main.py

# Run this again after editing submodules so Colab uses the updated versions
from citylearn import CityLearn
from pathlib import Path
from TD3 import Agent
import numpy as np
import torch

# Load environment
climate_zone = 5
params = {
    "data_path": Path("data/Climate_Zone_" + str(climate_zone)),
    "building_attributes": "building_attributes.json",
    "weather_file": "weather_data.csv",
    "solar_profile": "solar_generation_1kW.csv",
    "carbon_intensity": "carbon_intensity.csv",
    "building_ids": ["Building_" + str(i) for i in [1, 2, 3, 4, 5, 6, 7, 8, 9]],
    "buildings_states_actions": "buildings_state_action_space.json",
    "simulation_period": (0, 8760 * 4 - 1),
    "cost_function": [
        "ramping",
        "1-load_factor",
        "average_daily_peak",
        "peak_demand",
        "net_electricity_consumption",
        "carbon_emissions",
    ],
    "central_agent": False,
    "save_memory": False,
}

# 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.
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()

params_agent = {
    "building_ids": ["Building_" + str(i) for i in [1, 2, 3, 4, 5, 6, 7, 8, 9]],
    "buildings_states_actions": "buildings_state_action_space.json",
    "building_info": building_info,
    "observation_spaces": observations_spaces,
    "action_spaces": actions_spaces,
}

# Instantiating the control agent(s)
agents = Agent(**params_agent)

state = env.reset()
done = False


while not done:
    action, _ = agents.select_action(state)
    next_state, reward, done, _ = env.step(action)
    agents.add_to_buffer(state, action, reward, next_state, done)
    state = next_state

    print(f"\r {env.time_step}", end="")

cost = env.cost()
print(cost)