citylearn.wrappers module

class citylearn.wrappers.DiscreteActionWrapper(env: citylearn.citylearn.CityLearnEnv, bin_sizes: Optional[List[Mapping[str, int]]] = None, default_bin_size: Optional[int] = None)[source]

Bases: gym.core.ActionWrapper

Wrapper for action space discretization.

Parameters

  • env (CityLearnEnv) – CityLearn environment.

  • bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active action in each building.

  • default_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building action.

action(actions: List[float]) → List[List[float]][source]

Returns discretized actions.

property action_space: List[gym.spaces.multi_discrete.MultiDiscrete]

Returns action space for discretized actions.

class citylearn.wrappers.DiscreteObservationWrapper(env: citylearn.citylearn.CityLearnEnv, bin_sizes: Optional[List[Mapping[str, int]]] = None, default_bin_size: Optional[int] = None)[source]

Bases: gym.core.ObservationWrapper

Wrapper for observation space discretization.

Parameters

  • env (CityLearnEnv) – CityLearn environment.

  • bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active observation in each building.

  • default_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building observation.

observation(observations: List[List[float]]) → numpy.ndarray[source]

Returns discretized observations.

property observation_space: List[gym.spaces.multi_discrete.MultiDiscrete]

Returns observation space for discretized observations.

class citylearn.wrappers.DiscreteSpaceWrapper(env: citylearn.citylearn.CityLearnEnv, observation_bin_sizes: Optional[List[Mapping[str, int]]] = None, action_bin_sizes: Optional[List[Mapping[str, int]]] = None, default_observation_bin_size: Optional[int] = None, default_action_bin_size: Optional[int] = None)[source]

Bases: gym.core.Wrapper

Wrapper for observation and action spaces discretization.

Wraps env in citylearn.wrappers.DiscreteObservationWrapper and citylearn.wrappers.DiscreteActionWrapper.

Parameters

  • env (CityLearnEnv) – CityLearn environment.

  • observation_bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active observation in each building.

  • action_bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active action in each building.

  • default_observation_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building observation.

  • default_action_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building action.

class citylearn.wrappers.NormalizedObservationWrapper(env: citylearn.citylearn.CityLearnEnv)[source]

Bases: gym.core.ObservationWrapper

Wrapper for observations min-max and periodic normalization.

Temporal observations including hour, day_type and month are periodically normalized using sine/cosine transformations and then all observations are min-max normalized between 0 and 1.

Parameters

env (CityLearnEnv) – CityLearn environment.

observation(observations: List[List[float]]) → List[List[float]][source]

Returns normalized observations.

property observation_space: List[gym.spaces.box.Box]

Returns observation space for normalized observations.

class citylearn.wrappers.StableBaselines3ActionWrapper(env: citylearn.citylearn.CityLearnEnv)[source]

Bases: gym.core.ActionWrapper

Action wrapper for stable-baselines3 algorithms.

Wraps actions so that they are returned in a 1-dimensional numpy array. This wrapper is only compatible when the environment is controlled by a central agent i.e., citylearn.citylearn.CityLearnEnv.central_agent = True.

Parameters

env (CityLearnEnv) – CityLearn environment.

action(actions: List[float]) → List[List[float]][source]

Returns actions as 1-dimensional numpy array.

property action_space: gym.spaces.box.Box

Returns single spaces Box object.

class citylearn.wrappers.StableBaselines3ObservationWrapper(env: citylearn.citylearn.CityLearnEnv)[source]

Bases: gym.core.ObservationWrapper

Observation wrapper for stable-baselines3 algorithms.

Wraps observations so that they are returned in a 1-dimensional numpy array. This wrapper is only compatible when the environment is controlled by a central agent i.e., citylearn.citylearn.CityLearnEnv.central_agent = True.

Parameters

env (CityLearnEnv) – CityLearn environment.

observation(observations: List[List[float]]) → numpy.ndarray[source]

Returns observations as 1-dimensional numpy array.

property observation_space: gym.spaces.box.Box

Returns single spaces Box object.

class citylearn.wrappers.StableBaselines3RewardWrapper(env: citylearn.citylearn.CityLearnEnv)[source]

Bases: gym.core.RewardWrapper

Reward wrapper for stable-baselines3 algorithms.

Wraps rewards so that it is returned as float value. This wrapper is only compatible when the environment is controlled by a central agent i.e., citylearn.citylearn.CityLearnEnv.central_agent = True.

Parameters

env (CityLearnEnv) – CityLearn environment.

reward(reward: List[float]) → float[source]

Returns reward as float value.

class citylearn.wrappers.StableBaselines3Wrapper(env: citylearn.citylearn.CityLearnEnv)[source]

Bases: gym.core.Wrapper

Wrapper for stable-baselines3 algorithms.

Wraps env in citylearn.wrappers.StableBaselines3ObservationWrapper, citylearn.wrappers.StableBaselines3ActionWrapper and citylearn.wrappers.StableBaselines3RewardWrapper.

Parameters

env (CityLearnEnv) – CityLearn environment.

class citylearn.wrappers.TabularQLearningActionWrapper(env: citylearn.citylearn.CityLearnEnv, bin_sizes: Optional[List[Mapping[str, int]]] = None, default_bin_size: Optional[int] = None)[source]

Bases: gym.core.ActionWrapper

Action wrapper for citylearn.agents.q_learning.TabularQLearning agent.

Wraps env in citylearn.wrappers.DiscreteActionWrapper.

Parameters

  • env (CityLearnEnv) – CityLearn environment.

  • bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active action in each building.

  • default_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building action.

action(actions: List[float]) → List[List[int]][source]

Returns discretized actions.

property action_space: List[gym.spaces.discrete.Discrete]

Returns action space for discretized actions.

set_combinations() → List[List[int]][source]

Returns all combinations of discrete actions.

class citylearn.wrappers.TabularQLearningObservationWrapper(env: citylearn.citylearn.CityLearnEnv, bin_sizes: Optional[List[Mapping[str, int]]] = None, default_bin_size: Optional[int] = None)[source]

Bases: gym.core.ObservationWrapper

Observation wrapper for citylearn.agents.q_learning.TabularQLearning agent.

Wraps env in citylearn.wrappers.DiscreteObservationWrapper.

Parameters

  • env (CityLearnEnv) – CityLearn environment.

  • bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active observation in each building.

  • default_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building observation.

observation(observations: List[List[int]]) → List[List[int]][source]

Returns discretized observations.

property observation_space: List[gym.spaces.discrete.Discrete]

Returns observation space for discretized observations.

set_combinations() → List[List[int]][source]

Returns all combinations of discrete observations.

class citylearn.wrappers.TabularQLearningWrapper(env: citylearn.citylearn.CityLearnEnv, observation_bin_sizes: Optional[List[Mapping[str, int]]] = None, action_bin_sizes: Optional[List[Mapping[str, int]]] = None, default_observation_bin_size: Optional[int] = None, default_action_bin_size: Optional[int] = None)[source]

Bases: gym.core.Wrapper

Wrapper for citylearn.agents.q_learning.TabularQLearning agent.

Wraps env in citylearn.wrappers.DiscreteObservationWrapper and citylearn.wrappers.DiscreteActionWrapper.

Parameters

  • env (CityLearnEnv) – CityLearn environment.

  • observation_bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active observation in each building.

  • action_bin_sizes (List[Mapping[str, int]], optional) – Then number of bins for each active action in each building.

  • default_observation_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building observation.

  • default_action_bin_size (int, default = 10) – The default number of bins if bin_sizes is unspecified for any active building action.