training_main_Rainbow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd3305d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime\n",
    "from shutil import copyfile\n",
    "import sys\n",
    "import math \n",
    "import traci\n",
    "import numpy as np\n",
    "import random\n",
    "import timeit\n",
    "\n",
    "from generator import TrafficGenerator\n",
    "from memory import Memory     ## Prority Experience Memory \n",
    "from visualization import Visualization\n",
    "from utils import import_train_configuration,set_sumo, set_train_path,get_model_path\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0570c094",
   "metadata": {},
   "outputs": [],
   "source": [
    "from d2l import torch as d2l  # Transformer block \n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.autograd as autograd \n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.nn.utils import clip_grad_norm_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "937e5697",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b273f0",
   "metadata": {},
   "source": [
    "## Your Hyperparameter Setting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "467454e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# [simulation]\n",
    "gui = False\n",
    "total_episodes = 25\n",
    "max_steps = 3600\n",
    "n_cars_generated = 1000\n",
    "green_duration = 7\n",
    "yellow_duration = 4\n",
    "\n",
    "# [model]\n",
    "num_layers = 4\n",
    "width_layers = 800\n",
    "batch_size = 64\n",
    "learning_rate = 0.0001\n",
    "training_epochs = 512\n",
    "update_epochs = 10\n",
    "num_atoms = 51\n",
    "Vmin = -10\n",
    "Vmax = 10\n",
    "\n",
    "# [memory]\n",
    "memory_size_min = 1000\n",
    "memory_size_max = 50000\n",
    "\n",
    "# [agent]\n",
    "num_states = 1400\n",
    "num_actions = 4\n",
    "gamma = 0.9\n",
    "\n",
    "# [dir] \n",
    "model_to_test = 1   # load model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b13ef30",
   "metadata": {},
   "source": [
    "## Your Neural Network Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16fa1d5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NoisyLinear(nn.Module):\n",
    "    def __init__(self, in_features, out_features, std_init=0.4):\n",
    "        super(NoisyLinear, self).__init__()\n",
    "        \n",
    "        self.in_features  = in_features\n",
    "        self.out_features = out_features\n",
    "        self.std_init     = std_init\n",
    "        \n",
    "        self.weight_mu    = nn.Parameter(torch.FloatTensor(out_features, in_features))\n",
    "        self.weight_sigma = nn.Parameter(torch.FloatTensor(out_features, in_features))\n",
    "        self.register_buffer('weight_epsilon', torch.FloatTensor(out_features, in_features))\n",
    "        \n",
    "        self.bias_mu    = nn.Parameter(torch.FloatTensor(out_features))\n",
    "        self.bias_sigma = nn.Parameter(torch.FloatTensor(out_features))\n",
    "        self.register_buffer('bias_epsilon', torch.FloatTensor(out_features))\n",
    "        \n",
    "        self.reset_parameters()\n",
    "        self.reset_noise()\n",
    "    \n",
    "    def forward(self, x):\n",
    "        if self.training: \n",
    "            weight = self.weight_mu + self.weight_sigma.mul(Variable(self.weight_epsilon))\n",
    "            bias   = self.bias_mu   + self.bias_sigma.mul(Variable(self.bias_epsilon))\n",
    "        else:\n",
    "            weight = self.weight_mu\n",
    "            bias   = self.bias_mu\n",
    "        \n",
    "        return F.linear(x, weight, bias)\n",
    "    \n",
    "    def reset_parameters(self):\n",
    "        mu_range = 1 / math.sqrt(self.weight_mu.size(1))\n",
    "        \n",
    "        self.weight_mu.data.uniform_(-mu_range, mu_range)\n",
    "        self.weight_sigma.data.fill_(self.std_init / math.sqrt(self.weight_sigma.size(1)))\n",
    "        \n",
    "        self.bias_mu.data.uniform_(-mu_range, mu_range)\n",
    "        self.bias_sigma.data.fill_(self.std_init / math.sqrt(self.bias_sigma.size(0)))\n",
    "    \n",
    "    def reset_noise(self):\n",
    "        epsilon_in  = self._scale_noise(self.in_features)\n",
    "        epsilon_out = self._scale_noise(self.out_features)\n",
    "        \n",
    "        self.weight_epsilon.copy_(epsilon_out.ger(epsilon_in))\n",
    "        self.bias_epsilon.copy_(self._scale_noise(self.out_features))\n",
    "    \n",
    "    def _scale_noise(self, size):\n",
    "        x = torch.randn(size)\n",
    "        x = x.sign().mul(x.abs().sqrt())\n",
    "        return x\n",
    "\n",
    "Variable = lambda *args, **kwargs: autograd.Variable(*args, **kwargs).cuda() if torch.cuda.is_available() else autograd.Variable(*args, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67b5e711",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Categorical & Dueling DQN \n",
    "class Critic(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size,num_layers,num_atoms):\n",
    "        super().__init__()\n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size\n",
    "        self.num_atoms = num_atoms\n",
    "        self.support = torch.linspace(Vmin, Vmax, num_atoms).to(device) \n",
    "        \n",
    "        self.conv1 = nn.Conv2d(3, 16, (1,3), (1,2), padding=(0,1))\n",
    "        # self.bn1 = nn.BatchNorm2d(8)\n",
    "        self.conv2 = nn.Conv2d(16, 32, (2,3), (2,2), padding=(0,1))\n",
    "        self.linear_c = nn.Linear(32*7*25, 4096)\n",
    "        self.linear_p = nn.Linear(4,10) \n",
    "        self.linear1 = nn.Linear(4096, 2048)\n",
    "        self.linear2 = NoisyLinear(2068, 1024)   \n",
    "        self.linear3 = NoisyLinear(2068, 1024)\n",
    "        self.linear_A = NoisyLinear(1024, self.output_size*self.num_atoms)\n",
    "        self.linear_V = NoisyLinear(1024, 1*self.num_atoms)\n",
    "    \n",
    "    def forward(self, s,a,b):\n",
    "        dist = self.dist(s,a,b)\n",
    "        q = torch.sum(dist*self.support, dim=2)\n",
    "        return q\n",
    "    \n",
    "    def dist(self, s,a,b):\n",
    "        x = F.relu(self.conv2(F.relu(self.conv1(s))))\n",
    "        x = x.view(-1, 32*7*25)\n",
    "        x = F.relu(self.linear1(F.relu(self.linear_c(x)))) \n",
    "        a = torch.tensor(a,dtype=torch.float).view(-1,4)\n",
    "        b = torch.tensor(b,dtype=torch.float).view(-1,4)\n",
    "        a = F.relu(self.linear_p(a)).view(-1,10)\n",
    "        b = F.relu(self.linear_p(b)).view(-1,10) \n",
    "        x = torch.cat([x,a,b],dim=1).view(-1,2068)\n",
    "        V = self.linear_V(F.relu(self.linear2(x)))\n",
    "        A = self.linear_A(F.relu(self.linear3(x)))\n",
    "        V = V.view(-1,1,self.num_atoms)\n",
    "        A = A.view(-1,self.output_size,self.num_atoms)\n",
    "        q = V + A - A.mean(dim=1, keepdim=True)\n",
    "        \n",
    "        dist = F.softmax(q,dim=-1)\n",
    "        dist = dist.clamp(min=1e-3) # for avoiding nans \n",
    "        return dist\n",
    "    \n",
    "    def reset_noise(self):\n",
    "        self.linear2.reset_noise()\n",
    "        self.linear3.reset_noise()\n",
    "        self.linear_A.reset_noise()\n",
    "        self.linear_V.reset_noise()\n",
    "        \n",
    "    # def feature_size(self):\n",
    "        # return self.conv2(self.conv1(torch.zeros(1, *self.input_size))).view(1, -1).size(1)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "c4ea1311",
   "metadata": {},
   "source": [
    "# DQN Framework --> Rainbow\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "bdcdcea5",
   "metadata": {},
   "source": [
    "## 3-Step TD Learning\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7998e2",
   "metadata": {},
   "source": [
    "- Paper link: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf32a236",
   "metadata": {},
   "source": [
    "## Phase Setting based on Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2483dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# phase codes based on SUMO environment.net.xml \n",
    "PHASE_NS_GREEN = 0  # action 0 for Variable Order\n",
    "PHASE_NS_YELLOW = 1\n",
    "PHASE_NSL_GREEN = 2  # action 1 for Variable Order\n",
    "PHASE_NSL_YELLOW = 3\n",
    "PHASE_EW_GREEN = 4  # action 2 for Variable Order\n",
    "PHASE_EW_YELLOW = 5\n",
    "PHASE_EWL_GREEN = 6  # action 3 for Variable Order\n",
    "PHASE_EWL_YELLOW = 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27991183",
   "metadata": {},
   "source": [
    "## Your State Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acc7a6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_state():\n",
    "    \"\"\"\n",
    "    Retrieve the state of the intersection from sumo, in the form of cell occupancy\n",
    "    \"\"\"\n",
    "    #state = np.zeros((56))        ## Vector \n",
    "    state = np.zeros((3,14,100))   ## DTSE \n",
    "    \n",
    "    lane = [\"N2TL_0\",\"N2TL_1\",\"N2TL_2\",\"E2TL_0\",\"E2TL_1\",\"E2TL_2\",\"E2TL_3\",\"S2TL_0\",\"S2TL_1\",\"S2TL_2\",\"W2TL_0\",\"W2TL_1\",\"W2TL_2\",\"W2TL_3\"]\n",
    "    car_list = traci.vehicle.getIDList()\n",
    "    lane_group = 0\n",
    "\n",
    "    for car_id in car_list:\n",
    "        lane_pos = traci.vehicle.getLanePosition(car_id)\n",
    "        car_speed = traci.vehicle.getSpeed(car_id)\n",
    "        lane_id = traci.vehicle.getLaneID(car_id)\n",
    "        # inversion of lane pos, so if the car is close to the traffic light -> lane_pos = 0 --- 750 = max len of a road\n",
    "        lane_pos = 750 - lane_pos\n",
    "        \n",
    "        # distance in meters from the traffic light -> mapping into cells\n",
    "        lane_cell = int(lane_pos/7.5)\n",
    "\n",
    "        for i in range(len(lane)):\n",
    "            if lane_id == lane[i]:\n",
    "                lane_group = i\n",
    "\n",
    "        state[0][lane_group][lane_cell] = 1\n",
    "        state[1][lane_group][lane_cell] = car_speed\n",
    "        state[2][lane_group][lane_cell] = traci.vehicle.getAccumulatedWaitingTime(car_id)\n",
    "\n",
    "        #if lane_cell <= 10: \n",
    "        #    state[lane_group] += 1\n",
    "        #state[lane_group+14] += 1\n",
    "        #state[lane_group+28] += car_speed\n",
    "        #state[lane_group+42] += traci.vehicle.getAccumulatedWaitingTime(car_id)\n",
    "        \n",
    "    return state.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffebf6e",
   "metadata": {},
   "source": [
    "## Your Action Definition "
   ]
  },
  {
   "cell_type": "raw",
   "id": "3c1d8be0",
   "metadata": {},
   "source": [
    "#Duration_NS = 12\n",
    "#Duration_NSL = 9\n",
    "#Duration_EW = 16\n",
    "#Duration_EWL = 7\n",
    "\n",
    "Duration_NS = 18\n",
    "Duration_NSL = 7\n",
    "Duration_EW = 16\n",
    "Duration_EWL = 7\n",
    "\n",
    "# Duration_NS = 30\n",
    "# Duration_NSL = 11\n",
    "# Duration_EW = 22\n",
    "# Duration_EWL = 12\n",
    "\n",
    "O = [Duration_NS,Duration_NSL,Duration_EW,Duration_EWL]\n",
    "duration = [Duration_NS,Duration_NSL,Duration_EW,Duration_EWL]\n",
    "old_duration = [Duration_NS,Duration_NSL,Duration_EW,Duration_EWL]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2719e984",
   "metadata": {},
   "source": [
    "# 1. Simulation Part "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9b6d23e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Simulation:\n",
    "    def __init__(self, Model, Memory, TrafficGen, sumo_cmd, gamma, max_steps, green_duration, yellow_duration, num_states, num_actions):\n",
    "        self._model = Model\n",
    "        self._Model_A = Model.critic\n",
    "        self._Model_B = Model.critic_target\n",
    "        self._Memory = Memory\n",
    "        self._TrafficGen = TrafficGen\n",
    "        self._gamma = gamma\n",
    "        self._step = 0\n",
    "        self._sumo_cmd = sumo_cmd\n",
    "        self._max_steps = max_steps \n",
    "        self._green_duration = green_duration\n",
    "        self._yellow_duration = yellow_duration\n",
    "        self._num_states = num_states\n",
    "        self._num_actions = num_actions\n",
    "        self._reward_store = []\n",
    "        self._speed_store = []\n",
    "        self._cumulative_wait_store = []\n",
    "        self._avg_queue_length_store = []\n",
    "\n",
    "    def run(self, episode, epsilon):\n",
    "        \"\"\"\n",
    "        Runs an episode of simulation, then starts a training session\n",
    "        \"\"\"\n",
    "        self.training = False\n",
    "        start_time = timeit.default_timer()\n",
    "\n",
    "        # first, generate the route file for this simulation and set up sumo\n",
    "        self._TrafficGen.generate_routefile(seed=episode)\n",
    "        traci.start(self._sumo_cmd)\n",
    "        print(\"Simulating...\")\n",
    "\n",
    "        # inits\n",
    "        self._step = 0\n",
    "        self._waiting_times = {}\n",
    "        self._sum_neg_reward = 0\n",
    "        self._sum_queue_length = 0\n",
    "        self._sum_waiting_time = 0\n",
    "        self._sum_speed = 0\n",
    "        old_total_wait = 0\n",
    "        old_action = 0\n",
    "        reward = 0\n",
    "        re = 0\n",
    "        current_phase = 0\n",
    "        self.reward = 0\n",
    "        duration = [0,0,0,0]\n",
    "        old_duration = [0,0,0,0]\n",
    "        old_duration_2 = [0,0,0,0]\n",
    "        old_duration_3 = [0,0,0,0]\n",
    "        old_action_2 = 0\n",
    "        old_action_3 = 0\n",
    "        reward_1 = 0\n",
    "        reward_2 = 0\n",
    "        x = [0,0,0,0]\n",
    "        x_1 = [0,0,0,0]\n",
    "        x_2 = [0,0,0,0]\n",
    "        x_3 = [0,0,0,0]\n",
    "        \n",
    "        self._simulate(50)  ## Warm Environment\n",
    "        old_state = _get_state()\n",
    "        old_state_2 = _get_state()\n",
    "        old_state_3 = _get_state()\n",
    "\n",
    "        while self._step < self._max_steps:\n",
    "            \n",
    "            # get current state of the intersection\n",
    "            current_state = _get_state()\n",
    "\n",
    "            # calculate reward of previous action: \n",
    "            # waiting time = seconds waited by a car since the spawn in the environment, cumulated for every car in incoming lanes\n",
    "            current_total_wait = self._collect_waiting_times()    \n",
    "            \n",
    "            ## Your Reward Function \n",
    "            reward = - current_total_wait\n",
    "            \n",
    "            ## (change in cumulative waiting time between actions) is not suitable for Multi-step TD learning \n",
    "            # reward = old_total_wait - current_total_wait           \n",
    "    \n",
    "            current_phase = int(traci.trafficlight.getPhase(\"TL\")/2)\n",
    "            phase = current_phase - 1\n",
    "            if phase == -1:\n",
    "                phase = 3\n",
    "                \n",
    "            action = self._choose_action(current_state, epsilon,current_phase,duration) # phase, epsilon) \n",
    "            \n",
    "            # print(phase, old_action, old_duration, duration)\n",
    "            # saving the data into the memory\n",
    "            if self._step != 0:\n",
    "                x = [0,0,0,0]\n",
    "                y = [0,0,0,0]\n",
    "                x[old_action] = 1\n",
    "                y[action] = 1         \n",
    "                \n",
    "                if self._step < self._max_steps - self._green_duration - self._yellow_duration:\n",
    "                    TD = self._model.get_TD(old_state_3,old_action_3,reward_2,reward_1,reward,current_state,0,x_3,y,old_duration_3,duration).squeeze(0).detach().tolist()\n",
    "                    self._Memory.add_sample(TD, (old_state_3,old_action_3,reward_2,reward_1,reward,current_state,0,x_3,y,old_duration_3,duration))\n",
    "                else:\n",
    "                    TD = self._model.get_TD(old_state_3,old_action_3,reward_2,reward_1,reward,current_state,1,x_3,y,old_duration_3,duration).squeeze(0).detach().tolist()\n",
    "                    self._Memory.add_sample(TD, (old_state_3,old_action_3,reward_2,reward_1,reward,current_state,1,x_3,y,old_duration_3,duration))\n",
    "\n",
    "            # if the chosen phase is different from the last phase, activate the yellow phase\n",
    "            if self._step != 0 and old_action != action:# and i == 0:\n",
    "                self._set_yellow_phase(current_phase)\n",
    "                self._simulate(self._yellow_duration)\n",
    "                duration[action] = self._green_duration\n",
    "            else: \n",
    "                duration[action] += 7\n",
    "                \n",
    "            self._set_green_phase(action)\n",
    "            self._simulate(self._green_duration)\n",
    "            # print(\"current phase:\",current_phase,\"green:\",a)\n",
    "\n",
    "            # saving variables for later & accumulate reward\n",
    "            old_state_3 = old_state_2\n",
    "            old_state_2 = old_state\n",
    "            old_state = current_state\n",
    "            old_action_3 = old_action_2\n",
    "            old_action_2 = old_action\n",
    "            old_action = action\n",
    "            x_3 = x_2\n",
    "            x_2 = x_1\n",
    "            x_1 = x\n",
    "            old_duration_3 = old_duration_2\n",
    "            old_duration_2 = old_duration\n",
    "            old_duration = duration\n",
    "            old_total_wait = current_total_wait\n",
    "            reward_2 = reward_1\n",
    "            reward_1 = reward\n",
    "\n",
    "            # saving only the meaningful reward to better see if the agent is behaving correctly\n",
    "            # if reward < 0:\n",
    "            self._sum_neg_reward += reward\n",
    "            re += 1\n",
    "            self.reward = self._sum_neg_reward/re\n",
    "                \n",
    "        print(\"Total Queue:\",self._sum_queue_length, \"  \", \"Average Reward:\", self.reward, \" \", \"Average Speed:\",self._sum_speed/self._max_steps)\n",
    "\n",
    "        self._save_episode_stats()\n",
    "        print(\"Total Reward:\", self._sum_neg_reward, \"- Epsilon:\", round(epsilon, 2))\n",
    "        traci.close()\n",
    "        simulation_time = round(timeit.default_timer() - start_time, 1)\n",
    "\n",
    "        return simulation_time\n",
    "\n",
    "\n",
    "    def _simulate(self, steps_todo):\n",
    "        \"\"\"\n",
    "        Execute steps in sumo while gathering statistics\n",
    "        \"\"\"\n",
    "        if (self._step + steps_todo) >= self._max_steps:  # do not do more steps than the maximum allowed number of steps\n",
    "            steps_todo = self._max_steps - self._step\n",
    "\n",
    "        while steps_todo > 0:\n",
    "            traci.simulationStep()  # simulate 1 step in sumo\n",
    "            self._step += 1 # update the step counter\n",
    "            steps_todo -= 1\n",
    "            queue_length = self._get_queue_length()\n",
    "            self._sum_queue_length += queue_length\n",
    "            self._sum_waiting_time += queue_length # 1 step while wating in queue means 1 second waited, for each car, therefore queue_lenght == waited_seconds\n",
    "            speed = self._get_speed()\n",
    "            self._sum_speed += speed\n",
    "        \n",
    "    def _collect_waiting_times(self):            # For reward \n",
    "        \"\"\"\n",
    "        Retrieve the waiting time of every car in the incoming roads\n",
    "        \"\"\"\n",
    "        incoming_roads = [\"E2TL\", \"N2TL\", \"W2TL\", \"S2TL\"]\n",
    "        car_list = traci.vehicle.getIDList()\n",
    "        self._waiting_times = {}\n",
    "        for car_id in car_list:\n",
    "            wait_time = traci.vehicle.getAccumulatedWaitingTime(car_id)\n",
    "            road_id = traci.vehicle.getRoadID(car_id)  # get the road id where the car is located\n",
    "            if road_id in incoming_roads:  # consider only the waiting times of cars in incoming roads\n",
    "                self._waiting_times[car_id] = wait_time \n",
    "            else:\n",
    "                if car_id in self._waiting_times: # a car that was tracked has cleared the intersection\n",
    "                    del self._waiting_times[car_id] \n",
    "                \n",
    "        if len(self._waiting_times) == 0: \n",
    "            total_waiting_time = 0\n",
    "        else: \n",
    "            total_waiting_time = sum(self._waiting_times.values())/len(self._waiting_times)\n",
    "        return total_waiting_time \n",
    "\n",
    "    def _choose_action(self, state, epsilon,phase,old_duration): #phase, epsilon):\n",
    "        \"\"\"\n",
    "        Decide wheter to perform an explorative or exploitative action, according to an epsilon-greedy policy\n",
    "        \"\"\"\n",
    "        if random.random() < epsilon:\n",
    "            return random.randint(0, self._num_actions - 1) # random action\n",
    "        else:\n",
    "            x = [0,0,0,0]\n",
    "            x[phase] = 1\n",
    "            return torch.argmax(self._model.predict(state,x,old_duration))#,phase)) # the best action given the current state\n",
    "\n",
    "    def _set_yellow_phase(self, old_action):\n",
    "        \"\"\"\n",
    "        Activate the correct yellow light combination in sumo\n",
    "        \"\"\"\n",
    "        yellow_phase_code = old_action * 2 + 1 # obtain the yellow phase code, based on the old action (ref on environment.net.xml)\n",
    "        traci.trafficlight.setPhase(\"TL\", yellow_phase_code)\n",
    "\n",
    "    def _set_green_phase(self, action_number):   ## For Variable Order Method \n",
    "        \"\"\"\n",
    "        Activate the correct green light combination in sumo\n",
    "        \"\"\"\n",
    "        if action_number == 0:\n",
    "            traci.trafficlight.setPhase(\"TL\", PHASE_NS_GREEN)\n",
    "        elif action_number == 1:\n",
    "            traci.trafficlight.setPhase(\"TL\", PHASE_NSL_GREEN)\n",
    "        elif action_number == 2:\n",
    "            traci.trafficlight.setPhase(\"TL\", PHASE_EW_GREEN)\n",
    "        elif action_number == 3:\n",
    "            traci.trafficlight.setPhase(\"TL\", PHASE_EWL_GREEN)\n",
    "\n",
    "    def _get_green(self,current_phase):       ## For Finetuning Method \n",
    "        if current_phase == 0:\n",
    "            green = Duration_NS\n",
    "        elif current_phase == 1:\n",
    "            green = Duration_NSL\n",
    "        elif current_phase == 2:\n",
    "            green = Duration_EW\n",
    "        else: \n",
    "            green = Duration_EWL\n",
    "        return green\n",
    "\n",
    "    def _get_queue_length(self):          # For evaluation \n",
    "        \"\"\"\n",
    "        Retrieve the number of cars with speed = 0 in every incoming lane\n",
    "        \"\"\"\n",
    "        halt_N = traci.edge.getLastStepHaltingNumber(\"N2TL\")\n",
    "        halt_S = traci.edge.getLastStepHaltingNumber(\"S2TL\")\n",
    "        halt_E = traci.edge.getLastStepHaltingNumber(\"E2TL\")\n",
    "        halt_W = traci.edge.getLastStepHaltingNumber(\"W2TL\")\n",
    "        queue_length = halt_N + halt_S + halt_E + halt_W\n",
    "        return queue_length\n",
    "    \n",
    "    def _get_speed(self):                  # For evaluation \n",
    "        total_speed = 0\n",
    "        car_list = traci.vehicle.getIDList()\n",
    "        for car_id in car_list:\n",
    "            car_speed = traci.vehicle.getSpeed(car_id)\n",
    "            total_speed +=car_speed\n",
    "        if len(car_list) == 0: \n",
    "            s = 0\n",
    "        else: \n",
    "            s = total_speed/len(car_list)\n",
    "        return s\n",
    "            \n",
    "    def _save_episode_stats(self):\n",
    "        \"\"\"\n",
    "        Save the stats of the episode to plot the graphs at the end of the session\n",
    "        \"\"\"\n",
    "        self._reward_store.append(self.reward)  # how much negative reward in this episode\n",
    "        self._speed_store.append(self._sum_speed / self._max_steps)\n",
    "        self._cumulative_wait_store.append(self._sum_waiting_time)  # total number of seconds waited by cars in this episode\n",
    "        self._avg_queue_length_store.append(self._sum_queue_length / self._max_steps)  # average number of queued cars per step, in this episode\n",
    "    \n",
    "    @property\n",
    "    def reward_store(self):\n",
    "        return self._reward_store\n",
    "\n",
    "    @property\n",
    "    def speed_store(self):\n",
    "        return self._speed_store\n",
    "    \n",
    "    @property\n",
    "    def cumulative_wait_store(self):\n",
    "        return self._cumulative_wait_store\n",
    "\n",
    "    @property\n",
    "    def avg_queue_length_store(self):\n",
    "        return self._avg_queue_length_store"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90af0691",
   "metadata": {},
   "source": [
    "# 2. Training Part "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "643c5bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Training:\n",
    "    def __init__(self, Model, Memory, training_epochs, update_epochs, batch_size):\n",
    "        self._model = Model\n",
    "        self._Model_A = Model.critic\n",
    "        self._Model_B = Model.critic_target\n",
    "        self._Memory = Memory\n",
    "        self._loss_store = []\n",
    "        self._batch_size = batch_size\n",
    "        self.history = 0\n",
    "        self.loss_value = 0\n",
    "            \n",
    "    def run(self):\n",
    "        self.training = True\n",
    "        print(\"Training...\")\n",
    "        start_time = timeit.default_timer()\n",
    "        self.loss_value = 0\n",
    "        \n",
    "        for _ in range(training_epochs):\n",
    "            self.experience_replay()\n",
    "            self.loss_value += self.history\n",
    "            if ( _ / update_epochs == 0):\n",
    "                self._model._update_model()\n",
    "        self.loss_value = torch.tensor(self.loss_value)\n",
    "        self._save_loss()\n",
    "        print(\"loss:\",self.loss_value)\n",
    "        training_time = round(timeit.default_timer() - start_time, 1)\n",
    "        \n",
    "        return training_time\n",
    "    \n",
    "    def experience_replay(self):\n",
    "        \"\"\"\n",
    "        Retrieve a group of samples from the memory and for each of them update the learning equation, then train\n",
    "        \"\"\"\n",
    "        #batch = self._Memory.get_samples(self._batch_size)  \n",
    "        batch, idxs, is_weights = self._Memory.get_samples(self._batch_size) \n",
    "    \n",
    "        if len(batch) > 0:  # if the memory is full enough\n",
    "            old_states_3 = torch.tensor(([val[0] for val in batch]),dtype=torch.float).to(device) \n",
    "            old_actions_3 = torch.tensor(([val[1] for val in batch]),dtype=torch.int64).to(device)\n",
    "            rewards_2 = torch.tensor(([val[2] for val in batch]),dtype=torch.float).reshape(-1,1).to(device)\n",
    "            rewards_1 = torch.tensor(([val[3] for val in batch]),dtype=torch.float).reshape(-1,1).to(device)\n",
    "            rewards = torch.tensor(([val[4] for val in batch]),dtype=torch.float).reshape(-1,1).to(device)\n",
    "            next_states = torch.tensor(([val[5] for val in batch]),dtype=torch.float).to(device)  \n",
    "            dones = torch.tensor(([val[6] for val in batch]),dtype=torch.float).reshape(-1,1).to(device)\n",
    "            a = torch.tensor(([val[7] for val in batch]),dtype=torch.float).to(device)\n",
    "            b = torch.tensor(([val[8] for val in batch]),dtype=torch.float).to(device)\n",
    "            c = torch.tensor(([val[9] for val in batch]),dtype=torch.float).to(device)\n",
    "            d = torch.tensor(([val[10] for val in batch]),dtype=torch.float).to(device)\n",
    "            weights = torch.tensor((is_weights),dtype=torch.float).reshape(-1,1).to(device)\n",
    "            \n",
    "            ## train the NN  \n",
    "            self.history,TD = self._model.critic_learn(old_states_3,old_actions_3,rewards_2,rewards_1,rewards,next_states,dones,a,b,c,d,weights) \n",
    "            self.history = self.history.to(\"cpu\").squeeze(0).detach() \n",
    "            TD = TD.squeeze(0).detach().tolist()\n",
    "            ## update the priorities \n",
    "            #TD = (self._model.get_TD(old_states_3,old_actions_3,rewards_2,rewards_1,rewards,next_states,dones,a,b,c,d)).squeeze(0).detach().tolist()\n",
    "            \n",
    "            for i in range(len(batch)): \n",
    "                idx = idxs[i]\n",
    "                td = TD[i]   # prior_eps \n",
    "                # s0,a0,r1,s1,done = batch[i]\n",
    "                # TD = self._model.get_TD(s0,a0,r1,s1,0.8,done).squeeze(0).detach().numpy()\n",
    "                self._Memory.update(idx,td)\n",
    "                \n",
    "            self._Model_A.reset_noise()\n",
    "            self._Model_B.reset_noise()\n",
    "                    \n",
    "    def _save_loss(self):\n",
    "        self._loss_store.append(self.loss_value)\n",
    "\n",
    "    @property\n",
    "    def loss_store(self):\n",
    "        return self._loss_store"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85df95f1",
   "metadata": {},
   "source": [
    "## Your Deep Reinforcement Learning Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98543acd",
   "metadata": {},
   "source": [
    "#### Categorical DQN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3595e2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def projection_distribution(ns, r2,r1,r, dones,b,d):\n",
    "    with torch.no_grad():\n",
    "        batch_size  = ns.size(0)\n",
    "        \n",
    "        delta_z = float(Vmax - Vmin) / (num_atoms - 1)\n",
    "        support = torch.linspace(Vmin, Vmax, num_atoms).to(device)\n",
    "\n",
    "        next_action = Model.critic_target(ns,b,d).argmax(1)\n",
    "        next_dist = Model.critic_target.dist(ns,b,d)\n",
    "        next_dist = next_dist[range(batch_size),next_action]\n",
    "\n",
    "        # Tz = rewards + (1 - dones) * gamma * support\n",
    "        Tz = r2+gamma*r1+gamma**2*r + gamma**3 * support * (1-dones)\n",
    "        Tz = Tz.clamp(min=Vmin, max=Vmax)\n",
    "        b  = (Tz - Vmin) / delta_z\n",
    "        l  = b.floor().long()\n",
    "        u  = b.ceil().long()\n",
    "\n",
    "        offset = torch.linspace(0, (batch_size - 1) * num_atoms, batch_size).long()\\\n",
    "                        .unsqueeze(1).expand(batch_size, num_atoms).to(device)\n",
    "\n",
    "        proj_dist = torch.zeros(next_dist.size()).to(device)    \n",
    "        proj_dist.view(-1).index_add_(0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1))\n",
    "        proj_dist.view(-1).index_add_(0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1))\n",
    "        \n",
    "    return proj_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1a46a1",
   "metadata": {},
   "source": [
    "#### 3-Step TD Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd772f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DRL_Model():\n",
    "    def __init__(self, num_layers, width, batch_size, learning_rate, input_dim, output_dim, tau,num_atoms):\n",
    "        self._i_dim = (3,14,100) #input_dim\n",
    "        self._o_dim = output_dim\n",
    "        self.critic_lr = learning_rate\n",
    "        self.tau = tau\n",
    "        self.batch_size = batch_size\n",
    "        \n",
    "        self.critic = Critic(self._i_dim, width, self._o_dim, num_layers,num_atoms)\n",
    "        self.critic_target = Critic(self._i_dim, width, self._o_dim, num_layers,num_atoms)\n",
    "        self.critic_optim = optim.Adam(self.critic.parameters(), lr = self.critic_lr)\n",
    "        self._update_model()\n",
    "        self.critic.to(device)\n",
    "        self.critic_target.to(device)\n",
    "     \n",
    "    ## DQN Algorithm \n",
    "    def critic_learn(self,s3,a3,r2,r1,r,ns,done,a,b,c,d,weights):    \n",
    "        # y_pred = torch.zeros(self.batch_size,1)\n",
    "        # y_true =  r2+gamma*r1+gamma**2*r+gamma**3*torch.max(self.critic_target(ns,b,d)).detach()       #*(1-done) ).detach()   # y_true \n",
    "        # a3 = torch.tensor(a3,dtype=torch.int64).reshape(-1,1)\n",
    "        # y_pred = torch.gather(self.critic(s3,a,c),1,a3)\n",
    "        # loss_fn = nn.MSELoss()\n",
    "        # loss = (weights * loss_fn(y_pred, y_true)).mean()\n",
    "        \n",
    "        proj_dist = projection_distribution(ns, r2,r1,r, done,b,d)\n",
    "        dist = self.critic.dist(s3,a,c) \n",
    "        log_p = torch.log(dist[range(batch_size),a3]) \n",
    "        elementwise_loss = - (Variable(proj_dist) * log_p).sum(1)\n",
    "        TD = elementwise_loss.detach()\n",
    "        loss = torch.mean(weights * elementwise_loss) \n",
    "        \n",
    "        self.critic_optim.zero_grad()\n",
    "        loss.backward()\n",
    "        clip_grad_norm_(self.critic.parameters(), 10.0)\n",
    "        self.critic_optim.step()\n",
    "        return loss,TD\n",
    "    \n",
    "    def get_TD(self,s3,a3,r2,r1,r,ns,done,a,b,c,d, alpha=0.6):        # for priority experience replay \n",
    "        s3 = torch.tensor(s3,dtype=torch.float).reshape(-1,3,14,100).to(device)\n",
    "        ns = torch.tensor(ns,dtype=torch.float).reshape(-1,3,14,100).to(device)\n",
    "        a = torch.tensor(a,dtype=torch.float).to(device)\n",
    "        b = torch.tensor(b,dtype=torch.float).to(device)\n",
    "        c = torch.tensor(c,dtype=torch.float).to(device)\n",
    "        d = torch.tensor(d,dtype=torch.float).to(device)\n",
    "        done = torch.tensor(done,dtype=torch.float).reshape(-1,1).to(device)\n",
    "        a3 = torch.tensor(a3,dtype=torch.int64).reshape(-1,1).to(device)\n",
    "        r2 = torch.tensor(r2,dtype=torch.float).to(device)\n",
    "        r1 = torch.tensor(r1,dtype=torch.float).to(device)\n",
    "        r = torch.tensor(r,dtype=torch.float).to(device)\n",
    "        \n",
    "        #y_true = r2+gamma*r1+gamma**2*r+gamma**3*torch.max(self.critic_target(ns,b,d))#*(1-done)   # y_true \n",
    "        #y_pred = torch.zeros(self.batch_size,1)\n",
    "        #y_pred = torch.gather(self.critic(s3,a,c),1,a3)\n",
    "        #TD = abs(y_true - y_pred).detach() \n",
    "        \n",
    "        proj_dist = projection_distribution(ns, r2,r1,r, done,b,d)\n",
    "        dist = self.critic.dist(s3,a,c)\n",
    "        \n",
    "        action = a3.unsqueeze(1).expand(s3.shape[0], 1, num_atoms)\n",
    "        dist = dist.gather(1, action).squeeze(1)\n",
    "        dist.data.clamp_(0.01, 0.99)\n",
    "        log_p = dist.log() \n",
    "        #log_p = torch.log(torch.gather(self.critic.dist(s3,a,c),1,a3))\n",
    "        \n",
    "        elementwise_loss = - (Variable(proj_dist) * log_p).sum(1)\n",
    "        elementwise_loss = elementwise_loss.mean()\n",
    "        TD = elementwise_loss\n",
    "        \n",
    "        return TD\n",
    "    \n",
    "    def predict(self, s,a,c):\n",
    "        s = torch.tensor(s, dtype=torch.float).unsqueeze(0).to(device)\n",
    "        a = torch.tensor(a,dtype=torch.float).to(device)\n",
    "        c = torch.tensor(c,dtype=torch.float).to(device)\n",
    "        return self.critic(s,a,c).squeeze(0).detach()\n",
    "        \n",
    "    def _update_model(self):\n",
    "        self.critic_target.load_state_dict(self.critic.state_dict())\n",
    "        self.critic_target.eval()\n",
    "                                       \n",
    "    def soft_update(self,net_target, net):\n",
    "        for target_param, param  in zip(net_target.parameters(), net.parameters()):\n",
    "            target_param.data.copy_(target_param.data * (1.0 - self.tau) + param.data * self.tau)\n",
    "        self.critic_target.eval()\n",
    "        \n",
    "    def _load_my_model(self, model_folder_path):\n",
    "        \"\"\"\n",
    "        Load the model stored in the folder specified by the model number, if it exists\n",
    "        \"\"\"\n",
    "        model_file_path = os.path.join(model_folder_path, 'trained_model.h5')\n",
    "        \n",
    "        if os.path.isfile(model_file_path):\n",
    "            loaded_model = torch.load(model_file_path)\n",
    "            self.critic = loaded_model\n",
    "            print(\"Load model successfully!\")\n",
    "        else:\n",
    "            sys.exit(\"Model number not found!\")\n",
    "            \n",
    "    def save_model(self, path):\n",
    "        \"\"\"\n",
    "        Save the current model in the folder as h5 file and a model architecture summary as png\n",
    "        \"\"\"\n",
    "        torch.save(self.critic, os.path.join(path, 'trained_model.h5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5c4b33",
   "metadata": {},
   "source": [
    "# 3. Run and Result Display "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b8d5edb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    config = import_train_configuration(config_file='training_settings.ini')\n",
    "    sumo_cmd = set_sumo(gui, config['sumocfg_file_name'], max_steps)\n",
    "    path = set_train_path(config['models_path_name'])\n",
    "    model_path = get_model_path(config['models_path_name'], model_to_test)\n",
    "\n",
    "    Model = DRL_Model(num_layers, width_layers, batch_size, learning_rate, input_dim=num_states, output_dim=num_actions,tau=1,num_atoms=num_atoms)\n",
    "\n",
    "    Memory = Memory(memory_size_max, memory_size_min)\n",
    "\n",
    "    TrafficGen = TrafficGenerator(max_steps, n_cars_generated)\n",
    "\n",
    "    Visualization = Visualization(path, dpi=96)\n",
    "        \n",
    "    Simulation = Simulation(Model,Memory,TrafficGen,sumo_cmd,gamma,max_steps,green_duration,yellow_duration,num_states,num_actions)\n",
    "  \n",
    "    Training = Training(Model,Memory,training_epochs,update_epochs,batch_size)\n",
    "    \n",
    "    episode = 0\n",
    "    timestamp_start = datetime.datetime.now()\n",
    "     \n",
    "    # Model._load_my_model(model_path)\n",
    "    \n",
    "    while episode < total_episodes:\n",
    "        print('\\n----- Episode', str(episode+1), 'of', str(total_episodes))\n",
    "        epsilon = (1.0 - ((episode+1) / total_episodes))  # set the epsilon for this episode according to epsilon-greedy policy\n",
    "        #epsilon = 0\n",
    "        simulation_time = Simulation.run(episode, epsilon)  # run the simulation\n",
    "        training_time = Training.run()                      # train the model  \n",
    "        print('Simulation time:', simulation_time, 's - Training time:', training_time, 's - Total:', round(simulation_time+training_time, 1), 's')\n",
    "        episode += 1\n",
    "        \n",
    "    print(\"\\n----- Start time:\", timestamp_start)\n",
    "    print(\"----- End time:\", datetime.datetime.now())\n",
    "    print(\"----- Session info saved at:\", path)\n",
    "\n",
    "    Model.save_model(path)\n",
    "\n",
    "    copyfile(src='training_settings.ini', dst=os.path.join(path, 'training_settings.ini'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7d1d04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Visualization.save_data_and_plot(data=Simulation.reward_store, filename='reward', xlabel='Episode', ylabel='Cumulative negative reward')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "578e3c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Visualization.save_data_and_plot(data=Simulation.cumulative_wait_store, filename='delay', xlabel='Episode', ylabel='Cumulative delay (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e7c57e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Visualization.save_data_and_plot(data=Simulation.avg_queue_length_store, filename='queue', xlabel='Episode', ylabel='Average queue length (vehicles)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fcc6cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Visualization.save_data_and_plot(data=Training.loss_store, filename='Loss', xlabel='Episode', ylabel='Total Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37846a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "Visualization.save_data_and_plot(data=Simulation.speed_store, filename='Speed', xlabel='Episode', ylabel='Average Speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d559ba79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7727e7c7",
   "metadata": {},
   "source": [
    "- Categorical \n",
    "- Action definition \n",
    "- Phase Gate, FRAP \n",
    "- Memory Palace for Change-Stay\n",
    "- Run 3 * 4 model\n",
    "- not too difference with dones"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}