# citylearn.reward_function module

class citylearn.reward_function.BuildingDynamicsReward(env: citylearn.citylearn.CityLearnEnv)[source]
calculate() List[float][source]

Returned reward assumes that the building-agents act independently of each other, without sharing information through the reward.

Recommended for use with the SAC controllers.

Notes

Reward value is calculated as $$[\textrm{min}(-e_0^3, 0), \dots, \textrm{min}(-e_n^3, 0)]$$ where $$e$$ is electricity_consumption and $$n$$ is the number of agents.

calculate_comfort_reward() List[float][source]
calculate_peak_reward() List[float][source]
calculate_storage_reward() List[float][source]
class citylearn.reward_function.IndependentSACReward(env: citylearn.citylearn.CityLearnEnv)[source]
calculate() List[float][source]

Returned reward assumes that the building-agents act independently of each other, without sharing information through the reward.

Recommended for use with the SAC controllers.

Notes

Reward value is calculated as $$[\textrm{min}(-e_0^3, 0), \dots, \textrm{min}(-e_n^3, 0)]$$ where $$e$$ is electricity_consumption and $$n$$ is the number of agents.

class citylearn.reward_function.MARL(env: citylearn.citylearn.CityLearnEnv)[source]
calculate() List[float][source]

Calculates MARL reward.

Notes

Reward value is calculated as $$\textrm{sign}(-e) \times 0.01(e^2) \times \textrm{max}(0, E)$$ where $$e$$ is the building electricity_consumption and $$E$$ is the district electricity_consumption.

class citylearn.reward_function.RewardFunction(env: citylearn.citylearn.CityLearnEnv, **kwargs)[source]

Bases: object

calculate() List[float][source]

Calculates default reward.

Notes

Reward value is calculated as $$[\textrm{min}(-e_0, 0), \dots, \textrm{min}(-e_n, 0)]$$ where $$e$$ is electricity_consumption and $$n$$ is the number of agents.

property env: citylearn.citylearn.CityLearnEnv

Simulation environment.