sinergym.utils.wrappers.MultiObjectiveReward
- class sinergym.utils.wrappers.MultiObjectiveReward(*args, **kwargs)
- __init__(*args, **kwargs)
Wraps an environment to allow a modular transformation of the
step()
andreset()
methods.- Parameters:
env – The environment to wrap
Methods
__init__
(*args, **kwargs)Wraps an environment to allow a modular transformation of the
step()
andreset()
methods.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.
render
()Uses the
render()
of theenv
that can be overwritten to change the returned data.reset
(*[, seed, options])Uses the
reset()
of theenv
that can be overwritten to change the returned data.set_wrapper_attr
(name, value, *[, force])Sets an attribute on this wrapper or lower environment if name is already defined.
step
(action)Perform the action and environment return reward vector.
wrapper_spec
(**kwargs)Generates a WrapperSpec for the wrappers.
Attributes
action_space
Return the
Env
action_space
unless overwritten then the wrapperaction_space
is used.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 wrapperobservation_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 MultiObjectiveReward (INFO)>
- step(action: ndarray) Tuple[ndarray, List[float], bool, bool, Dict[str, Any]]
Perform the action and environment return reward vector. If reward term is not in info reward_terms, it will be ignored.
- Parameters:
action (np.ndarray) – Action to be executed in environment.
- Returns:
observation, vector reward, terminated, truncated and info.
- Return type:
Tuple[ np.ndarray, List[float], bool, bool, Dict[str, Any]]