Main Configuration File

The main environment for wrapping the environments is sustaindc_env.py, which reads configurations from dc_config.json and manages the external variables using managers for weather, carbon intensity, and workload.

Example Configuration

# Default configuration for this environment. New parameters should be
# added here
DEFAULT_CONFIG = {
     # Agents active
     'agents': ['agent_ls', 'agent_dc', 'agent_bat'],

     # Datafiles
     'location': 'ny',
     'cintensity_file': 'NYIS_NG_&_avgCI.csv',
     'weather_file': 'USA_NY_New.York-Kennedy.epw',
     'workload_file': 'Alibaba_CPU_Data_Hourly_1.csv',

     # Capacity (MW) of the datacenter
     'datacenter_capacity_mw': 1,

     # Maximum battery capacity
     'max_bat_cap_Mw': 2,

     # weight of the individual reward (1=full individual, 0=full collaborative, default=0.8)
     'individual_reward_weight': 0.8,

     # flexible load ratio of the total workload
     'flexible_load': 0.1,

     # Specify reward methods. These are defined in utils/reward_creator.
     'ls_reward': 'default_ls_reward',
     'dc_reward': 'default_dc_reward',
     'bat_reward': 'default_bat_reward',

     # Evaluation flag that is required by the load-shifting environment
     # To be set only during offline evaluation
     'evaluation': False,

     # Set this to True if an agent (like MADDPG) returns continuous actions,
     "actions_are_logits": False
}