sumo_A2C.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyvirtualdisplay in c:\\users\\naren\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.0)\n",
      "Requirement already satisfied: Pillow in c:\\users\\naren\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (11.0.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install pyvirtualdisplay Pillow\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUMO Setups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUMO_HOME is set to: C:\\Program Files (x86)\\Eclipse\\Sumo\n",
      "Does SUMO_HOME exist? True\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import gymnasium as gym\n",
    "from tqdm import tqdm\n",
    "from dotenv import load_dotenv\n",
    "from gymnasium import spaces\n",
    "import matplotlib.patches as patches\n",
    "import shutil\n",
    "\n",
    "SUMO_HOME = 'C:\\\\Program Files (x86)\\\\Eclipse\\\\Sumo'\n",
    "\n",
    "os.environ['SUMO_HOME'] = SUMO_HOME\n",
    "\n",
    "# Print the SUMO_HOME environment variable to verify\n",
    "print(\"SUMO_HOME is set to:\", os.environ.get('SUMO_HOME'))\n",
    "\n",
    "# Check if the SUMO_HOME path exists\n",
    "print(\"Does SUMO_HOME exist?\", os.path.exists(os.environ.get('SUMO_HOME')))\n",
    "\n",
    "\n",
    "# # Define file paths \n",
    "# net_file = 'C:/Users/ASUS/Sumo/2024-11-03-18-23-47/osm.net.xml/osm.net.xml'\n",
    "# route_file = 'C:/Users/ASUS/Sumo/2024-11-03-18-23-47/osm.passenger.rou.xml'\n",
    "# out_csv_name = 'C:/Users/ASUS/Sumo/2024-11-03-18-23-47/osm.passenger.csv'\n",
    "# config_file = 'C:/Users/ASUS/Sumo/2024-11-03-18-23-47/osm.sumocfg'\n",
    "\n",
    "# Define file paths Nishanth (temp)\n",
    "net_file = 'C:/Users/naren/ReinforcementLearning_FinalCourseProject/nets/single_intersection_simple.net.xml'\n",
    "route_file = 'C:/Users/naren/ReinforcementLearning_FinalCourseProject/nets/single_intersection_simple.rou.xml'\n",
    "out_csv_name = 'C:/Users/naren/ReinforcementLearning_FinalCourseProject/nets/A3C_trial.csv'\n",
    "config_file = 'C:/Users/naren/ReinforcementLearning_FinalCourseProject/nets/single_intersection_simple.sumocfg'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import gymnasium as gym\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "import sumo_rl\n",
    "import traci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Environment:\n",
    "    def __init__(self, env_name, route_file, net_file, out_csv_name, render_mode='human', num_seconds=100000):\n",
    "        self.env_name = env_name\n",
    "        self.use_gui = True if render_mode == 'human' else False\n",
    "        self.route_file = route_file\n",
    "        self.net_file = net_file\n",
    "        self.out_csv_name = out_csv_name\n",
    "        self.env = gym.make(\n",
    "                        env_name,\n",
    "                        net_file=net_file,\n",
    "                        route_file=route_file,\n",
    "                        out_csv_name=out_csv_name,\n",
    "                        use_gui=self.use_gui,\n",
    "                        num_seconds=num_seconds\n",
    "                    )\n",
    "     \n",
    "        self.state, _ = self.env.reset()\n",
    "        self.done = False\n",
    "        self.observation_space = self.env.observation_space.shape[0]\n",
    "        self.action_space = self.env.action_space\n",
    "        # print(self.action_space)\n",
    "        self.traffic_signals = self.env.unwrapped.traffic_signals\n",
    "    \n",
    "    def reset(self):\n",
    "        self.state, _ = self.env.reset()\n",
    "        self.done = False\n",
    "        return self.state\n",
    "    \n",
    "    def custom_reward(self, traffic_signal, reward_type='average_speed', reward_method='simple'):\n",
    "        # print(\"Inside custom reward method\")\n",
    "        if reward_method == 'simple':\n",
    "            match reward_type:\n",
    "                case 'average_speed':\n",
    "                    return traffic_signal.get_avgerage_speed()\n",
    "                case 'congesion':\n",
    "                    return -1 * traffic_signal.get_pressure()\n",
    "                case 'emissions':\n",
    "                    return -1* traffic_signal.get_emission_co2()\n",
    "                case 'throughput':\n",
    "                    return traffic_signal.get_throughput()\n",
    "\n",
    "        else:\n",
    "            # Weighted sum of the metrics\n",
    "            reward = 0\n",
    "            if weights is None:\n",
    "                weights = {\n",
    "                    'average_speed': 0.4,\n",
    "                    'waiting_time': 0.3,\n",
    "                    'emissions': 0.2,\n",
    "                    'throughput': 0.1\n",
    "                }\n",
    "\n",
    "            # Calculate individual rewards\n",
    "            average_speed = traffic_signal.get_average_speed()\n",
    "            waiting_time = -1* traffic_signal._diff_waiting_time_reward()\n",
    "            total_queue = -1 * traffic_signal.get_total_queued()\n",
    "            congesion = traffic_signal.get_pressure()\n",
    "\n",
    "            print(average_speed, waiting_time, total_queue, congesion)\n",
    "            weighted_reward = (\n",
    "                weights['average_speed'] * average_speed +\n",
    "                weights['waiting_time'] * waiting_time +\n",
    "                weights['emissions'] * total_queue +\n",
    "                weights['throughput'] * congesion\n",
    "            )\n",
    "\n",
    "            return weighted_reward\n",
    "            \n",
    "            \n",
    "    def step(self, action):\n",
    "        # print(\"Inside step method\")\n",
    "        next_state, reward, terminated, truncated, info = self.env.step(action)\n",
    "        # print(\"Step taken\", next_state, terminated, truncated, info)\n",
    "\n",
    "        # print(\"Traffic signals:\", list(self.traffic_signals.values())[0])\n",
    "        # traffic_signal = list(self.traffic_signals.values())[0]\n",
    "        # print(\"Pressure:\", traffic_signal.get_pressure())\n",
    "        # # print(traffic_signal.get_average_speed(), traffic_signal.get_total_queued(), traffic_signal._diff_waiting_time_reward(), traffic_signal.get_pressure())\n",
    "        # reward = self.custom_reward(traffic_signal, reward_type='congesion', reward_method='simple')\n",
    "        # print(\"Reward:\", reward)\n",
    "\n",
    "        self.state = next_state\n",
    "        self.done = terminated\n",
    "        return next_state, reward, self.done or truncated\n",
    "    \n",
    "    def render(self):\n",
    "        self.env.render()\n",
    "    \n",
    "    def close(self):\n",
    "        try:\n",
    "            self.env.close()\n",
    "            if traci.isLoaded():\n",
    "                traci.close()\n",
    "            print(\"Env and Traci closed successfully.\")\n",
    "        except Exception as e:\n",
    "            print(\"Error while closing the environment:\", e)\n",
    "    \n",
    "    def get_state(self):\n",
    "        return self.state\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Agent Run Basic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Environment (Simple Intersection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation Space: 11\n",
      "Action Space: 2\n",
      "Initial State: [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "action space Discrete(2)\n",
      "Action  1  Reward  0.0  Done  False observation [1.         0.         0.         0.         0.         0.10567101\n",
      " 0.05283551 0.         0.         0.         0.        ]\n",
      "Action  1  Reward  0.0  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.15850651\n",
      " 0.15850651 0.         0.         0.         0.        ]\n",
      "Action  0  Reward  -0.01  Done  False observation [1.         0.         1.         0.15146415 0.1009761  0.26417753\n",
      " 0.21134202 0.         0.         0.05283551 0.        ]\n",
      "Action  1  Reward  0.01  Done  False observation [0.         1.         0.         0.2019522  0.2019522  0.26417753\n",
      " 0.15850651 0.         0.         0.         0.        ]\n",
      "Action  1  Reward  -0.07  Done  False observation [0.         1.         1.         0.25244024 0.2019522  0.15850651\n",
      " 0.21134202 0.05048805 0.05048805 0.         0.        ]\n",
      "Action  0  Reward  -0.03  Done  False observation [1.         0.         0.         0.2019522  0.15146415 0.21134202\n",
      " 0.15850651 0.         0.         0.05283551 0.        ]\n",
      "Action  1  Reward  -0.1  Done  False observation [1.         0.         1.         0.1009761  0.1009761  0.26417753\n",
      " 0.26417753 0.         0.         0.15850651 0.10567101]\n",
      "Action  0  Reward  -0.27999999999999997  Done  False observation [1.         0.         1.         0.         0.05048805 0.31701303\n",
      " 0.36984855 0.         0.         0.15850651 0.15850651]\n",
      "Action  1  Reward  0.04999999999999999  Done  False observation [0.         1.         0.         0.         0.05048805 0.36984855\n",
      " 0.36984855 0.         0.         0.05283551 0.05283551]\n",
      "Action  0  Reward  0.33999999999999997  Done  False observation [0.         1.         1.         0.05048805 0.         0.31701303\n",
      " 0.36984855 0.05048805 0.         0.         0.        ]\n",
      "Action  0  Reward  -0.020000000000000004  Done  False observation [1.         0.         0.         0.         0.         0.36984855\n",
      " 0.47551954 0.         0.         0.05283551 0.10567101]\n",
      "Action  0  Reward  -0.28  Done  False observation [1.         0.         1.         0.05048805 0.         0.47551954\n",
      " 0.52835506 0.         0.         0.21134202 0.21134202]\n",
      "Action  0  Reward  -0.5499999999999999  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.5811906\n",
      " 0.63402605 0.         0.         0.31701303 0.36984855]\n",
      "Action  1  Reward  -0.48  Done  False observation [0.         1.         0.         0.05048805 0.05048805 0.5811906\n",
      " 0.63402605 0.05048805 0.         0.31701303 0.42268404]\n",
      "Action  1  Reward  0.11999999999999988  Done  False observation [0.         1.         1.         0.05048805 0.05048805 0.52835506\n",
      " 0.52835506 0.05048805 0.         0.15850651 0.21134202]\n",
      "Action  1  Reward  0.22999999999999998  Done  False observation [0.         1.         1.         0.1009761  0.1009761  0.42268404\n",
      " 0.47551954 0.05048805 0.         0.         0.05283551]\n",
      "Action  0  Reward  0.07000000000000006  Done  False observation [1.         0.         0.         0.1009761  0.05048805 0.47551954\n",
      " 0.47551954 0.         0.         0.05283551 0.10567101]\n",
      "Action  1  Reward  -0.30000000000000004  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.47551954\n",
      " 0.47551954 0.         0.         0.21134202 0.21134202]\n",
      "Action  1  Reward  -0.18999999999999995  Done  False observation [0.         1.         0.         0.         0.05048805 0.47551954\n",
      " 0.47551954 0.         0.         0.21134202 0.26417753]\n",
      "Action  0  Reward  0.3699999999999999  Done  False observation [0.         1.         1.         0.         0.05048805 0.47551954\n",
      " 0.47551954 0.         0.         0.10567101 0.10567101]\n",
      "Action  0  Reward  -0.11999999999999988  Done  False observation [1.         0.         0.         0.1009761  0.05048805 0.52835506\n",
      " 0.5811906  0.         0.         0.         0.15850651]\n",
      "Action  1  Reward  -0.31000000000000005  Done  False observation [1.         0.         1.         0.1009761  0.1009761  0.63402605\n",
      " 0.6868616  0.         0.         0.15850651 0.31701303]\n",
      "Action  0  Reward  -0.6399999999999999  Done  False observation [1.         0.         1.         0.         0.05048805 0.7396971\n",
      " 0.7396971  0.         0.         0.36984855 0.47551954]\n",
      "Action  1  Reward  -0.31999999999999984  Done  False observation [0.         1.         0.         0.05048805 0.1009761  0.79253256\n",
      " 0.79253256 0.         0.         0.42268404 0.47551954]\n",
      "Action  1  Reward  0.18999999999999995  Done  False observation [0.         1.         1.         0.1009761  0.05048805 0.6868616\n",
      " 0.6868616  0.05048805 0.         0.31701303 0.42268404]\n",
      "Action  0  Reward  -0.30000000000000027  Done  False observation [1.         0.         0.         0.05048805 0.05048805 0.7396971\n",
      " 0.7396971  0.         0.         0.26417753 0.26417753]\n",
      "Action  1  Reward  -0.5099999999999998  Done  False observation [1.         0.         1.         0.05048805 0.         0.8453681\n",
      " 0.79253256 0.         0.         0.21134202 0.36984855]\n",
      "Action  1  Reward  0.10000000000000009  Done  False observation [0.         1.         0.         0.05048805 0.05048805 0.79253256\n",
      " 0.7396971  0.         0.         0.26417753 0.21134202]\n",
      "Action  0  Reward  0.6099999999999999  Done  False observation [0.         1.         1.         0.1009761  0.05048805 0.79253256\n",
      " 0.7396971  0.         0.         0.15850651 0.10567101]\n",
      "Action  1  Reward  0.76  Done  False observation [0.         1.         1.         0.1009761  0.05048805 0.63402605\n",
      " 0.63402605 0.05048805 0.         0.10567101 0.        ]\n",
      "Action  1  Reward  0.6299999999999999  Done  False observation [0.         1.         1.         0.2019522  0.1009761  0.52835506\n",
      " 0.52835506 0.05048805 0.05048805 0.         0.        ]\n",
      "Action  1  Reward  0.26  Done  False observation [0.         1.         1.         0.2019522  0.1009761  0.42268404\n",
      " 0.42268404 0.1009761  0.05048805 0.         0.        ]\n",
      "Action  0  Reward  0.38  Done  False observation [1.         0.         0.         0.15146415 0.15146415 0.47551954\n",
      " 0.47551954 0.         0.         0.05283551 0.        ]\n",
      "Action  1  Reward  -0.15000000000000002  Done  False observation [1.         0.         1.         0.1009761  0.1009761  0.52835506\n",
      " 0.5811906  0.         0.         0.15850651 0.21134202]\n",
      "Action  0  Reward  -0.52  Done  False observation [1.         0.         1.         0.1009761  0.1009761  0.63402605\n",
      " 0.6868616  0.         0.         0.31701303 0.36984855]\n",
      "Action  1  Reward  -0.31000000000000005  Done  False observation [0.         1.         0.         0.15146415 0.15146415 0.6868616\n",
      " 0.6868616  0.         0.         0.31701303 0.36984855]\n",
      "Action  0  Reward  0.07000000000000006  Done  False observation [0.         1.         1.         0.15146415 0.15146415 0.7396971\n",
      " 0.6868616  0.05048805 0.05048805 0.15850651 0.21134202]\n",
      "Action  1  Reward  0.15000000000000013  Done  False observation [0.         1.         1.         0.15146415 0.15146415 0.63402605\n",
      " 0.63402605 0.1009761  0.1009761  0.10567101 0.10567101]\n",
      "Action  0  Reward  -0.15000000000000013  Done  False observation [1.         0.         0.         0.1009761  0.15146415 0.6868616\n",
      " 0.6868616  0.         0.         0.15850651 0.05283551]\n",
      "Action  0  Reward  -0.07999999999999985  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.6868616\n",
      " 0.6868616  0.         0.         0.15850651 0.21134202]\n",
      "Action  0  Reward  -0.51  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.79253256\n",
      " 0.79253256 0.         0.         0.31701303 0.31701303]\n",
      "Action  0  Reward  -0.8600000000000001  Done  False observation [1.         0.         1.         0.         0.05048805 0.8453681\n",
      " 0.8982036  0.         0.         0.47551954 0.52835506]\n",
      "Action  0  Reward  -1.2000000000000002  Done  False observation [1.         0.         1.         0.1009761  0.05048805 0.8982036\n",
      " 0.9510391  0.         0.         0.6868616  0.6868616 ]\n",
      "Action  0  Reward  -1.5599999999999996  Done  False observation [1.         0.         1.         0.1009761  0.05048805 0.8982036\n",
      " 0.9510391  0.         0.         0.8982036  0.8982036 ]\n",
      "Action  1  Reward  -0.9000000000000004  Done  False observation [0.         1.         0.         0.1009761  0.05048805 0.9510391\n",
      " 0.9510391  0.         0.05048805 0.7396971  0.79253256]\n",
      "Action  0  Reward  -0.019999999999999574  Done  False observation [0.         1.         1.         0.15146415 0.1009761  0.8453681\n",
      " 0.8453681  0.05048805 0.05048805 0.63402605 0.5811906 ]\n",
      "Action  1  Reward  0.8099999999999996  Done  False observation [0.         1.         1.         0.2019522  0.15146415 0.6868616\n",
      " 0.6868616  0.1009761  0.05048805 0.36984855 0.36984855]\n",
      "Action  0  Reward  -0.3700000000000001  Done  False observation [1.         0.         0.         0.15146415 0.15146415 0.6868616\n",
      " 0.6868616  0.         0.         0.26417753 0.21134202]\n",
      "Action  1  Reward  -0.22999999999999954  Done  False observation [1.         0.         1.         0.1009761  0.05048805 0.6868616\n",
      " 0.6868616  0.         0.         0.15850651 0.15850651]\n",
      "Action  1  Reward  0.3199999999999994  Done  False observation [0.         1.         0.         0.15146415 0.1009761  0.7396971\n",
      " 0.7396971  0.         0.         0.15850651 0.21134202]\n",
      "Action  0  Reward  0.9700000000000006  Done  False observation [0.         1.         1.         0.15146415 0.15146415 0.7396971\n",
      " 0.7396971  0.05048805 0.05048805 0.05283551 0.10567101]\n",
      "Action  1  Reward  1.4699999999999998  Done  False observation [0.         1.         1.         0.2019522  0.15146415 0.63402605\n",
      " 0.63402605 0.1009761  0.05048805 0.         0.        ]\n",
      "Action  1  Reward  0.98  Done  False observation [0.         1.         1.         0.2019522  0.2019522  0.5811906\n",
      " 0.52835506 0.15146415 0.15146415 0.         0.05283551]\n",
      "Action  1  Reward  0.8899999999999999  Done  False observation [0.         1.         1.         0.25244024 0.25244024 0.42268404\n",
      " 0.42268404 0.15146415 0.15146415 0.         0.        ]\n",
      "Action  0  Reward  0.5800000000000001  Done  False observation [1.         0.         0.         0.25244024 0.2019522  0.47551954\n",
      " 0.47551954 0.05048805 0.         0.         0.05283551]\n",
      "Action  1  Reward  0.41000000000000003  Done  False observation [1.         0.         1.         0.15146415 0.2019522  0.52835506\n",
      " 0.5811906  0.         0.         0.15850651 0.15850651]\n",
      "Action  0  Reward  -0.44000000000000006  Done  False observation [1.         0.         1.         0.15146415 0.15146415 0.63402605\n",
      " 0.7396971  0.         0.         0.31701303 0.31701303]\n",
      "Action  1  Reward  -0.09999999999999987  Done  False observation [0.         1.         0.         0.15146415 0.15146415 0.6868616\n",
      " 0.79253256 0.05048805 0.05048805 0.26417753 0.36984855]\n",
      "Action  0  Reward  -0.17000000000000015  Done  False observation [0.         1.         1.         0.15146415 0.15146415 0.6868616\n",
      " 0.79253256 0.1009761  0.1009761  0.21134202 0.21134202]\n",
      "Action  1  Reward  -0.06000000000000005  Done  False observation [0.         1.         1.         0.15146415 0.15146415 0.63402605\n",
      " 0.6868616  0.15146415 0.15146415 0.10567101 0.15850651]\n",
      "Action  0  Reward  -0.11999999999999988  Done  False observation [1.         0.         0.         0.15146415 0.15146415 0.6868616\n",
      " 0.6868616  0.         0.         0.10567101 0.10567101]\n",
      "Action  1  Reward  0.11999999999999988  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.6868616\n",
      " 0.6868616  0.         0.         0.21134202 0.15850651]\n",
      "Action  0  Reward  -0.5299999999999998  Done  False observation [1.         0.         1.         0.1009761  0.1009761  0.79253256\n",
      " 0.79253256 0.         0.         0.36984855 0.31701303]\n",
      "Action  0  Reward  -0.8599999999999999  Done  False observation [1.         0.         1.         0.1009761  0.1009761  0.8453681\n",
      " 0.8982036  0.         0.         0.52835506 0.52835506]\n",
      "Action  0  Reward  -1.1500000000000004  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.9510391\n",
      " 0.9510391  0.         0.         0.7396971  0.63402605]\n",
      "Action  0  Reward  -1.5  Done  False observation [1.         0.         1.         0.05048805 0.         1.\n",
      " 1.         0.         0.         0.8982036  0.8453681 ]\n",
      "Action  1  Reward  -0.9500000000000002  Done  False observation [0.         1.         0.         0.05048805 0.05048805 0.9510391\n",
      " 0.9510391  0.         0.         0.8453681  0.8453681 ]\n",
      "Action  1  Reward  -0.019999999999999574  Done  False observation [0.         1.         1.         0.05048805 0.05048805 0.8453681\n",
      " 0.8453681  0.05048805 0.         0.63402605 0.5811906 ]\n",
      "Action  1  Reward  0.8999999999999995  Done  False observation [0.         1.         1.         0.1009761  0.05048805 0.6868616\n",
      " 0.6868616  0.05048805 0.05048805 0.36984855 0.31701303]\n",
      "Action  1  Reward  0.8000000000000007  Done  False observation [0.         1.         1.         0.1009761  0.1009761  0.5811906\n",
      " 0.52835506 0.05048805 0.05048805 0.15850651 0.10567101]\n",
      "Action  1  Reward  1.0899999999999999  Done  False observation [0.         1.         1.         0.15146415 0.1009761  0.42268404\n",
      " 0.42268404 0.05048805 0.05048805 0.         0.        ]\n",
      "Action  0  Reward  0.43999999999999995  Done  False observation [1.         0.         0.         0.15146415 0.1009761  0.52835506\n",
      " 0.47551954 0.         0.         0.05283551 0.        ]\n",
      "Action  0  Reward  -0.18000000000000016  Done  False observation [1.         0.         1.         0.1009761  0.1009761  0.63402605\n",
      " 0.52835506 0.         0.         0.15850651 0.15850651]\n",
      "Action  1  Reward  0.2200000000000002  Done  False observation [0.         1.         0.         0.1009761  0.1009761  0.6868616\n",
      " 0.6868616  0.         0.         0.15850651 0.15850651]\n",
      "Action  1  Reward  0.8199999999999998  Done  False observation [0.         1.         1.         0.15146415 0.15146415 0.7396971\n",
      " 0.6868616  0.05048805 0.1009761  0.10567101 0.05283551]\n",
      "Action  0  Reward  0.16999999999999993  Done  False observation [1.         0.         0.         0.15146415 0.15146415 0.7396971\n",
      " 0.7396971  0.         0.         0.05283551 0.10567101]\n",
      "Action  0  Reward  -0.22999999999999998  Done  False observation [1.         0.         1.         0.1009761  0.15146415 0.79253256\n",
      " 0.79253256 0.         0.         0.21134202 0.31701303]\n",
      "Action  0  Reward  -0.5899999999999999  Done  False observation [1.         0.         1.         0.1009761  0.05048805 0.79253256\n",
      " 0.8453681  0.         0.         0.31701303 0.47551954]\n",
      "Action  0  Reward  -0.9100000000000001  Done  False observation [1.         0.         1.         0.1009761  0.15146415 0.8982036\n",
      " 0.9510391  0.         0.         0.47551954 0.63402605]\n",
      "Action  1  Reward  -0.2899999999999996  Done  False observation [0.         1.         0.         0.15146415 0.15146415 0.8982036\n",
      " 0.9510391  0.05048805 0.         0.47551954 0.6868616 ]\n",
      "Action  0  Reward  0.5599999999999996  Done  False observation [0.         1.         1.         0.2019522  0.15146415 0.8453681\n",
      " 0.8453681  0.05048805 0.         0.42268404 0.5811906 ]\n",
      "Action  1  Reward  0.52  Done  False observation [0.         1.         1.         0.25244024 0.2019522  0.6868616\n",
      " 0.6868616  0.1009761  0.1009761  0.36984855 0.36984855]\n",
      "Action  1  Reward  0.06000000000000005  Done  False observation [0.         1.         1.         0.3029283  0.25244024 0.5811906\n",
      " 0.52835506 0.15146415 0.15146415 0.15850651 0.15850651]\n",
      "Action  1  Reward  0.22999999999999998  Done  False observation [0.         1.         1.         0.3029283  0.25244024 0.42268404\n",
      " 0.42268404 0.25244024 0.2019522  0.         0.        ]\n",
      "Action  0  Reward  0.31000000000000005  Done  False observation [1.         0.         0.         0.25244024 0.2019522  0.47551954\n",
      " 0.47551954 0.15146415 0.05048805 0.         0.        ]\n",
      "Action  0  Reward  0.41999999999999993  Done  False observation [1.         0.         1.         0.15146415 0.1009761  0.5811906\n",
      " 0.5811906  0.         0.         0.15850651 0.15850651]\n",
      "Action  1  Reward  0.09000000000000008  Done  False observation [0.         1.         0.         0.1009761  0.1009761  0.6868616\n",
      " 0.6868616  0.05048805 0.05048805 0.15850651 0.15850651]\n",
      "Action  1  Reward  0.3699999999999999  Done  False observation [0.         1.         1.         0.15146415 0.15146415 0.6868616\n",
      " 0.6868616  0.05048805 0.1009761  0.05283551 0.05283551]\n",
      "Action  1  Reward  0.68  Done  False observation [0.         1.         1.         0.15146415 0.2019522  0.63402605\n",
      " 0.63402605 0.1009761  0.1009761  0.         0.        ]\n",
      "Action  0  Reward  0.21999999999999997  Done  False observation [1.         0.         0.         0.1009761  0.15146415 0.6868616\n",
      " 0.6868616  0.         0.         0.05283551 0.05283551]\n",
      "Action  1  Reward  -0.08999999999999997  Done  False observation [1.         0.         1.         0.05048805 0.05048805 0.6868616\n",
      " 0.6868616  0.         0.         0.21134202 0.26417753]\n",
      "Action  1  Reward  -0.12  Done  False observation [0.         1.         0.         0.05048805 0.05048805 0.7396971\n",
      " 0.7396971  0.         0.05048805 0.26417753 0.26417753]\n",
      "Action  0  Reward  -0.030000000000000027  Done  False observation [0.         1.         1.         0.1009761  0.1009761  0.7396971\n",
      " 0.7396971  0.05048805 0.         0.15850651 0.26417753]\n",
      "Action  0  Reward  -0.20999999999999985  Done  False observation [1.         0.         0.         0.05048805 0.05048805 0.79253256\n",
      " 0.7396971  0.         0.         0.21134202 0.21134202]\n",
      "Action  0  Reward  -0.4800000000000002  Done  False observation [1.         0.         1.         0.1009761  0.05048805 0.8453681\n",
      " 0.79253256 0.         0.         0.26417753 0.26417753]\n",
      "Action  1  Reward  -0.16999999999999993  Done  False observation [0.         1.         0.         0.15146415 0.1009761  0.79253256\n",
      " 0.7396971  0.05048805 0.         0.21134202 0.15850651]\n",
      "Action  0  Reward  0.28  Done  False observation [0.         1.         1.         0.15146415 0.1009761  0.79253256\n",
      " 0.7396971  0.1009761  0.         0.10567101 0.05283551]\n",
      "Action  0  Reward  -0.020000000000000018  Done  False observation [1.         0.         0.         0.15146415 0.1009761  0.7396971\n",
      " 0.79253256 0.         0.         0.15850651 0.10567101]\n",
      "Action  0  Reward  -0.3699999999999999  Done  False observation [1.         0.         1.         0.1009761  0.05048805 0.79253256\n",
      " 0.8453681  0.         0.         0.21134202 0.26417753]\n",
      "Action  1  Reward  -0.09000000000000008  Done  False observation [0.         1.         0.         0.1009761  0.1009761  0.7396971\n",
      " 0.79253256 0.05048805 0.         0.15850651 0.26417753]\n",
      "Env and Traci closed successfully.\n"
     ]
    }
   ],
   "source": [
    "# Define paths to the network and route files (Buffalo Road Intersection)\n",
    "\n",
    "# net_file = 'C:/Users/ASUS/Sumo/2024-11-03-18-23-47/osm.net.xml/osm.net.xml'\n",
    "# route_file = 'C:/Users/ASUS/Sumo/2024-11-03-18-23-47/osm.passenger.rou.xml'\n",
    "# out_csv_name = 'C:/Users/ASUS/Sumo/2024-11-03-18-23-47/osm.passenger.csv'\n",
    "\n",
    "\n",
    "# Using Custom simple Traffic Intersection\n",
    "nets_dir = 'nets'\n",
    "\n",
    "file_name = 'single_intersection_simple'\n",
    "out_csv_name = f'{file_name}.csv'\n",
    "\n",
    "nets_file = os.path.join(nets_dir, f'{file_name}.net.xml')\n",
    "routes_file = os.path.join(nets_dir, f'{file_name}.rou.xml')\n",
    "out_csv_name = os.path.join(nets_dir, f'{file_name}.csv')\n",
    "\n",
    "file_exists = lambda file_path: os.path.exists(file_path)\n",
    "\n",
    "if not file_exists(nets_file):\n",
    "    raise FileNotFoundError(f\"Net file not found: {net_file}\")\n",
    "if not file_exists(routes_file):\n",
    "    raise FileNotFoundError(f\"Route file not found: {route_file}\")\n",
    "\n",
    "\n",
    "# Instantiate the environment\n",
    "sumo_env = Environment('sumo-rl-v0', net_file=nets_file, route_file=routes_file, out_csv_name=out_csv_name, render_mode=None, num_seconds=100000)\n",
    "\n",
    "print(\"Observation Space:\", sumo_env.observation_space)\n",
    "print(\"Action Space:\", sumo_env.action_space.n)\n",
    "print(\"Initial State:\", sumo_env.state)\n",
    "\n",
    "max_steps = 100\n",
    "\n",
    "try:\n",
    "    print(\"action space\", sumo_env.action_space)\n",
    "    for step in range(max_steps):\n",
    "        # sumo_env.render()\n",
    "        action = sumo_env.env.action_space.sample()\n",
    "        next_state, reward, done = sumo_env.step(action)\n",
    "        print(\"Action \", action, \" Reward \", reward, \" Done \", done, \"observation\", next_state)\n",
    "        \n",
    "        if done:\n",
    "            sumo_env.reset()\n",
    "            break\n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    print(\"Keyboard interrupt detected. Closing the environment.\")\n",
    "\n",
    "except Exception as e:\n",
    "    print(\"Error occurred:\", e)\n",
    "\n",
    "finally:\n",
    "    sumo_env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A2C implementation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "\n",
    "input_nodes = 128\n",
    "\n",
    "class ActorCritic(nn.Module):\n",
    "    def __init__(self, observation_size, action_size, hidden_size=128):\n",
    "        super(ActorCritic, self).__init__()\n",
    "        self.fc = nn.Linear(observation_size, hidden_size)\n",
    "        self.actor = nn.Linear(hidden_size, action_size)\n",
    "        self.critic = nn.Linear(hidden_size, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc(x))\n",
    "        action_probs = torch.softmax(self.actor(x), dim=-1)\n",
    "        state_value = self.critic(x)\n",
    "        return action_probs, state_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class A2CAgent:\n",
    "    def __init__(self, env, gamma=0.99, learning_rate=1e-3):\n",
    "        self.env = env\n",
    "        self.gamma = gamma\n",
    "        self.policy = ActorCritic(env.observation_space, env.action_space.n)\n",
    "        self.optimizer = optim.Adam(self.policy.parameters(), lr=learning_rate)\n",
    "    \n",
    "    def choose_action(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0)\n",
    "        action_probs, _ = self.policy(state)\n",
    "        action = torch.multinomial(action_probs, 1).item()\n",
    "        return action\n",
    "    \n",
    "    def choose_greedy_action(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0)\n",
    "        action_probs, _ = self.policy(state)\n",
    "        action = torch.argmax(action_probs).item()  # choosing action with the highest probability\n",
    "        return action\n",
    "\n",
    "    def compute_returns(self, rewards, dones, next_value):\n",
    "        returns = []\n",
    "        R = next_value\n",
    "        for r, done in zip(reversed(rewards), reversed(dones)):\n",
    "            R = r + self.gamma * R * (1 - done)\n",
    "            returns.insert(0, R)\n",
    "        return torch.tensor(returns)\n",
    "\n",
    "    def train(self, n_episodes=1000, max_steps=500):\n",
    "\n",
    "        p_bar = tqdm(range(n_episodes), colour='green', desc='Training progress', unit='Episode')\n",
    "\n",
    "        try:\n",
    "            for episode in p_bar:\n",
    "                state = self.env.reset()\n",
    "                log_probs, values, rewards, dones = [], [], [], []\n",
    "                total_reward = 0\n",
    "                done = False\n",
    "                step = 0  # Initialize step counter\n",
    "\n",
    "                # while not done and step < max_steps:\n",
    "                while step < max_steps:\n",
    "                    action = self.choose_action(state)\n",
    "                    next_state, reward, done = self.env.step(action)\n",
    "                    total_reward += reward\n",
    "                    \n",
    "                    # Store experience\n",
    "                    state_tensor = torch.FloatTensor(state).unsqueeze(0)\n",
    "                    action_prob, value = self.policy(state_tensor)\n",
    "                    log_prob = torch.log(action_prob.squeeze(0)[action])\n",
    "                    \n",
    "                    log_probs.append(log_prob)\n",
    "                    values.append(value)\n",
    "                    rewards.append(reward)\n",
    "                    dones.append(done)\n",
    "                    \n",
    "                    state = next_state\n",
    "                    step += 1  # Increment step counter\n",
    "\n",
    "                    # Handle early termination\n",
    "                    # if done:\n",
    "                    #     print(f\"Episode {episode} terminated early at step {step}\")\n",
    "                    #     break\n",
    "                \n",
    "                # Compute loss\n",
    "                next_state_tensor = torch.FloatTensor(next_state).unsqueeze(0)\n",
    "                _, next_value = self.policy(next_state_tensor)\n",
    "                returns = self.compute_returns(rewards, dones, next_value.item())\n",
    "                values = torch.cat(values).squeeze()\n",
    "                \n",
    "                advantage = returns - values\n",
    "                actor_loss = -(torch.stack(log_probs) * advantage.detach()).mean()\n",
    "                critic_loss = advantage.pow(2).mean()\n",
    "                loss = actor_loss + critic_loss\n",
    "                \n",
    "                # Update network\n",
    "                self.optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                nn.utils.clip_grad_norm_(self.policy.parameters(), 1.0)\n",
    "                self.optimizer.step()\n",
    "\n",
    "                # Log progress\n",
    "                p_bar.set_postfix({\n",
    "                    \"Episode Reward\": total_reward,\n",
    "                    \"Steps\": step,\n",
    "                    \"Loss\": loss.item()\n",
    "                })\n",
    "\n",
    "        except traci.exceptions.FatalTraCIError as e:\n",
    "            print(\"TraCI error:\", e)\n",
    "        \n",
    "        except KeyboardInterrupt:\n",
    "            print(\"Training interrupted by the user.\")\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Error occurred in episode {episode}, step {step}: {e}\")\n",
    "            raise\n",
    "\n",
    "        finally:\n",
    "            print(\"Training completed.\")\n",
    "            self.env.close()\n",
    "\n",
    "        # Save the trained model\n",
    "        torch.save(self.policy.state_dict(), 'a2c_trained_model.pth')\n",
    "        print(\"Model saved as 'a2c_trained_model.pth'\")\n",
    "        return rewards\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training progress:   2%|\u001b[32m▏         \u001b[0m| 20/1000 [04:37<3:46:38, 13.88s/Episode, Episode Reward=-2.18, Steps=500, Loss=0.33] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training interrupted by the user.\n",
      "Training completed.\n",
      "Error while closing the environment: 135\n",
      "Model saved as 'a2c_trained_model.pth'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Train the agent\n",
    "nets_dir = 'nets'\n",
    "\n",
    "file_name = 'single_intersection_simple'\n",
    "\n",
    "nets_file = os.path.join(nets_dir, f'{file_name}.net.xml')\n",
    "routes_file = os.path.join(nets_dir, f'{file_name}.rou.xml')\n",
    "out_csv_name = os.path.join(nets_dir+\"/results_csv\", f'{file_name}.a2c.passenger.csv')\n",
    "\n",
    "file_exists = lambda file_path: os.path.exists(file_path)\n",
    "\n",
    "results_dir = os.path.join(nets_dir, 'results_csv')\n",
    "if os.path.exists(results_dir):\n",
    "    shutil.rmtree(results_dir)\n",
    "os.makedirs(results_dir, exist_ok=True)\n",
    "\n",
    "if not file_exists(nets_file):\n",
    "    raise FileNotFoundError(f\"Net file not found: {net_file}\")\n",
    "if not file_exists(routes_file):\n",
    "    raise FileNotFoundError(f\"Route file not found: {route_file}\")\n",
    "\n",
    "num_seconds = 500\n",
    "episodes = 1000\n",
    "\n",
    "\n",
    "\n",
    "sumo_env = Environment('sumo-rl-v0', net_file=nets_file, route_file=routes_file, out_csv_name=out_csv_name, render_mode=None, num_seconds=num_seconds)\n",
    "agent = A2CAgent(sumo_env, gamma=0.99, learning_rate=1e-3)\n",
    "a2c_rewards = agent.train(n_episodes=1000, max_steps=500)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rewards_per_episode_plot(rewards_per_ep, title):\n",
    "    episodes = np.arange(1, len(rewards_per_ep) + 1)\n",
    "    rewards = np.array(rewards_per_ep)\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(episodes, rewards, marker='o', linestyle='-', markersize=4)\n",
    "    plt.title(f'Rewards per Episode {title}')\n",
    "    plt.xlabel('Episode')\n",
    "    plt.ylabel('Reward')\n",
    "    plt.grid(True)\n",
    "    plt.xticks(range(0, len(episodes)+1, max(1, len(episodes)//10)))\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def rewards_per_episode_plot_2(rewards_per_ep, environment_type, epsilons=None, window_size=10):\n",
    "    \"\"\"\n",
    "    Plots rewards per episode with optional smoothing.\n",
    "    \n",
    "    Args:\n",
    "        rewards_per_ep (dict): A dictionary where keys are episodes and values are rewards.\n",
    "        environment_type (str): The type of environment (for plot title).\n",
    "        epsilons (list, optional): Epsilon values per episode, if you'd like to include them in the plot. Default is None.\n",
    "        window_size (int, optional): The window size for moving average smoothing. Default is 10.\n",
    "    \"\"\"\n",
    "    episodes = np.array(list(rewards_per_ep.keys())).flatten()\n",
    "    rewards = np.array(list(rewards_per_ep.values())).flatten()\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(episodes, rewards, marker='o', linestyle='-', markersize=4, label='Rewards per Episode')\n",
    "\n",
    "    # Moving average for smoothing\n",
    "    if window_size > 1:\n",
    "        moving_avg = np.convolve(rewards, np.ones(window_size)/window_size, mode='valid')\n",
    "        plt.plot(episodes[:len(moving_avg)], moving_avg, color='r', label=f'Moving Avg (window={window_size})')\n",
    "\n",
    "    plt.title(f'Rewards per Episode {environment_type}', fontsize=20)\n",
    "    plt.xlabel('Episode', fontsize=14)\n",
    "    plt.ylabel('Reward', fontsize=14)\n",
    "    plt.grid(True)\n",
    "    \n",
    "    # Include epsilon plot if provided\n",
    "    if epsilons:\n",
    "        ax2 = plt.gca().twinx()\n",
    "        ax2.plot(episodes, epsilons, color='g', alpha=0.6, linestyle='--', label='Epsilon')\n",
    "        ax2.set_ylabel('Epsilon', fontsize=14)\n",
    "        ax2.tick_params(axis='y', labelsize=12)\n",
    "\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(\"Average Rewards:\", np.mean(rewards))\n",
    "rewards_per_episode_plot(a2c_rewards, \"A2C Agent Training\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greedy Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def greedy_agent(model, env, n_episodes=100, max_steps=500):\n",
    "    model.eval()  # Set the model to evaluation mode\n",
    "    greedy_rewards_per_episode = []  # Track rewards for each episode\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for episode in range(n_episodes):\n",
    "            state = env.reset()  # Reset the environment at the start of each episode\n",
    "            total_reward = 0\n",
    "            done = False\n",
    "            steps = 0\n",
    "            \n",
    "            while not done and steps < max_steps:\n",
    "                state_tensor = torch.FloatTensor(state).unsqueeze(0)  # Add batch dimension\n",
    "                action_probs, _ = model(state_tensor)  # Forward pass through the policy\n",
    "                greedy_action = torch.argmax(action_probs, dim=1).item()  # Select max probability action\n",
    "                \n",
    "                # Take the selected action in the environment\n",
    "                next_state, reward, done = env.step(greedy_action)\n",
    "                total_reward += reward  # Accumulate reward\n",
    "                \n",
    "                state = next_state  # Update state\n",
    "                steps += 1\n",
    "            \n",
    "            greedy_rewards_per_episode.append(total_reward)\n",
    "\n",
    "    avg_reward = np.mean(greedy_rewards_per_episode)  # Compute average reward over episodes\n",
    "    return avg_reward, greedy_rewards_per_episode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_38112\\690269224.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load('sumo_dqn.pth'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation size Network 11\n",
      "Action size Network 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing Progress:   0%|\u001b[31m          \u001b[0m| 0/100 [00:00<?, ?Episode/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing Progress: 100%|\u001b[31m██████████\u001b[0m| 100/100 [09:30<00:00,  5.71s/Episode]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: -0.2, 1: -0.79, 2: -0.10000000000000012, 3: -0.36, 4: -0.2200000000000001, 5: -0.05000000000000003, 6: -0.14999999999999988, 7: -0.86, 8: -0.66, 9: -0.4899999999999999, 10: -0.45000000000000023, 11: -1.39, 12: -0.41, 13: -0.48, 14: -0.21000000000000002, 15: -0.34999999999999987, 16: -0.14000000000000012, 17: -0.26, 18: -0.45, 19: -0.66, 20: -0.15, 21: -0.6, 22: -0.34, 23: -0.42, 24: -0.73, 25: -0.18, 26: -0.1200000000000001, 27: -0.51, 28: -0.2699999999999999, 29: -0.06, 30: -0.3800000000000002, 31: -0.5699999999999998, 32: -0.33, 33: -0.7900000000000001, 34: -0.05, 35: -0.3900000000000001, 36: -0.22, 37: -1.1100000000000003, 38: -0.040000000000000216, 39: -0.16000000000000023, 40: -0.030000000000000006, 41: -0.52, 42: -0.9, 43: -0.68, 44: -0.27, 45: -0.78, 46: -0.55, 47: -0.5799999999999998, 48: -0.26, 49: -0.13, 50: -0.020000000000000004, 51: -0.19, 52: -0.55, 53: -0.22, 54: -0.87, 55: -0.6199999999999998, 56: -0.38, 57: -0.29, 58: -0.62, 59: -0.4, 60: -0.8000000000000002, 61: -0.4, 62: -0.1899999999999999, 63: -0.009999999999999981, 64: -0.40000000000000013, 65: -0.66, 66: -0.21999999999999997, 67: -0.63, 68: -0.3599999999999999, 69: -0.1100000000000001, 70: -0.32, 71: -0.41, 72: -0.8900000000000002, 73: -0.8, 74: -0.42, 75: -0.75, 76: -0.34, 77: -0.5799999999999998, 78: -0.73, 79: -0.47, 80: -0.36, 81: -0.04000000000000001, 82: -0.1700000000000001, 83: -18.94, 84: -0.010000000000000002, 85: -0.31, 86: -0.73, 87: -0.48, 88: -0.54, 89: -0.13, 90: -0.07999999999999999, 91: -0.43999999999999984, 92: -0.52, 93: -0.52, 94: -0.52, 95: -0.48, 96: -0.53, 97: -0.58, 98: -0.5799999999999998, 99: -89.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Load the trained ActorCritic model\n",
    "trained_model = ActorCritic(sumo_env.observation_space, sumo_env.action_space.n)\n",
    "trained_model.load_state_dict(torch.load('a2c_trained_model.pth'))\n",
    "\n",
    "# Evaluate the greedy agent\n",
    "greedy_avg_reward, greedy_rewards_per_episode = greedy_agent(\n",
    "    model=trained_model, \n",
    "    env=sumo_env, \n",
    "    n_episodes=100, \n",
    "    max_steps=500\n",
    ")\n",
    "\n",
    "print(f\"Greedy Average Reward: {greedy_avg_reward}\")\n",
    "print(f\"Greedy Rewards per Episode: {greedy_rewards_per_episode}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Rewards per episode for Greedy Agent:  -1.4941\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'c:\\\\Users\\\\ASUS\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(\"Avg Rewards per episode for Greedy Agent: \", greedy_avg_rewards_over_eps)\n",
    "rewards_per_episode_plot_2(greedy_rewards_per_episode, 'Sumo-v0', window_size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}