debug.ipynb

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   "cell_type": "code",
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     "text": [
      " 850"
     ]
    }
   ],
   "source": [
    "# Run this again after editing submodules so Colab uses the updated versions\n",
    "from citylearn import CityLearn\n",
    "from pathlib import Path\n",
    "from agent_final_grad20 import Agent\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "import time\n",
    "\n",
    "# Load environment\n",
    "climate_zone = 5\n",
    "params = {\n",
    "    \"data_path\": Path(\"data/Climate_Zone_\" + str(climate_zone)),\n",
    "    \"building_attributes\": \"building_attributes.json\",\n",
    "    \"weather_file\": \"weather_data.csv\",\n",
    "    \"solar_profile\": \"solar_generation_1kW.csv\",\n",
    "    \"carbon_intensity\": \"carbon_intensity.csv\",\n",
    "    \"building_ids\": [\"Building_\" + str(i) for i in [1, 2, 3, 4, 5, 6, 7, 8, 9]],\n",
    "    \"buildings_states_actions\": \"buildings_state_action_space.json\",\n",
    "    \"simulation_period\": (0, 8760 * 2 - 1),\n",
    "    \"cost_function\": [\n",
    "        \"ramping\",\n",
    "        \"1-load_factor\",\n",
    "        \"average_daily_peak\",\n",
    "        \"peak_demand\",\n",
    "        \"net_electricity_consumption\",\n",
    "        \"carbon_emissions\",\n",
    "    ],\n",
    "    \"central_agent\": False,\n",
    "    \"save_memory\": False,\n",
    "}\n",
    "\n",
    "# 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.\n",
    "env = CityLearn(**params)\n",
    "observations_spaces, actions_spaces = env.get_state_action_spaces()\n",
    "\n",
    "# Provides information on Building type, Climate Zone, Annual DHW demand, Annual Cooling Demand, Annual Electricity Demand, Solar Capacity, and correllations among buildings\n",
    "building_info = env.get_building_information()\n",
    "\n",
    "params_agent = {\n",
    "    \"building_ids\": [\"Building_\" + str(i) for i in [1, 2, 3, 4, 5, 6, 7, 8, 9]],\n",
    "    \"buildings_states_actions\": \"buildings_state_action_space.json\",\n",
    "    \"building_info\": building_info,\n",
    "    \"observation_spaces\": observations_spaces,\n",
    "    \"action_spaces\": actions_spaces,\n",
    "}\n",
    "\n",
    "# Instantiating the control agent(s)\n",
    "agents = Agent(**params_agent)\n",
    "\n",
    "state = env.reset()\n",
    "done = False\n",
    "\n",
    "action, coordination_vars = agents.select_action(state)\n",
    "start = time.time()\n",
    "while not done:\n",
    "    next_state, reward, done, _ = env.step(action)\n",
    "    action_next, coordination_vars_next = agents.select_action(next_state)\n",
    "    agents.add_to_buffer(\n",
    "        state,\n",
    "        action,\n",
    "        reward,\n",
    "        next_state,\n",
    "        done,\n",
    "        coordination_vars,\n",
    "        coordination_vars_next,\n",
    "    )\n",
    "    coordination_vars = coordination_vars_next\n",
    "    state = next_state\n",
    "    action = action_next\n",
    "    print(f\"\\r {env.time_step}\", end=\"\")\n",
    "\n",
    "print(f\"\\ntotal time (min): {np.round((time.time() - start)/60, 3)}\")\n",
    "\n",
    "cost = env.cost()\n",
    "cost_details = env.cost_detailed_view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agents.actor.scs_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agents.actor.fail_cnt"
   ]
  }
 ],
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