with discrete environments).
`action`(action)	Returns a modified action before `env.step()` is called.
`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.
`render`()	Uses the `render()` of the `env` that can be overwritten to change the returned data.
`reset`(*[, seed, options])	Uses the `reset()` of the `env` that can be overwritten to change the returned data.
`reverting_action`(action)	This method maps a normalized action in a real action space.
`step`(action)	Runs the `env` `env.step()` using the modified `action` from `self.action()`.
`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.
`observation_space`	Return the `Env` `observation_space` unless overwritten then the wrapper `observation_space` is used.
`render_mode`	Returns the `Env` `render_mode`.
`reward_range`	Return the `Env` `reward_range` unless overwritten then the wrapper `reward_range` is used.
`spec`	Returns the `Env` `spec` attribute with the WrapperSpec if the wrapper inherits from EzPickle.
`unwrapped`	Returns the base environment of the wrapper.

action(action: Any)

Returns a modified action before env.step() is called.

Parameters:: action – The original step() actions
Returns:: The modified actions

logger = <Logger WRAPPER NormalizeAction (INFO)>

reverting_action(action: Any) → List[float]

This method maps a normalized action in a real action space.

Parameters:: action (Any) – Normalize action received in environment
Returns:: Action transformed in simulator real action space.
Return type:: List[float]