Thank you very much @dipam . And I have another question are we guaranteed that the observation spaces are the same for all buildings?
Because if not I think , in the evaluation script, we should receive the observation_spaces (as well as the action spaces) and building information as in citylearn repo main examples
See
# Contain the lower and upper bounds of the states and actions, to be provided to the agent to normalize the variables between 0 and 1.
# Can be obtained using observations_spaces[i].low or .high
env = CityLearn(**params)
observations_spaces, actions_spaces = env.get_state_action_spaces()
# Provides information on Building type, Climate Zone, Annual DHW demand, Annual Cooling Demand, Annual Electricity Demand, Solar Capacity, and correllations among buildings
building_info = env.get_building_information()