citylearn.agents.rbc module

class citylearn.agents.rbc.BasicBatteryRBC(*args, **kwargs)[source]

Bases: citylearn.agents.rbc.BasicRBC

select_actions(observations: List[List[float]], deterministic: Optional[bool] = None) → List[List[float]][source]

Provide actions for current time step.

Parameters

  • observations (List[List[float]]) – Environment observations

  • deterministic (bool, default: False) – Wether to return purely exploitatative deterministic actions.

Returns

actions – Action values

Return type

List[float]

Notes

The actions are optimized for electrical storage (battery) such that the agent charges the controlled storage system(s) by 11.0% of its maximum capacity every hour between 06:00 AM and 02:00 PM, and discharges 6.7% of its maximum capacity at every other hour.

class citylearn.agents.rbc.BasicRBC(*args, **kwargs)[source]

Bases: citylearn.agents.rbc.RBC

select_actions(observations: List[List[float]], deterministic: Optional[bool] = None) → List[List[float]][source]

Provide actions for current time step.

Parameters

  • observations (List[List[float]]) – Environment observations

  • deterministic (bool, default: False) – Wether to return purely exploitatative deterministic actions.

Returns

actions – Action values

Return type

List[float]

Notes

The actions are designed such that the agent charges the controlled storage system(s) by 9.1% of its maximum capacity every hour between 10:00 PM and 08:00 AM, and discharges 8.0% of its maximum capacity at every other hour.

class citylearn.agents.rbc.OptimizedRBC(*args, **kwargs)[source]

Bases: citylearn.agents.rbc.BasicRBC

select_actions(observations: List[List[float]], deterministic: Optional[bool] = None) → List[List[float]][source]

Provide actions for current time step.

Parameters

  • observations (List[List[float]]) – Environment observations

  • deterministic (bool, default: False) – Wether to return purely exploitatative deterministic actions.

Returns

actions – Action values

Return type

List[float]

Notes

The actions are designed such that the agent discharges the controlled storage system(s) by 2.0% of its maximum capacity every hour between 07:00 AM and 03:00 PM, discharges by 4.4% of its maximum capacity between 04:00 PM and 06:00 PM, discharges by 2.4% of its maximum capacity between 07:00 PM and 10:00 PM, charges by 3.4% of its maximum capacity between 11:00 PM to midnight and charges by 5.532% of its maximum capacity at every other hour.

class citylearn.agents.rbc.RBC(*args, **kwargs)[source]

Bases: citylearn.agents.base.Agent