sinergym.envs.eplus_env.EplusEnv

class sinergym.envs.eplus_env.EplusEnv(building_file: str, weather_file: str | ~typing.List[str], observation_space: ~gymnasium.spaces.box.Box = Box(-5000000.0, 5000000.0, (4,), float32), observation_variables: ~typing.List[str] = [], action_space: ~gymnasium.spaces.box.Box | ~gymnasium.spaces.discrete.Discrete = Box([], [], (0,), float32), action_variables: ~typing.List[str] = [], action_mapping: ~typing.Dict[int, ~typing.Tuple[float, ...]] = {}, weather_variability: ~typing.Tuple[float] | None = None, reward: ~typing.Any = <class 'sinergym.utils.rewards.LinearReward'>, reward_kwargs: ~typing.Dict[str, ~typing.Any] | None = {}, act_repeat: int = 1, max_ep_data_store_num: int = 10, action_definition: ~typing.Dict[str, ~typing.Any] | None = None, env_name: str = 'eplus-env-v1', config_params: ~typing.Dict[str, ~typing.Any] | None = None)

__init__(building_file: str, weather_file: str | ~typing.List[str], observation_space: ~gymnasium.spaces.box.Box = Box(-5000000.0, 5000000.0, (4,), float32), observation_variables: ~typing.List[str] = [], action_space: ~gymnasium.spaces.box.Box | ~gymnasium.spaces.discrete.Discrete = Box([], [], (0,), float32), action_variables: ~typing.List[str] = [], action_mapping: ~typing.Dict[int, ~typing.Tuple[float, ...]] = {}, weather_variability: ~typing.Tuple[float] | None = None, reward: ~typing.Any = <class 'sinergym.utils.rewards.LinearReward'>, reward_kwargs: ~typing.Dict[str, ~typing.Any] | None = {}, act_repeat: int = 1, max_ep_data_store_num: int = 10, action_definition: ~typing.Dict[str, ~typing.Any] | None = None, env_name: str = 'eplus-env-v1', config_params: ~typing.Dict[str, ~typing.Any] | None = None)

Environment with EnergyPlus simulator.

Parameters:

building_file (str) – Name of the JSON file with the building definition.
weather_file (Union[str,List[str]]) – Name of the EPW file for weather conditions. It can be specified a list of weathers files in order to sample a weather in each episode randomly.
observation_space (gym.spaces.Box, optional) – Gym Observation Space definition. Defaults to an empty observation_space (no control).
observation_variables (List[str], optional) – List with variables names in building. Defaults to an empty observation variables (no control).
action_space (Union[gym.spaces.Box, gym.spaces.Discrete], optional) – Gym Action Space definition. Defaults to an empty action_space (no control).
action_variables (List[str],optional) – Action variables to be controlled in building, if that actions names have not been configured manually in building, you should configure or use action_definition. Default to empty List.
action_mapping (Dict[int, Tuple[float, ...]], optional) – Action mapping list for discrete actions spaces only. Defaults to empty list.
weather_variability (Optional[Tuple[float]], optional) – Tuple with sigma, mu and tao of the Ornstein-Uhlenbeck process to be applied to weather data. Defaults to None.
reward (Any, optional) – Reward function instance used for agent feedback. Defaults to LinearReward.
reward_kwargs (Optional[Dict[str, Any]], optional) – Parameters to be passed to the reward function. Defaults to empty dict.
act_repeat (int, optional) – Number of timesteps that an action is repeated in the simulator, regardless of the actions it receives during that repetition interval.
max_ep_data_store_num (int, optional) – Number of last sub-folders (one for each episode) generated during execution on the simulation.
action_definition (Optional[Dict[str, Any]) – Dict with building components to being controlled by Sinergym automatically if it is supported. Default value to None.
env_name (str, optional) – Env name used for working directory generation. Defaults to eplus-env-v1.
config_params (Optional[Dict[str, Any]], optional) – Dictionary with all extra configuration for simulator. Defaults to None.

Methods

`__init__`(building_file, weather_file[, ...])	Environment with EnergyPlus simulator.
`close`()	End simulation.
`get_schedulers`([path])	Extract all schedulers available in the building model to be controlled.
`get_wrapper_attr`(name)	Gets the attribute name from the environment.
`get_zones`()	Get the zone names available in the building model of that environment.
`render`([mode])	Environment rendering.
`reset`([seed, options])	Reset the environment.
`step`(action)	Sends action to the environment

Attributes

`action_space`
`metadata`
`np_random`	Returns the environment's internal `_np_random` that if not set will initialise with a random seed.
`observation_space`
`render_mode`
`reward_range`
`spec`
`unwrapped`	Returns the base non-wrapped environment.

property action_space: Space[Any]

close() → None: End simulation.

get_schedulers(path: str | None = None) → Dict[str, Dict[str, str | Dict[str, str]]]

Extract all schedulers available in the building model to be controlled.

Parameters:: path (str, optional) – If path is specified, then this method export a xlsx file version in addition to return the dictionary.
Returns:: Python Dictionary: For each scheduler found, it shows type value and where this scheduler is present (Object name, Object field and Object type).
Return type:: Dict[str, Any]

get_zones() → List[str]

Get the zone names available in the building model of that environment.

Returns:: List of the zone names.
Return type:: List[str]

metadata: dict[str, Any] = {'render_modes': ['human']}

property observation_space: Space[Any]

render(mode: str = 'human') → None

Environment rendering.

Parameters:: mode (str, optional) – Mode for rendering. Defaults to ‘human’.

reset(seed: int | None = None, options: Dict[str, Any] | None = None) → Tuple[ndarray, Dict[str, Any]]

Reset the environment.

Parameters:

seed (Optional[int]) – The seed that is used to initialize the environment’s episode (np_random). if value is None, a seed will be chosen from some source of entropy. Defaults to None.
options (Optional[Dict[str, Any]]) – Additional information to specify how the environment is reset. Defaults to None.

Returns:

Current observation and info context with additional information.

Return type:

Tuple[np.ndarray,Dict[str,Any]]

Sends action to the environment

Parameters:: action (Union[int, float, np.integer, np.ndarray, List[Any], Tuple[Any]]) – Action selected by the agent.
Returns:: Observation for next timestep, reward obtained, Whether the episode has ended or not, Whether episode has been truncated or not, and a dictionary with extra information
Return type:: Tuple[np.ndarray, float, bool, Dict[str, Any]]