sinergym.utils.wrappers.DiscreteIncrementalWrapper

class sinergym.utils.wrappers.DiscreteIncrementalWrapper(env: Env, initial_values: List[float], delta_temp: float = 2.0, step_temp: float = 0.5)

A wrapper for an incremental setpoint discrete action space environment. WARNING: A environment with only temperature setpoints control must be used with this wrapper.

__init__(env: Env, initial_values: List[float], delta_temp: float = 2.0, step_temp: float = 0.5)

Parameters:

env – The original Sinergym env.
action_names – Name of the action variables with the setpoint control you want to do incremental.
initial_values – Initial values of the setpoints.
delta_temp – Maximum temperature variation in the setpoints in one step.
step_temp – Minimum temperature variation in the setpoints in one step.

Methods

`__init__`(env, initial_values[, delta_temp, ...])
`action`(action)	Takes the discrete action and transforms it to setpoints tuple.
`action_mapping`(action)
`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.
`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.
`is_discrete`
`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): Takes the discrete action and transforms it to setpoints tuple.

action_mapping(action: int) → List[float]

property is_discrete: bool

logger = <Logger WRAPPER DiscreteIncrementalWrapper (INFO)>