Collisions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a12dba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import GPy\n",
    "import random\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from IPython.display import display\n",
    "from tqdm import tqdm\n",
    "import scipy\n",
    "import time\n",
    "import threading\n",
    "\n",
    "import tensorflow as tf\n",
    "import tf_agents\n",
    "from tf_agents.environments import py_environment\n",
    "from tf_agents.environments import tf_py_environment\n",
    "from tf_agents.specs import array_spec\n",
    "from tf_agents.environments import wrappers\n",
    "from tf_agents.trajectories import time_step as ts\n",
    "from tf_agents.agents.categorical_dqn import categorical_dqn_agent\n",
    "from tf_agents.networks import categorical_q_network\n",
    "from tf_agents.trajectories import trajectory\n",
    "from tf_agents.utils import common\n",
    "\n",
    "tf.compat.v1.enable_v2_behavior()\n",
    "np.random.seed(123)\n",
    "random.seed(123)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "341878c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version:  3.9.7\n",
      "NetworkX version:  2.6.3\n",
      "Scipy version:  1.7.1\n",
      "Numpy version:  1.20.3\n",
      "GPy version:  1.10.0\n",
      "TensorFlow version:  2.8.0\n",
      "TF Agents version:  0.11.0\n"
     ]
    }
   ],
   "source": [
    "from platform import python_version\n",
    "print(\"Python version: \", python_version())\n",
    "print(\"NetworkX version: \", nx.__version__)\n",
    "print(\"Scipy version: \", scipy.__version__)\n",
    "print(\"Numpy version: \", np.__version__)\n",
    "print(\"GPy version: \", GPy.__version__)\n",
    "print(\"TensorFlow version: \", tf.__version__)\n",
    "print(\"TF Agents version: \", tf_agents.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "683d84a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def flatten(liste):\n",
    "    return [item for sublist in liste for item in sublist]\n",
    "\n",
    "def preprocessing(X_s):\n",
    "    X = []\n",
    "    for liste in X_s:\n",
    "        X += liste.copy()\n",
    "    return np.array(X)\n",
    "\n",
    "def copie_paths(paths):\n",
    "    X = []\n",
    "    for liste in paths:\n",
    "        X.append(liste.copy())\n",
    "    return X\n",
    "\n",
    "def copy_X_D(X_D):\n",
    "    X = []\n",
    "    for liste in X_D:\n",
    "        X.append(liste.copy())\n",
    "    return np.array(X)\n",
    "\n",
    "def concatenate(x, y):\n",
    "    if len(x)==0:\n",
    "        return y\n",
    "    else:\n",
    "        return np.concatenate((x, y))\n",
    "\n",
    "def mean_confidence_interval(data, confidence=0.95):\n",
    "    a = 1.0 * np.array(data)\n",
    "    n = len(a)\n",
    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
    "    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n",
    "    return m, m-h, m+h\n",
    "\n",
    "def distance(a,b):\n",
    "    return np.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)\n",
    "        \n",
    "def sigma_matrix(gp_model, X_v):\n",
    "    return gp_model.kern.K(X_v, X_v)\n",
    "    \n",
    "def sigma_prime_matrix(gp_model, X_v, X_s):\n",
    "    kernel = gp_model.rbf\n",
    "    variance_n = float(gp_model.Gaussian_noise.variance)\n",
    "    a = kernel.K(X_v, X_v)\n",
    "    b = kernel.K(X_v, X_s)\n",
    "    c = kernel.K(X_s, X_s)\n",
    "    d = variance_n*np.eye(len(X_s))\n",
    "    e = kernel.K(X_s, X_v)\n",
    "    f = np.linalg.inv(c + d)\n",
    "    g = np.dot(b, f)\n",
    "    h = np.dot(g, e)\n",
    "    return a - h\n",
    "\n",
    "def reward_function(gp_model, X_v, X_s_old, X_s_new):\n",
    "    if len(X_s_old)==0:\n",
    "        sigma_prime_old = sigma_matrix(gp_model, X_v)\n",
    "    else:\n",
    "        sigma_prime_old = sigma_prime_matrix(gp_model, X_v, X_s_old)\n",
    "    if len(X_s_new)==0:\n",
    "        sigma_prime_new = sigma_matrix(gp_model, X_v)\n",
    "    else:\n",
    "        sigma_prime_new = sigma_prime_matrix(gp_model, X_v, X_s_new)\n",
    "    return 0.5 * (np.log(np.linalg.det(sigma_prime_old)) - np.log(np.linalg.det(sigma_prime_new)))\n",
    "\n",
    "def credit_assignment(env, reward, dones):\n",
    "    if env.nb_agents==1:\n",
    "        return np.array([reward])\n",
    "    rewards = np.zeros(env.nb_agents)\n",
    "    if reward==0:\n",
    "        return rewards\n",
    "    X_s = preprocessing(env.X_s)\n",
    "    H = np.log(np.linalg.det(sigma_prime_matrix(env.gp_model, env.X_v, X_s)))\n",
    "    for i in range(env.nb_agents):\n",
    "        if not dones[i]:\n",
    "            X_s_i = env.X_s.copy()\n",
    "            del X_s_i[i]\n",
    "            X_s_i = preprocessing(X_s_i)\n",
    "            if len(X_s_i)==0:\n",
    "                rewards[i] = 0.5 * (np.log(np.linalg.det(sigma_matrix(env.gp_model, env.X_v))) - H)\n",
    "            else:\n",
    "                rewards[i] = 0.5 * (np.log(np.linalg.det(sigma_prime_matrix(env.gp_model, env.X_v, X_s_i))) - H)\n",
    "    somme = np.sum(rewards)\n",
    "    if somme==0:\n",
    "        return rewards\n",
    "    return rewards*(reward/(np.sum(rewards)))\n",
    "\n",
    "def construct_graph(graph_dim, graph_unity):\n",
    "    x = graph_dim[0]\n",
    "    y = graph_dim[1]\n",
    "    i, j = 0.0, 0.0\n",
    "    graph = nx.Graph()\n",
    "    for k in range(x*y):\n",
    "        graph.add_node(k, coord=[i, j])\n",
    "        i += graph_unity\n",
    "        if i==x*graph_unity:\n",
    "            i = 0.0\n",
    "            j += graph_unity\n",
    "    for k in range(x*y):\n",
    "        if (k+1)%x!=0:\n",
    "            graph.add_edge(k, k+1)\n",
    "        if k<x*y-x:\n",
    "            graph.add_edge(k, k+x)\n",
    "    return graph\n",
    "            \n",
    "def show_graph(graph):\n",
    "    nx.draw(graph, with_labels=True, font_weight='bold')\n",
    "\n",
    "def remove_nodes_from_graph(graph, liste):\n",
    "    graph.remove_nodes_from(liste)\n",
    "    return graph\n",
    "\n",
    "def incertitude_initiale(sigma, graph):\n",
    "    return 0.5 * np.log(np.linalg.det(sigma)) + (graph.number_of_nodes()/2) * (1 + np.log(2*np.pi))\n",
    "\n",
    "def coord_to_id(coord, graph_dim):\n",
    "    return graph_dim[0] * int(coord[1]) + int(coord[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc691978",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_pilot_data(nb_agents, exploration_steps, graph, graph_dim, means, variance, lengthscale, memory_window, gamma):\n",
    "    X_D = np.array([graph.nodes[i][\"coord\"] for i in range(graph.number_of_nodes())])\n",
    "    simulation_kernel = GPy.kern.RBF(input_dim = 2, variance=variance, lengthscale=lengthscale)\n",
    "    cov = simulation_kernel.K(X_D)\n",
    "    X, Y = [], []\n",
    "    for _ in range(exploration_steps):\n",
    "        Y_D = np.random.multivariate_normal(np.array(means), cov, 1)[0]\n",
    "        Y_D = np.reshape(Y_D, (len(Y_D),1))\n",
    "        Y_D[Y_D<0] = 0.1\n",
    "        sparse_nodes = random.sample(range(0, graph.number_of_nodes()), nb_agents)\n",
    "        sorted_nodes = sorted(sparse_nodes)\n",
    "        if sorted_nodes[0]==0:\n",
    "            for i in range(1,len(sorted_nodes)):\n",
    "                if sorted_nodes[i]!=sorted_nodes[i-1]:\n",
    "                    l = covariances[sorted_nodes[i]-1]\n",
    "                    l[0], l[1], l[2], l[3] = weighted_covariance(l[0], l[1], l[2], l[3], Y_D[0], Y_D[sorted_nodes[i]], memory_window, gamma)\n",
    "        X.append(X_D[sparse_nodes])\n",
    "        Y.append(Y_D[sparse_nodes])\n",
    "    return incertitude_initiale(cov, graph), np.array(X), np.array(Y)\n",
    "\n",
    "def hyperparameters_estimator(X, Y, gp_model, graph, graph_dim, memory_window, gamma):\n",
    "    for i in range(len(X)):\n",
    "        l = variances[coord_to_id(X[i], graph_dim)]\n",
    "        l[0], l[1], l[2] = weighted_variance(l[0], l[1], l[2], Y[i][0], memory_window, gamma)\n",
    "    cumul_var = 0\n",
    "    coeffs = 0\n",
    "    for ele in variances:\n",
    "        coeffs += ele[2]\n",
    "        cumul_var += float(ele[0])*ele[2]\n",
    "    variance = cumul_var/coeffs\n",
    "    ###########################################\n",
    "    X_v = np.array([graph.nodes[i][\"coord\"] for i in range(graph.number_of_nodes())])\n",
    "    moyenne = np.reshape(gp_model.predict(X_v)[0], (graph.number_of_nodes(),))\n",
    "    ###########################################\n",
    "    lengthscale, cpt = 0, 0\n",
    "    for i in range(len(covariances)):\n",
    "        if covariances[i][0]>1:\n",
    "            covariance = covariances[i][1] - covariances[i][2]*covariances[i][3]\n",
    "            if 0<covariance<variance:\n",
    "                cpt += covariances[i][0]\n",
    "                lengthscale += find_lengthscale(X_v[0], X_v[i+1], covariance, variance) * covariances[i][0]\n",
    "    if cpt!=0:\n",
    "        lengthscale /= cpt\n",
    "    else:\n",
    "        lengthscale = 0\n",
    "    return moyenne, variance, lengthscale\n",
    "\n",
    "def find_lengthscale(x1, x2, y, var):\n",
    "    return distance(x1, x2) * np.sqrt(-0.5/np.log(y/var))\n",
    "\n",
    "def weighted_covariance(n, moy_prod, moy_x, moy_y, x_new, y_new, memory_capacity, gamma):\n",
    "    if n>memory_capacity:\n",
    "        return n + 1, gamma * moy_prod + (1-gamma) * x_new * y_new, gamma * moy_x + (1-gamma) * x_new, gamma * moy_y + (1-gamma) * y_new\n",
    "    return n + 1, (n * moy_prod + x_new * y_new)/(n + 1), (n * moy_x + x_new)/(n + 1), (n * moy_y + y_new)/(n + 1)\n",
    "\n",
    "def weighted_variance(var, moy, n, x_new, memory_capacity, gamma):\n",
    "    if n>memory_capacity:\n",
    "        a = gamma*(var+(moy**2)) + (1-gamma)*(x_new**2)\n",
    "        moy = gamma * moy + (1-gamma) * x_new\n",
    "    else:\n",
    "        a = (n*(var+(moy**2)) + (x_new**2))/(n+1)\n",
    "        moy = (n*moy+x_new)/(n+1)\n",
    "    return a-moy**2, moy, n+1\n",
    "\n",
    "def in_collision(env, agent):\n",
    "    for i in range(env.nb_agents):\n",
    "        if i!=agent and env.paths[i][-1]==env.paths[agent][-1]:\n",
    "            return True\n",
    "    return False\n",
    "    \n",
    "def nb_agents_in_collision(env):\n",
    "    cpt = 0\n",
    "    for agent in range(env.nb_agents):\n",
    "        if in_collision(env, agent):\n",
    "            cpt += 1\n",
    "    return cpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60e90449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance:  6.308772713397341\n",
      "Lengthscale:  [1.66076309]\n",
      "Carte:  [59.85483899 32.11779084 18.48143476 13.82936597  9.90567897 31.42864025\n",
      " 23.56361743 21.16534224 17.11838639  9.15379147 18.64726466 22.34266819\n",
      " 24.11017583 19.78071109 10.78276591 12.74158079 17.33884851 18.80636175\n",
      " 16.94169911 12.14119882  7.33509245  9.70290213 11.42148126 13.4479377\n",
      " 13.23346071]\n",
      "Incertitude initiale :  72.33041096055982\n"
     ]
    }
   ],
   "source": [
    "graph_dim = (5, 5)\n",
    "graph_unity = 1\n",
    "graph = construct_graph(graph_dim, graph_unity)\n",
    "X_v = np.array([graph.nodes[i][\"coord\"] for i in range(graph.number_of_nodes())])\n",
    "means = [60, 30, 20, 15, 10, 30, 27.5, 25, 17.5, 11.25, 20, 25, 22.5, 20, 12.5, 15, 17.5, 20, 17.5, 15, 10, 11.25, 12.5, 15, 12.5]\n",
    "nb_agents = 4\n",
    "exploration_steps = 25\n",
    "variance_lengthscale = [10, 2]\n",
    "memory_window = 1000\n",
    "gamma = 0.997\n",
    "\n",
    "variances = [[0, 0, 0] for _ in range(graph.number_of_nodes())]\n",
    "covariances = [[0, 0, 0, 0] for _ in range(graph.number_of_nodes()-1)]\n",
    "\n",
    "incertitude, x, y = collect_pilot_data(nb_agents, exploration_steps, graph, graph_dim, means, variance_lengthscale[0], variance_lengthscale[1], memory_window, gamma)\n",
    "\n",
    "x = np.reshape(x, (nb_agents*exploration_steps,2))\n",
    "y = np.reshape(y, (nb_agents*exploration_steps,1))\n",
    "\n",
    "gp_model = GPy.models.GPRegression(x, y, GPy.kern.RBF(2))\n",
    "gp_model.optimize(messages=False)\n",
    "\n",
    "moyenne, var, l = hyperparameters_estimator(x, y, gp_model, graph, graph_dim, memory_window, gamma)\n",
    "print(\"Variance: \", var)\n",
    "print(\"Lengthscale: \", l)\n",
    "print(\"Carte: \", moyenne)\n",
    "print(\"Incertitude initiale : \", incertitude_initiale(sigma_matrix(gp_model, X_v), graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74bb46a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f017e26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a95bbca0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "542d999c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_gp_model(gp_model, x, y, graph, graph_dim, memory_window, gamma, display_gp_model=False):\n",
    "    moy, variance, lengthscale = hyperparameters_estimator(x, y, gp_model, graph, graph_dim, memory_window, gamma)\n",
    "    X = np.concatenate((gp_model.X, x))[-1000:]\n",
    "    Y = np.concatenate((gp_model.Y, y))[-1000:]\n",
    "    gp_model.set_XY(X, Y)\n",
    "    gp_model.optimize(messages=False)\n",
    "    if display_gp_model:\n",
    "        display(gp_model)\n",
    "    return gp_model, moy, variance, lengthscale\n",
    "\n",
    "def take_measurements(nodes, means, X_v, memory_window, gamma):\n",
    "    simulation_kernel = GPy.kern.RBF(input_dim = 2, variance=variance_lengthscale[0], lengthscale=variance_lengthscale[1])\n",
    "    cov = simulation_kernel.K(X_v)\n",
    "    Y_v = np.random.multivariate_normal(np.array(means), cov, 1)[0]\n",
    "    Y_v = np.reshape(Y_v, (len(Y_v),1))\n",
    "    Y_v[Y_v<0] = 0.1\n",
    "    sorted_nodes = sorted(nodes)\n",
    "    if sorted_nodes[0]==0:\n",
    "        for i in range(1,len(sorted_nodes)):\n",
    "            if sorted_nodes[i]!=sorted_nodes[i-1]:\n",
    "                l = covariances[sorted_nodes[i]-1]\n",
    "                l[0], l[1], l[2], l[3] = weighted_covariance(l[0], l[1], l[2], l[3], Y_v[0], Y_v[sorted_nodes[i]], memory_window, gamma)\n",
    "    return X_v[nodes], Y_v[nodes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b643543",
   "metadata": {},
   "outputs": [],
   "source": [
    "class pfeEnv(py_environment.PyEnvironment):\n",
    "\n",
    "    def __init__(self, graph, gp_model, nb_agents, budgets, graph_unity, graph_dim, initial_positions, variance_0, lengthscale_0, memory_window, gamma, train_env=True):\n",
    "        self.nb_agents = nb_agents\n",
    "        self.graph = graph\n",
    "        self.graph_unity = graph_unity\n",
    "        self.graph_dim = graph_dim\n",
    "        self.initial_budgets = budgets/self.graph_unity\n",
    "        self.budgets = self.initial_budgets.copy()\n",
    "        if initial_positions!=None:\n",
    "            self.initial_positions = copie_paths(initial_positions)\n",
    "            self.paths = copie_paths(initial_positions)\n",
    "        else:\n",
    "            self.initial_positions = None\n",
    "            self.paths = [[random.randint(0, self.graph.number_of_nodes()-1)] for i in range(self.nb_agents)]\n",
    "        self.neighbors = [sorted(list(self.graph.neighbors(self.current_node(i)))) for i in range(self.nb_agents)]\n",
    "        self.formater()\n",
    "        self.X_v = np.array([self.graph.nodes[i][\"coord\"] for i in range(self.graph.number_of_nodes())])\n",
    "        self.gp_model = gp_model\n",
    "        self.X_s = [[] for i in range(self.nb_agents)]\n",
    "        self.dones = [False for i in range(self.nb_agents)]\n",
    "        self.cpt_done = 0\n",
    "        self._action_spec = array_spec.BoundedArraySpec(shape=(), dtype=np.int32, minimum=0, maximum=3, name=\"action\")\n",
    "        self._observation_spec = array_spec.BoundedArraySpec(shape=(self.nb_agents*2,), dtype=np.float32, minimum=np.zeros((self.nb_agents*2,)), name=\"observation\")\n",
    "        self._state = flatten([self.graph.nodes[self.current_node(i)][\"coord\"] for i in range(self.nb_agents)])\n",
    "        self._episode_ended = False\n",
    "        #################################\n",
    "        self.cpt_update_gp_model = 0\n",
    "        self.memory_window, self.gamma = memory_window, gamma\n",
    "        self.x, self.y = np.array([]), np.array([])\n",
    "        self.liste_variances, self.liste_lengthscales = [variance_0], [lengthscale_0]\n",
    "        self.train_env = train_env\n",
    "        self.agents_in_collision = []\n",
    "        \n",
    "    def current_node(self, id_agent):\n",
    "        return self.paths[id_agent][-1]\n",
    "\n",
    "    def action_spec(self):\n",
    "        return self._action_spec\n",
    "\n",
    "    def observation_spec(self):\n",
    "        return self._observation_spec\n",
    "    \n",
    "    def formater(self):\n",
    "        for i in range(len(self.neighbors)):\n",
    "            liste = self.neighbors[i]\n",
    "            node = self.current_node(i)\n",
    "            try:\n",
    "                if liste[0]>=node-1:\n",
    "                    liste.insert(0,-1)\n",
    "                if liste[1]!=node-1:\n",
    "                    liste.insert(1,-1)\n",
    "                if liste[2]!=node+1:\n",
    "                    liste.insert(2,-1)\n",
    "            except:\n",
    "                pass\n",
    "            for _ in range(4-len(liste)):\n",
    "                liste.append(-1)\n",
    "\n",
    "    def _reset(self):\n",
    "        if self.initial_positions!=None:\n",
    "            self.paths = copie_paths(self.initial_positions)\n",
    "        else:\n",
    "            self.paths = [[random.randint(0, self.graph.number_of_nodes()-1)] for i in range(self.nb_agents)]\n",
    "        self._state = flatten([self.graph.nodes[self.current_node(i)][\"coord\"] for i in range(self.nb_agents)])\n",
    "        self.neighbors = [sorted(list(self.graph.neighbors(self.current_node(i)))) for i in range(self.nb_agents)] \n",
    "        self.formater()\n",
    "        self.budgets = self.initial_budgets.copy()\n",
    "        self.X_s = [[] for i in range(self.nb_agents)]\n",
    "        self.dones = [False for i in range(self.nb_agents)]\n",
    "        self.cpt_done = 0\n",
    "        self._episode_ended = False\n",
    "        return ts.restart(np.array(self._state, dtype=np.float32))\n",
    "        \n",
    "    def _step(self, abstract_actions):\n",
    "        if self._episode_ended:\n",
    "            return self.reset()\n",
    "        actions = []\n",
    "        for i in range(self.nb_agents):\n",
    "            actions.append(self.neighbors[i][abstract_actions[i]])\n",
    "        X_s_old = preprocessing(self.X_s)\n",
    "        for i in range(self.nb_agents):\n",
    "            if not self.dones[i]:\n",
    "                if actions[i]!=-1:\n",
    "                    self.X_s[i].append([graph.nodes[actions[i]][\"coord\"][0], graph.nodes[actions[i]][\"coord\"][1]])\n",
    "                    self.budgets[i] -= 1\n",
    "                else:\n",
    "                    actions[i] = self.current_node(i)\n",
    "                    self.budgets[i] -= 0.3\n",
    "                if self.budgets[i]<1:\n",
    "                    self.dones[i] = True\n",
    "                    self.cpt_done += 1\n",
    "                self.paths[i].append(actions[i])\n",
    "                coord = self.graph.nodes[self.current_node(i)][\"coord\"]\n",
    "                self._state[2*i] = coord[0]\n",
    "                self._state[2*i+1] = coord[1]\n",
    "        \n",
    "        if self.train_env:\n",
    "            positions, mesures = take_measurements(actions, means, self.X_v, self.memory_window, self.gamma)\n",
    "            self.x = concatenate(self.x, positions)\n",
    "            self.y = concatenate(self.y, mesures)\n",
    "            self.cpt_update_gp_model = (self.cpt_update_gp_model + 1)%100\n",
    "            if self.cpt_update_gp_model==0:\n",
    "                self.gp_model, m, var, l = update_gp_model(self.gp_model, self.x, self.y, self.graph, self.graph_dim, self.memory_window, self.gamma)\n",
    "                self.liste_variances.append(var)\n",
    "                self.liste_lengthscales.append(l)\n",
    "                self.moy = m\n",
    "                self.x, self.y = np.array([]), np.array([])\n",
    "        \n",
    "        self.neighbors = [sorted(list(self.graph.neighbors(self.current_node(i)))) for i in range(self.nb_agents)]\n",
    "        self.formater()\n",
    "        reward = reward_function(self.gp_model, self.X_v, X_s_old, preprocessing(self.X_s))\n",
    "        nb = nb_agents_in_collision(self)\n",
    "        self.agents_in_collision.append(nb)\n",
    "        #reward -= (reward * nb * 0.25)\n",
    "        if self.cpt_done==self.nb_agents:\n",
    "            self._episode_ended = True\n",
    "            return ts.termination(np.array(self._state, dtype=np.float32), reward)\n",
    "        return ts.transition(np.array(self._state, dtype=np.float32), reward=reward, discount=0.97)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6baa6a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_iterations = 60000\n",
    "fc_layer_params = (128,64,)\n",
    "replay_buffer_capacity = 30000\n",
    "batch_size = 128\n",
    "learning_rate = 1e-5\n",
    "\n",
    "num_atoms = 51\n",
    "min_q_value = -20\n",
    "max_q_value = 20\n",
    "n_step_update = 3\n",
    "\n",
    "num_eval_episodes = 10\n",
    "log_eval_interval = 1000\n",
    "\n",
    "max_episode_length = 25\n",
    "budgets = 25 * np.ones(nb_agents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d93bc57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_positions = None #[[random.randint(0, graph.number_of_nodes()-1)] for i in range(nb_agents)]\n",
    "train_py_env = wrappers.TimeLimit(pfeEnv(graph, gp_model, nb_agents, budgets, graph_unity, graph_dim, initial_positions, var, l, memory_window, gamma, train_env=True), duration=max_episode_length)\n",
    "eval_py_env = wrappers.TimeLimit(pfeEnv(graph, gp_model, nb_agents, budgets, graph_unity, graph_dim, initial_positions, var, l, memory_window, gamma, train_env=False), duration=max_episode_length)\n",
    "\n",
    "train_env = tf_py_environment.TFPyEnvironment(train_py_env)\n",
    "eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)\n",
    "\n",
    "agents_tf = []\n",
    "eval_policies = []\n",
    "collect_policies = []\n",
    "policies_checkpointers = []\n",
    "\n",
    "for i in range(nb_agents):\n",
    "    categorical_q_net = categorical_q_network.CategoricalQNetwork(\n",
    "        train_env.observation_spec(),\n",
    "        train_env.action_spec(),\n",
    "        num_atoms=num_atoms,\n",
    "        fc_layer_params=fc_layer_params)\n",
    "        \n",
    "    optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "\n",
    "    train_step_counter = tf.compat.v2.Variable(0)\n",
    "\n",
    "    agents_tf.append(categorical_dqn_agent.CategoricalDqnAgent(\n",
    "        train_env.time_step_spec(),\n",
    "        train_env.action_spec(),\n",
    "        categorical_q_network=categorical_q_net,\n",
    "        optimizer=optimizer,\n",
    "        min_q_value=min_q_value,\n",
    "        max_q_value=max_q_value,\n",
    "        n_step_update=n_step_update,\n",
    "        target_update_period=5,\n",
    "        target_update_tau=0.05,\n",
    "        td_errors_loss_fn=common.element_wise_squared_loss,\n",
    "        gamma=0.99,\n",
    "        train_step_counter=train_step_counter))\n",
    "\n",
    "    agents_tf[-1].initialize()\n",
    "\n",
    "    eval_policies.append(agents_tf[-1].policy)\n",
    "    collect_policies.append(agents_tf[-1].collect_policy)\n",
    "    \n",
    "    policies_checkpointers.append(common.Checkpointer(ckpt_dir='Collisions_Politique_'+str(i), policy=eval_policies[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "562a871b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_avg_return(environment, policies, num_episodes=10):\n",
    "    total_return = 0.0\n",
    "    for _ in range(num_episodes):\n",
    "        time_step = environment.reset()\n",
    "        episode_return = 0.0\n",
    "        while not time_step.is_last():\n",
    "            actions = []\n",
    "            for i in range(nb_agents):\n",
    "                actions.append(policies[i].action(time_step).action.numpy()[0])\n",
    "            actions = tf.convert_to_tensor([actions])\n",
    "            time_step = environment.step(actions)\n",
    "            episode_return += time_step.reward\n",
    "        total_return += episode_return\n",
    "    avg_return = total_return / num_episodes\n",
    "    return avg_return.numpy()[0]\n",
    "\n",
    "def collect_step(environment, environment_py, policies):\n",
    "    time_step = environment.current_time_step()\n",
    "    actions = []\n",
    "    actions_step = []\n",
    "    dones = environment_py.dones.copy()\n",
    "    for i in range(nb_agents):\n",
    "        actions_step.append(policies[i].action(time_step))\n",
    "        actions.append(actions_step[-1].action.numpy()[0])\n",
    "    actions = tf.convert_to_tensor([actions])\n",
    "    next_time_step = environment.step(actions)\n",
    "    rewards = credit_assignment(environment_py, next_time_step.reward.numpy()[0], dones)\n",
    "    for i in range(nb_agents):\n",
    "        if not dones[i]:\n",
    "            nts = next_time_step._replace(reward=tf.convert_to_tensor([rewards[i]], dtype=np.float32))\n",
    "            traj = trajectory.from_transition(time_step, actions_step[i], nts)\n",
    "            replay_buffers[i].add_batch(traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ea3fe65",
   "metadata": {},
   "outputs": [],
   "source": [
    "replay_buffers = []\n",
    "for i in range(nb_agents):\n",
    "    replay_buffers.append(tf_agents.replay_buffers.TFUniformReplayBuffer(\n",
    "        data_spec = agents_tf[i].collect_data_spec,\n",
    "        batch_size = train_env.batch_size,\n",
    "        max_length = replay_buffer_capacity))\n",
    "\n",
    "iterators = []\n",
    "for i in range(nb_agents):\n",
    "    dataset = replay_buffers[i].as_dataset(\n",
    "        num_parallel_calls=3,\n",
    "        sample_batch_size=batch_size,\n",
    "        num_steps=2).prefetch(3)\n",
    "\n",
    "    iterators.append(iter(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1bcd59fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.539692\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 500/500 [00:20<00:00, 24.50it/s]\n",
      "  0%|                                                                                        | 0/60000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\hm_as\\anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1082: calling foldr_v2 (from tensorflow.python.ops.functional_ops) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.foldr(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.foldr(fn, elems))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\hm_as\\anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1082: calling foldr_v2 (from tensorflow.python.ops.functional_ops) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.foldr(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.foldr(fn, elems))\n",
      "  2%|█▎                                                                         | 1003/60000 [01:05<5:31:45,  2.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 1000: Average Return = 24.86440658569336\n",
      "Variance :  9.8357937075869\n",
      "Lengthscale :  [1.95504867]\n",
      "MSE de la moyenne :  1.0260209733981598\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|██▍                                                                       | 2003/60000 [02:25<14:39:01,  1.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 2000: Average Return = 26.90337562561035\n",
      "Variance :  9.934669687885005\n",
      "Lengthscale :  [1.98326516]\n",
      "MSE de la moyenne :  0.41413848019025457\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|███▊                                                                       | 3003/60000 [03:40<9:07:57,  1.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 3000: Average Return = 38.71929168701172\n",
      "Variance :  10.035791933358063\n",
      "Lengthscale :  [1.9986679]\n",
      "MSE de la moyenne :  0.41788720900325765\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|█████                                                                      | 4003/60000 [04:55<8:14:10,  1.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 4000: Average Return = 42.70213317871094\n",
      "Variance :  9.962110485223723\n",
      "Lengthscale :  [1.97238778]\n",
      "MSE de la moyenne :  0.4663025017173844\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|██████▎                                                                    | 5002/60000 [06:07<8:30:25,  1.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 5000: Average Return = 33.3515625\n",
      "Variance :  9.942394468905707\n",
      "Lengthscale :  [1.96460125]\n",
      "MSE de la moyenne :  0.31418021053897566\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 10%|███████▌                                                                   | 6003/60000 [07:23<8:30:39,  1.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 6000: Average Return = 39.026947021484375\n",
      "Variance :  9.84023132858811\n",
      "Lengthscale :  [1.96068071]\n",
      "MSE de la moyenne :  0.4457796883514067\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|████████▊                                                                  | 7003/60000 [08:40<8:42:07,  1.69it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 7000: Average Return = 39.14008331298828\n",
      "Variance :  9.809043949100936\n",
      "Lengthscale :  [1.99760381]\n",
      "MSE de la moyenne :  0.35476618907749546\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 13%|██████████                                                                 | 8003/60000 [09:54<6:46:47,  2.13it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 8000: Average Return = 34.68074035644531\n",
      "Variance :  9.953876181423816\n",
      "Lengthscale :  [1.9636328]\n",
      "MSE de la moyenne :  0.214170403727475\n"
     ]
    },
    {
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      " 15%|███████████▎                                                               | 9003/60000 [11:11<8:03:43,  1.76it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 9000: Average Return = 39.214439392089844\n",
      "Variance :  10.072926759022886\n",
      "Lengthscale :  [1.97616578]\n",
      "MSE de la moyenne :  0.2503572056608944\n"
     ]
    },
    {
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      " 17%|████████████▎                                                             | 10002/60000 [12:24<7:27:12,  1.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 10000: Average Return = 40.80994415283203\n",
      "Variance :  9.961158268231765\n",
      "Lengthscale :  [2.0198376]\n",
      "MSE de la moyenne :  0.3665709889162161\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 18%|█████████████▌                                                            | 11004/60000 [13:38<5:31:08,  2.47it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 11000: Average Return = 39.79048538208008\n",
      "Variance :  10.0928975360422\n",
      "Lengthscale :  [1.98727507]\n",
      "MSE de la moyenne :  0.3883652137714248\n"
     ]
    },
    {
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     "output_type": "stream",
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      " 20%|██████████████▊                                                           | 12003/60000 [14:52<7:46:57,  1.71it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 12000: Average Return = 37.00612258911133\n",
      "Variance :  10.044872646728345\n",
      "Lengthscale :  [1.97525767]\n",
      "MSE de la moyenne :  0.5129842915816724\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 22%|████████████████                                                          | 13002/60000 [16:11<7:05:42,  1.84it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 13000: Average Return = 41.26437759399414\n",
      "Variance :  9.895725787736271\n",
      "Lengthscale :  [1.9697364]\n",
      "MSE de la moyenne :  0.5211810803108017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|█████████████████▎                                                        | 14003/60000 [17:25<7:03:56,  1.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 14000: Average Return = 35.30743408203125\n",
      "Variance :  9.93250335239761\n",
      "Lengthscale :  [1.99097549]\n",
      "MSE de la moyenne :  0.3303240955643546\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██████████████████▌                                                       | 15003/60000 [18:40<7:24:57,  1.69it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 15000: Average Return = 40.269798278808594\n",
      "Variance :  9.975225200152785\n",
      "Lengthscale :  [1.98414682]\n",
      "MSE de la moyenne :  0.2581240965626421\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|███████████████████▋                                                      | 16003/60000 [19:55<6:54:00,  1.77it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 16000: Average Return = 40.057044982910156\n",
      "Variance :  10.028204294411493\n",
      "Lengthscale :  [1.97643661]\n",
      "MSE de la moyenne :  0.3453569806227648\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 28%|████████████████████▉                                                     | 17003/60000 [21:12<5:36:57,  2.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 17000: Average Return = 40.54528045654297\n",
      "Variance :  9.885090432461647\n",
      "Lengthscale :  [1.97716508]\n",
      "MSE de la moyenne :  0.35038234480655883\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|██████████████████████▏                                                   | 18004/60000 [22:27<4:51:38,  2.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 18000: Average Return = 38.531070709228516\n",
      "Variance :  9.917256579910465\n",
      "Lengthscale :  [1.98557837]\n",
      "MSE de la moyenne :  0.3003471639374834\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|███████████████████████▍                                                  | 19003/60000 [23:41<5:43:58,  1.99it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 19000: Average Return = 41.56684112548828\n",
      "Variance :  10.124683382989124\n",
      "Lengthscale :  [1.94772783]\n",
      "MSE de la moyenne :  0.40416780490004384\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|████████████████████████▋                                                 | 20003/60000 [24:58<6:26:53,  1.72it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 20000: Average Return = 41.90004348754883\n",
      "Variance :  10.152731688423396\n",
      "Lengthscale :  [1.95588186]\n",
      "MSE de la moyenne :  0.4540331764841786\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
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     "output_type": "stream",
     "text": [
      "step = 21000: Average Return = 42.819297790527344\n",
      "Variance :  10.134007537311193\n",
      "Lengthscale :  [1.98548598]\n",
      "MSE de la moyenne :  0.3357448064295874\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "step = 22000: Average Return = 38.40540313720703\n",
      "Variance :  10.233881286823397\n",
      "Lengthscale :  [1.97174189]\n",
      "MSE de la moyenne :  0.32711209468510516\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 38%|████████████████████████████▎                                             | 23002/60000 [28:41<6:04:53,  1.69it/s]"
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    },
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     "text": [
      "step = 23000: Average Return = 42.44709396362305\n",
      "Variance :  10.123672632279185\n",
      "Lengthscale :  [1.98900919]\n",
      "MSE de la moyenne :  0.48712956856753065\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 40%|█████████████████████████████▌                                            | 24003/60000 [29:58<6:02:48,  1.65it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 24000: Average Return = 42.19451141357422\n",
      "Variance :  9.830253327969183\n",
      "Lengthscale :  [2.00818597]\n",
      "MSE de la moyenne :  0.26466808577065637\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|██████████████████████████████▊                                           | 25002/60000 [31:16<5:28:48,  1.77it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 25000: Average Return = 43.55809783935547\n",
      "Variance :  9.688865359687039\n",
      "Lengthscale :  [2.00720122]\n",
      "MSE de la moyenne :  0.4710473800360189\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|████████████████████████████████                                          | 26004/60000 [32:37<3:30:02,  2.70it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 26000: Average Return = 39.89484405517578\n",
      "Variance :  9.80256733934881\n",
      "Lengthscale :  [2.00751494]\n",
      "MSE de la moyenne :  0.19902691401189046\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 45%|█████████████████████████████████▎                                        | 27003/60000 [33:52<5:07:48,  1.79it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 27000: Average Return = 39.67634963989258\n",
      "Variance :  9.783161176677291\n",
      "Lengthscale :  [2.00743555]\n",
      "MSE de la moyenne :  0.24421595162933898\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|██████████████████████████████████▌                                       | 28002/60000 [35:09<6:14:00,  1.43it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 28000: Average Return = 44.97947311401367\n",
      "Variance :  9.927602198969383\n",
      "Lengthscale :  [1.9841665]\n",
      "MSE de la moyenne :  0.33987477720397175\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 48%|███████████████████████████████████▊                                      | 29002/60000 [36:22<4:42:47,  1.83it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 29000: Average Return = 42.75503158569336\n",
      "Variance :  9.938262697188424\n",
      "Lengthscale :  [1.97577352]\n",
      "MSE de la moyenne :  0.3912771283173497\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████████████████████████████████████                                     | 30003/60000 [37:36<4:31:09,  1.84it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 30000: Average Return = 39.5245475769043\n",
      "Variance :  10.01502303601163\n",
      "Lengthscale :  [1.98034241]\n",
      "MSE de la moyenne :  0.21209562608587715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|██████████████████████████████████████▏                                   | 31003/60000 [38:50<4:37:32,  1.74it/s]"
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     "output_type": "stream",
     "text": [
      "step = 31000: Average Return = 42.1297492980957\n",
      "Variance :  9.709631677942944\n",
      "Lengthscale :  [2.0078704]\n",
      "MSE de la moyenne :  0.23117200923913792\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|███████████████████████████████████████▍                                  | 32002/60000 [40:04<4:26:27,  1.75it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 32000: Average Return = 43.19251251220703\n",
      "Variance :  9.676282828420174\n",
      "Lengthscale :  [2.02049047]\n",
      "MSE de la moyenne :  0.2092564372489304\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|████████████████████████████████████████▋                                 | 33003/60000 [41:19<4:16:51,  1.75it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 33000: Average Return = 39.476768493652344\n",
      "Variance :  9.700248192458204\n",
      "Lengthscale :  [1.98276663]\n",
      "MSE de la moyenne :  0.37936253149744154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 57%|█████████████████████████████████████████▉                                | 34003/60000 [42:32<3:45:14,  1.92it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 34000: Average Return = 39.223876953125\n",
      "Variance :  9.81592737434238\n",
      "Lengthscale :  [1.97146347]\n",
      "MSE de la moyenne :  0.3272211437963965\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|███████████████████████████████████████████▏                              | 35004/60000 [43:49<3:28:15,  2.00it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 35000: Average Return = 40.25791549682617\n",
      "Variance :  9.895877846826469\n",
      "Lengthscale :  [1.95918606]\n",
      "MSE de la moyenne :  0.282638310985523\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|████████████████████████████████████████████▍                             | 36001/60000 [45:02<5:18:08,  1.26it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 36000: Average Return = 40.34299850463867\n",
      "Variance :  9.844395722132244\n",
      "Lengthscale :  [1.99552676]\n",
      "MSE de la moyenne :  0.25813438998644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|█████████████████████████████████████████████▋                            | 37003/60000 [46:17<3:04:52,  2.07it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 37000: Average Return = 37.390159606933594\n",
      "Variance :  9.974390251040765\n",
      "Lengthscale :  [1.99198616]\n",
      "MSE de la moyenne :  0.3303258064882598\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|██████████████████████████████████████████████▊                           | 38002/60000 [47:33<3:44:56,  1.63it/s]"
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    },
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     "output_type": "stream",
     "text": [
      "step = 38000: Average Return = 40.578033447265625\n",
      "Variance :  9.975981202410487\n",
      "Lengthscale :  [1.97455417]\n",
      "MSE de la moyenne :  0.42705876381803265\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 65%|████████████████████████████████████████████████                          | 39003/60000 [48:48<2:59:58,  1.94it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "step = 39000: Average Return = 36.36102294921875\n",
      "Variance :  9.952459483793954\n",
      "Lengthscale :  [1.97430763]\n",
      "MSE de la moyenne :  0.369547463338637\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|█████████████████████████████████████████████████▎                        | 40002/60000 [50:04<3:06:40,  1.79it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 40000: Average Return = 45.709373474121094\n",
      "Variance :  9.841753552416927\n",
      "Lengthscale :  [1.99077063]\n",
      "MSE de la moyenne :  0.35219539979683995\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████████████████████████████████████████████████▌                       | 41004/60000 [51:22<2:33:28,  2.06it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 41000: Average Return = 36.77003479003906\n",
      "Variance :  9.858988965540812\n",
      "Lengthscale :  [1.98599404]\n",
      "MSE de la moyenne :  0.46362847228434617\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████████████████████████████████████████████████▊                      | 42003/60000 [52:35<2:49:30,  1.77it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 42000: Average Return = 39.15499496459961\n",
      "Variance :  9.72322820794614\n",
      "Lengthscale :  [1.97255769]\n",
      "MSE de la moyenne :  0.3224509230840621\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|█████████████████████████████████████████████████████                     | 43002/60000 [53:52<2:32:22,  1.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 43000: Average Return = 38.36793899536133\n",
      "Variance :  9.747820414728158\n",
      "Lengthscale :  [1.96954062]\n",
      "MSE de la moyenne :  0.22160443635940466\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|██████████████████████████████████████████████████████▎                   | 44003/60000 [55:06<2:33:38,  1.74it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 44000: Average Return = 42.951663970947266\n",
      "Variance :  9.916731570463543\n",
      "Lengthscale :  [1.96903578]\n",
      "MSE de la moyenne :  0.3698465912554559\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|███████████████████████████████████████████████████████▌                  | 45004/60000 [56:21<2:06:54,  1.97it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 45000: Average Return = 41.15504455566406\n",
      "Variance :  10.040360332400986\n",
      "Lengthscale :  [1.96233425]\n",
      "MSE de la moyenne :  0.3191701473118642\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|████████████████████████████████████████████████████████▋                 | 46002/60000 [57:36<2:18:13,  1.69it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 46000: Average Return = 36.889835357666016\n",
      "Variance :  9.869962489094847\n",
      "Lengthscale :  [1.99324993]\n",
      "MSE de la moyenne :  0.1820361251796479\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|█████████████████████████████████████████████████████████▉                | 47004/60000 [58:50<1:51:47,  1.94it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 47000: Average Return = 40.838897705078125\n",
      "Variance :  9.773140980806682\n",
      "Lengthscale :  [2.00240905]\n",
      "MSE de la moyenne :  0.3252851519888069\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|█████████████████████████████████████████████████████████▌              | 48003/60000 [1:00:05<1:56:57,  1.71it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 48000: Average Return = 44.30256652832031\n",
      "Variance :  9.78542223378439\n",
      "Lengthscale :  [1.99640233]\n",
      "MSE de la moyenne :  0.21727790339006245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|██████████████████████████████████████████████████████████▊             | 49002/60000 [1:01:17<1:46:37,  1.72it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 49000: Average Return = 39.457054138183594\n",
      "Variance :  9.98543360436323\n",
      "Lengthscale :  [1.96665344]\n",
      "MSE de la moyenne :  0.2860278281057821\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████████████████████████████████████████████████████████            | 50003/60000 [1:02:32<1:34:22,  1.77it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 50000: Average Return = 40.653892517089844\n",
      "Variance :  9.886905779470208\n",
      "Lengthscale :  [1.96989797]\n",
      "MSE de la moyenne :  0.2892982459306868\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|█████████████████████████████████████████████████████████████▏          | 51002/60000 [1:03:47<1:27:15,  1.72it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 51000: Average Return = 38.44258117675781\n",
      "Variance :  9.918416645693723\n",
      "Lengthscale :  [1.98561422]\n",
      "MSE de la moyenne :  0.20563388581677572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|██████████████████████████████████████████████████████████████▍         | 52002/60000 [1:05:00<1:14:48,  1.78it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 52000: Average Return = 38.18368911743164\n",
      "Variance :  9.936685095676708\n",
      "Lengthscale :  [1.96776185]\n",
      "MSE de la moyenne :  0.14897774904010702\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|███████████████████████████████████████████████████████████████▌        | 53002/60000 [1:06:14<1:03:17,  1.84it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 53000: Average Return = 40.53230285644531\n",
      "Variance :  9.859083875383897\n",
      "Lengthscale :  [1.99066604]\n",
      "MSE de la moyenne :  0.2666259867979771\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|████████████████████████████████████████████████████████████████▊       | 54003/60000 [1:07:30<1:05:22,  1.53it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 54000: Average Return = 42.15492630004883\n",
      "Variance :  10.094756523265493\n",
      "Lengthscale :  [1.95968772]\n",
      "MSE de la moyenne :  0.2709124837073134\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|███████████████████████████████████████████████████████████████████▊      | 55003/60000 [1:08:43<41:32,  2.00it/s]"
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     "output_type": "stream",
     "text": [
      "step = 55000: Average Return = 43.62751007080078\n",
      "Variance :  9.9149011194759\n",
      "Lengthscale :  [1.97171141]\n",
      "MSE de la moyenne :  0.25660515035864423\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████████████████████████████████████████████████████████████████     | 56002/60000 [1:09:57<39:50,  1.67it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "step = 56000: Average Return = 37.47145080566406\n",
      "Variance :  9.992333761192697\n",
      "Lengthscale :  [1.95430268]\n",
      "MSE de la moyenne :  0.36658211117658224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|██████████████████████████████████████████████████████████████████████▎   | 57003/60000 [1:11:10<25:29,  1.96it/s]"
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     "output_type": "stream",
     "text": [
      "step = 57000: Average Return = 40.317588806152344\n",
      "Variance :  10.04694730616389\n",
      "Lengthscale :  [1.95372055]\n",
      "MSE de la moyenne :  0.44572671328261093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|███████████████████████████████████████████████████████████████████████▌  | 58003/60000 [1:12:27<18:50,  1.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 58000: Average Return = 37.698219299316406\n",
      "Variance :  10.124205179461299\n",
      "Lengthscale :  [1.95451942]\n",
      "MSE de la moyenne :  0.2865877711307913\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|████████████████████████████████████████████████████████████████████████▊ | 59002/60000 [1:13:43<10:06,  1.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 59000: Average Return = 37.24289321899414\n",
      "Variance :  10.012851334853318\n",
      "Lengthscale :  [1.97157059]\n",
      "MSE de la moyenne :  0.32515963531649705\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 60000/60000 [1:15:01<00:00, 13.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 60000: Average Return = 40.79149627685547\n",
      "Variance :  9.920798526137286\n",
      "Lengthscale :  [1.99052209]\n",
      "MSE de la moyenne :  0.3875696584711295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(compute_avg_return(eval_env, eval_policies, num_eval_episodes))\n",
    "\n",
    "steps = []\n",
    "avg = []\n",
    "err = []\n",
    "\n",
    "for i in range(nb_agents):\n",
    "    agents_tf[i].train = common.function(agents_tf[i].train)\n",
    "    agents_tf[i].train_step_counter.assign(0)\n",
    "\n",
    "for _ in tqdm(range(500)):\n",
    "    collect_step(train_env, train_py_env, collect_policies)\n",
    "    \n",
    "for step in tqdm(range(1, num_iterations+1)):\n",
    "    for _ in range(1):\n",
    "        collect_step(train_env, train_py_env, collect_policies)\n",
    "    \n",
    "    for i in range(nb_agents):\n",
    "        experience, _ = next(iterators[i])\n",
    "        agents_tf[i].train(experience=experience)\n",
    "\n",
    "    if step%log_eval_interval==0:\n",
    "        avg_return = compute_avg_return(eval_env, eval_policies, num_eval_episodes)\n",
    "        steps.append(step)\n",
    "        avg.append(avg_return)\n",
    "        print('step = {0}: Average Return = {1}'.format(step, avg_return))\n",
    "        print(\"Variance : \", train_py_env.liste_variances[-1])\n",
    "        print(\"Lengthscale : \", train_py_env.liste_lengthscales[-1])\n",
    "        err.append(np.mean((train_py_env.moy-means)**2))\n",
    "        print(\"MSE de la moyenne : \", err[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72839071",
   "metadata": {},
   "source": [
    "## Avec pénalisation des collisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c348f670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = []\n",
    "for x in range(nb_agents+1):\n",
    "    bins.append(x-0.5)\n",
    "    bins.append(x+0.5)\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(steps, avg)\n",
    "plt.xlabel(\"Étapes\")\n",
    "plt.ylabel(\"Récompenses\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(train_py_env.agents_in_collision, bins=bins, rwidth=0.5)\n",
    "plt.xlabel(\"Agents en collision\")\n",
    "plt.ylabel(\"Nombre d'occurrence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cfb3984c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "print(train_py_env.agents_in_collision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "551acce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46560\n",
      "11151\n",
      "273\n",
      "190\n"
     ]
    }
   ],
   "source": [
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    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==0:\n",
    "        cpt += 1\n",
    "print(cpt)\n",
    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==2:\n",
    "        cpt += 1\n",
    "print(cpt)\n",
    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==3:\n",
    "        cpt += 1\n",
    "print(cpt)\n",
    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==4:\n",
    "        cpt += 1\n",
    "print(cpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79c06817",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:47<00:00,  2.11it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rewards = []\n",
    "episodes = 100\n",
    "a = len(eval_py_env.agents_in_collision)\n",
    "for _ in tqdm(range(episodes)):\n",
    "    time_step = eval_env.reset()\n",
    "    episode_return = 0.0\n",
    "    while not time_step.is_last():\n",
    "        actions = []\n",
    "        for i in range(nb_agents):\n",
    "            actions.append(eval_policies[i].action(time_step).action.numpy()[0])\n",
    "        actions = tf.convert_to_tensor([actions])\n",
    "        time_step = eval_env.step(actions)\n",
    "        episode_return += time_step.reward\n",
    "    rewards.append(episode_return.numpy()[0])\n",
    "    \n",
    "bins = []\n",
    "for x in range(nb_agents+1):\n",
    "    bins.append(x-0.5)\n",
    "    bins.append(x+0.5)\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(1, episodes+1), rewards)\n",
    "plt.xlabel(\"Épisodes\")\n",
    "plt.ylabel(\"Récompenses\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(eval_py_env.agents_in_collision[a:], bins=bins, rwidth=0.5)\n",
    "plt.xlabel(\"Agents en collision\")\n",
    "plt.ylabel(\"Nombre d'occurrence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe05d478",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405\n"
     ]
    }
   ],
   "source": [
    "cpt  = 0\n",
    "for ele in eval_py_env.agents_in_collision[a:]:\n",
    "    if ele!=0:\n",
    "        cpt += 1\n",
    "print(cpt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25efc55",
   "metadata": {},
   "source": [
    "## Sans pénalisation des collisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15f3b0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = []\n",
    "for x in range(nb_agents+1):\n",
    "    bins.append(x-0.5)\n",
    "    bins.append(x+0.5)\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(steps, avg)\n",
    "plt.xlabel(\"Étapes\")\n",
    "plt.ylabel(\"Récompenses\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(train_py_env.agents_in_collision, bins=bins, rwidth=0.5)\n",
    "plt.xlabel(\"Agents en collision\")\n",
    "plt.ylabel(\"Nombre d'occurrence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ffe5c15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(train_py_env.agents_in_collision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58de8df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47434\n",
      "10375\n",
      "217\n",
      "148\n"
     ]
    }
   ],
   "source": [
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    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==0:\n",
    "        cpt += 1\n",
    "print(cpt)\n",
    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==2:\n",
    "        cpt += 1\n",
    "print(cpt)\n",
    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==3:\n",
    "        cpt += 1\n",
    "print(cpt)\n",
    "\n",
    "cpt  = 0\n",
    "for ele in liste:\n",
    "    if ele==4:\n",
    "        cpt += 1\n",
    "print(cpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "944e643a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:42<00:00,  2.37it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rewards = []\n",
    "episodes = 100\n",
    "a = len(eval_py_env.agents_in_collision)\n",
    "for _ in tqdm(range(episodes)):\n",
    "    time_step = eval_env.reset()\n",
    "    episode_return = 0.0\n",
    "    while not time_step.is_last():\n",
    "        actions = []\n",
    "        for i in range(nb_agents):\n",
    "            actions.append(eval_policies[i].action(time_step).action.numpy()[0])\n",
    "        actions = tf.convert_to_tensor([actions])\n",
    "        time_step = eval_env.step(actions)\n",
    "        episode_return += time_step.reward\n",
    "    rewards.append(episode_return.numpy()[0])\n",
    "    \n",
    "bins = []\n",
    "for x in range(nb_agents+1):\n",
    "    bins.append(x-0.5)\n",
    "    bins.append(x+0.5)\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(1, episodes+1), rewards)\n",
    "plt.xlabel(\"Épisodes\")\n",
    "plt.ylabel(\"Récompenses\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(eval_py_env.agents_in_collision[a:], bins=bins, rwidth=0.5)\n",
    "plt.xlabel(\"Agents en collision\")\n",
    "plt.ylabel(\"Nombre d'occurrence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5d40301",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "295\n"
     ]
    }
   ],
   "source": [
    "cpt  = 0\n",
    "for ele in eval_py_env.agents_in_collision[a:]:\n",
    "    if ele!=0:\n",
    "        cpt += 1\n",
    "print(cpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e2203b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06eb260",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "307c51b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f62f5510",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(nb_agents):\n",
    "    policies_checkpointers[i].save(global_step=agents_tf[i].train_step_counter)"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}