Skip to content

Files

Latest commit

 

History

History

rewards

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 
 
 
 
 

Reward Function

CityLearn allows for custom reward function design. The CityLearn Challenge 2022 provides an interface for custom user reward function.

Participants are to edit the get_reward() function in get_reward.py. Three observations from the environment are provided for the reward calculation and they include:

  1. electricity_consumption: List of each building's/total district electricity consumption in [kWh].
  2. carbon_emission: List of each building's/total district carbon emissions in [kg_co2].
  3. electricity_price: List of each building's/total district electricity price in [$].

By default, the reward function defined in get_reward() is: $$ \textrm{reward}_n = \textrm{min}(-G_n, 0) + \textrm{min}(-C_n, 0) $$

Where G_n and C_n are respectively the carbon_emission and electricity_price of the building(s) controlled by agent n.

Note that get_reward() function must return a list whose length is equal to the number of agents in the environment i.e. I the environment uses a single agent, the length of the list is equal to 1 else the length is equal to the number of buildings in the environment.

Do not edit user_reward.py module!