Global Framework [MULTI_VAR].ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "723586bb",
   "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 IPython.display import display\n",
    "from tqdm import tqdm\n",
    "import scipy\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from math import nan, isnan\n",
    "import time\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": "6f346427",
   "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 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 concatenate(x, y):\n",
    "    if len(x)==0:\n",
    "        return y\n",
    "    else:\n",
    "        return np.concatenate((x, y))\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.K(X_v, X_v)\n",
    "    \n",
    "def sigma_prime_matrix(gp_model, X_v, X_s):\n",
    "    variance_n = float(gp_model.gp_reg.Gaussian_noise.variance) #1e-5\n",
    "    a = gp_model.K(X_v, X_v)\n",
    "    b = gp_model.K(X_v, X_s)\n",
    "    c = gp_model.K(X_s, X_s)\n",
    "    d = variance_n*np.eye(len(X_s))\n",
    "    e = gp_model.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 f_D(gp_model, X_v, X_s):\n",
    "    sigma = sigma_matrix(gp_model, X_v)\n",
    "    sigma_prime = sigma_prime_matrix(gp_model, X_v, X_s)\n",
    "    return 0.5 * (np.log(np.linalg.det(sigma)) - np.log(np.linalg.det(sigma_prime)))\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 pollutant_concentration(x, y, z, Q, u, h_s, g, V, T_a, T_s, a_y, b_y, a_z, b_z):\n",
    "    if x==0:\n",
    "        x = 0.7\n",
    "    sigma_y = sigma_function(x, a_y, b_y)\n",
    "    sigma_z = sigma_function(x, a_z, b_z)\n",
    "    H = h_s + delta_h(x, u, g, V, T_a, T_s)\n",
    "    return (Q/(2*np.pi*u*sigma_y*sigma_z))*np.exp(-y**2/(2*sigma_y))*(np.exp(-(z-H)**2/(2*sigma_y))+np.exp(-(z+H)**2/(2*sigma_y)))\n",
    "\n",
    "def sigma_function(x, a, b):\n",
    "    return a * (abs(x) ** b)\n",
    "\n",
    "def delta_h(x, u, g, V, T_a, T_s):\n",
    "    F = (g/np.pi) * V * ((T_s - T_a)/T_s)\n",
    "    return (1.6 * (F**(1/3)) * (x**(2/3))) / u\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])\n",
    "    \n",
    "def plot_results(steps, avg_rewards, err, train_py_env, grd_truth_variances, grd_truth_lengthscales, perturbations_steps):\n",
    "    fig, axs = plt.subplots(1, 4, figsize=(20, 5))\n",
    "    axs[0].plot(steps, avg_rewards)\n",
    "    axs[1].plot(steps, err)\n",
    "    axs[2].plot(range(1,len(train_py_env.liste_variances)+1), train_py_env.liste_variances)\n",
    "    axs[3].plot(range(1,len(train_py_env.liste_lengthscales)+1), train_py_env.liste_lengthscales)\n",
    "    for var in grd_truth_variances:\n",
    "        axs[2].axhline(y=10, color='red', linestyle='--')\n",
    "    for l in grd_truth_lengthscales:\n",
    "        axs[3].axhline(y=l, color='red', linestyle='--')\n",
    "    for s in perturbations_steps:\n",
    "        axs[1].axvline(x=s, color='green', linestyle='--')\n",
    "        axs[2].axvline(x=s/100, color='green', linestyle='--')\n",
    "        axs[3].axvline(x=s/100, color='green', linestyle='--')\n",
    "    \n",
    "def compute_means(graph_dim, height, Q, u, h_s, g, V, T_a, T_s, a_y, b_y, a_z, b_z, display=False):\n",
    "    means = []\n",
    "    i = 0\n",
    "    j = -1 * int(graph_dim[1]/2)\n",
    "    i_m = 0\n",
    "    j_m = 0\n",
    "    matrice = [[-1 for _ in range(graph_dim[0])] for _ in range(graph_dim[1])]\n",
    "    for _ in range(graph_dim[1]):\n",
    "        for _ in range(graph_dim[0]):\n",
    "            concentration = pollutant_concentration(i, j, height, Q, u, h_s, g, V, T_a, T_s, a_y, b_y, a_z, b_z)\n",
    "            means.append(concentration + 10)\n",
    "            matrice[i_m][j_m] = concentration\n",
    "            i += 1\n",
    "            j_m += 1\n",
    "        j += 1\n",
    "        i = 0\n",
    "        j_m = 0\n",
    "        i_m += 1\n",
    "    if display:\n",
    "        ax = sns.heatmap(matrice, linewidth = 0, cmap = 'coolwarm')\n",
    "        ax.invert_yaxis()\n",
    "    return means\n",
    "\n",
    "def compute_covariance_matrix(X_v, variances, lengthscale):\n",
    "    cov = [[0 for _ in range(len(X_v))] for _ in range(len(X_v))]\n",
    "    cst = -2*(lengthscale**2)\n",
    "    for i in range(len(X_v)):\n",
    "        for j in range(len(X_v)):\n",
    "            cov[i][j] = np.sqrt(variances[i]) * np.sqrt(variances[j]) * np.exp(distance(X_v[i], X_v[j])**2/cst)\n",
    "    return cov\n",
    "\n",
    "def linear_regression(X, Y):\n",
    "    y = []\n",
    "    for ele in Y:\n",
    "        y.append(np.sqrt(ele))\n",
    "    a, b, _, _, _ = stats.linregress(X, y)\n",
    "    return a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b3f4c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_pilot_data(nb_agents, exploration_steps, graph, graph_dim, means, memory_window, gamma):\n",
    "    X_D = np.array([graph.nodes[i][\"coord\"] for i in range(graph.number_of_nodes())])\n",
    "    cov = compute_covariance_matrix(X_D, variances_lengthscale[0], variances_lengthscale[1])\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",
    "        \n",
    "        for i in sparse_nodes:\n",
    "            l = variances[i]\n",
    "            l[0], l[1], l[2] = weighted_variance(l[0], l[1], l[2], Y_D[i][0], memory_window, gamma)\n",
    "        \n",
    "        sorted_nodes = sorted(sparse_nodes)\n",
    "        \n",
    "        for j in range(len(sorted_nodes)):\n",
    "            if j==0 or sorted_nodes[j]!=sorted_nodes[j-1]:\n",
    "                for i in range(len(sorted_nodes)):\n",
    "                    if sorted_nodes[i]!=sorted_nodes[j] and (sorted_nodes[i]!=sorted_nodes[i-1] or i==0):\n",
    "                        if distance(X_D[sorted_nodes[j]], X_D[sorted_nodes[i]])<=3.1:\n",
    "                            l = liste_covariances[sorted_nodes[j]][sorted_nodes[i]]\n",
    "                            l[0], l[1], l[2], l[3] = weighted_covariance(l[0], l[1], l[2], l[3], Y_D[sorted_nodes[j]], Y_D[sorted_nodes[i]], memory_window, gamma)\n",
    "        \"\"\"\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]]\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",
    "        \"\"\"\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(gp_model, graph, graph_dim, memory_window, gamma):\n",
    "    X_v = np.array([graph.nodes[i][\"coord\"] for i in range(graph.number_of_nodes())])\n",
    "    moyenne = np.reshape(gp_model.gp_reg.predict(X_v)[0], (graph.number_of_nodes(),))\n",
    "    ###########################################\n",
    "    coeff = linear_regression(moyenne, np.array(variances)[:,0])\n",
    "    ###########################################\n",
    "    lengthscale, cpt = 0, 0\n",
    "    for j in range(len(liste_covariances)):\n",
    "        covariances = liste_covariances[j]\n",
    "    #j = 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<np.sqrt(variances[j][0])*np.sqrt(variances[i][0]):\n",
    "                    cpt += covariances[i][0]\n",
    "                    lengthscale += find_lengthscale(X_v[j], X_v[i], covariance, np.sqrt(variances[j][0])*np.sqrt(variances[i][0])) * covariances[i][0]\n",
    "    if cpt!=0:\n",
    "        lengthscale /= cpt\n",
    "    else:\n",
    "        lengthscale = 0\n",
    "    return moyenne, np.array(variances)[:,0], lengthscale, coeff\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",
    "    gamma = 0.9\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eefda13a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GP_Model():\n",
    "    def __init__(self, x, y, variances, lengthscale, graph_dim):\n",
    "        self.X = x\n",
    "        self.Y = y\n",
    "        self.variances = variances\n",
    "        self.lengthscale = lengthscale\n",
    "        self.graph_dim = graph_dim\n",
    "        self.gp_reg = GPy.models.GPRegression(self.X, self.Y, GPy.kern.RBF(2))\n",
    "        self.gp_reg.optimize(messages=False)\n",
    "        \n",
    "    def K(self, X_1, X_2):\n",
    "        res = np.array([[0. for _ in range(len(X_2))] for _ in range(len(X_1))])\n",
    "        ids_2 = [coord_to_id(X_2[k], self.graph_dim) for k in range(len(X_2))]\n",
    "        cst = -2*(self.lengthscale**2)\n",
    "        for i in range(len(X_1)):\n",
    "            id_1 = coord_to_id(X_1[i], self.graph_dim)\n",
    "            for j in range(len(X_2)):\n",
    "                res[i][j] = np.sqrt(self.variances[id_1]) * np.sqrt(self.variances[ids_2[j]]) * np.exp(distances[id_1][ids_2[j]]**2/cst)\n",
    "        return res\n",
    "    \n",
    "    def set_XY(self, x, y):\n",
    "        self.X = x\n",
    "        self.Y = y\n",
    "        self.gp_reg.set_XY(self.X, self.Y)\n",
    "        #self.gp_reg.optimize(messages=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "194304f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Variances:  [379.0809  83.7225  32.6041  15.9201   5.1529  83.7225  68.7241  55.2049\n",
      "  23.5225   7.29    32.6041  55.2049  43.1649  32.6041   9.7969  15.9201\n",
      "  23.5225  32.6041  23.5225  15.9201   5.1529   7.29     9.7969  15.9201\n",
      "   9.7969]\n",
      "Variances:  [347.93886573 127.86299832  40.04108035   3.03849795   8.65558154\n",
      "  32.72736757  16.00036659  20.16364381  18.63785194   9.26118173\n",
      "  28.2331647  111.21122996  55.32530628  32.6226712    5.85121895\n",
      "  29.01546791  24.29931765  30.22200868  28.97247926   3.99986574\n",
      "   4.81540376   7.60845392   4.07768217  19.61860999  13.90107516]\n",
      "Lengthscale:  [1.4158923]\n",
      "Carte:  [50.81018437 32.59702117 19.0396353  12.83605944  8.72706095 31.87736114\n",
      " 24.94560517 20.78723811 17.99444185 12.21612739 18.48696619 19.71686206\n",
      " 21.42903997 20.48106039 14.14835476 13.41490405 16.82690408 19.61917225\n",
      " 19.41754234 14.59674904 10.83056163 12.45894908 14.05164494 14.78401045\n",
      " 13.08363098]\n",
      "(a, b) =  (0.3615490196195394, -1.5800324557732948)\n",
      "Incertitude initiale :  43.97989274903053\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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 = np.array([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",
    "variances_lengthscale = [(0.344 * means - 1.17)**2, 2]\n",
    "nb_agents = 4\n",
    "exploration_steps = 50\n",
    "memory_window = 1000\n",
    "gamma = 0.99\n",
    "\n",
    "variances = [[0, 0, 0] for _ in range(graph.number_of_nodes())]\n",
    "liste_covariances = [[[0, 0, 0, 0] for _ in range(graph.number_of_nodes())] for _ in range(graph.number_of_nodes())]\n",
    "distances = [[distance(X_v[i], X_v[j]) for j in range(len(X_v))] for i in range(len(X_v))]\n",
    "\n",
    "incertitude, x, y = collect_pilot_data(nb_agents, exploration_steps, graph, graph_dim, means, 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",
    "gp_model = GP_Model(x, y, 0, 0, graph_dim)\n",
    "\n",
    "moyenne, var, l, coeff = hyperparameters_estimator(gp_model, graph, graph_dim, memory_window, gamma)\n",
    "print(\"True Variances: \", variances_lengthscale[0])\n",
    "print(\"Variances: \", var)\n",
    "print(\"Lengthscale: \", l)\n",
    "print(\"Carte: \", moyenne)\n",
    "print(\"(a, b) = \", coeff)\n",
    "\n",
    "gp_model.variances = var\n",
    "gp_model.lengthscale = l\n",
    "#display(gp_model.gp_reg)\n",
    "gp_model.gp_reg.plot()\n",
    "#print(gp_model.predict(np.array([[0, 0]])))\n",
    "#print(gp_model.predict(np.array([[4, 4]])))\n",
    "print(\"Incertitude initiale : \", incertitude_initiale(sigma_matrix(gp_model, X_v), graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1d33581",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2844d45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d8cab6b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9c25a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_gp_model(gp_model, x, y, var, l):\n",
    "    X = np.concatenate((gp_model.X, x))[-2000:]\n",
    "    Y = np.concatenate((gp_model.Y, y))[-2000:]\n",
    "    gp_model.variances = var\n",
    "    gp_model.lengthscale = l\n",
    "    gp_model.set_XY(X, Y)\n",
    "    return gp_model\n",
    "\n",
    "def take_measurements(nodes, means, X_v, graph_dim, memory_window, gamma):\n",
    "    cov = compute_covariance_matrix(X_v, variances_lengthscale[0], variances_lengthscale[1])\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",
    "    \n",
    "    for i in nodes:\n",
    "        l = variances[i]\n",
    "        l[0], l[1], l[2] = weighted_variance(l[0], l[1], l[2], Y_v[i][0], memory_window, gamma)\n",
    "    \n",
    "    sorted_nodes = sorted(nodes)\n",
    "    \n",
    "    for j in range(len(sorted_nodes)):\n",
    "            if j==0 or sorted_nodes[j]!=sorted_nodes[j-1]:\n",
    "                for i in range(len(sorted_nodes)):\n",
    "                    if sorted_nodes[i]!=sorted_nodes[j] and (sorted_nodes[i]!=sorted_nodes[i-1] or i==0):\n",
    "                        if distance(X_v[sorted_nodes[j]], X_v[sorted_nodes[i]])<=3.1:\n",
    "                            l = liste_covariances[sorted_nodes[j]][sorted_nodes[i]]\n",
    "                            l[0], l[1], l[2], l[3] = weighted_covariance(l[0], l[1], l[2], l[3], Y_v[sorted_nodes[j]], Y_v[sorted_nodes[i]], memory_window, gamma)\n",
    "    \"\"\"\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]]\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",
    "    \"\"\"\n",
    "    return X_v[nodes], Y_v[nodes]\n",
    "\n",
    "def clean():\n",
    "    for ele in liste_covariances:\n",
    "        for sub_ele in ele:\n",
    "            sub_ele[0], sub_ele[1], sub_ele[2], sub_ele[3] = 0, 0, 0, 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9b7d0e0",
   "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, coeff_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, self.coeffs = [variance_0], [lengthscale_0], [coeff_0]\n",
    "        self.train_env = train_env\n",
    "        self.indice_lengthscale = 0\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",
    "        \n",
    "        positions, mesures = take_measurements(actions, means, self.X_v, self.graph_dim, self.memory_window, self.gamma)\n",
    "        if self.train_env:\n",
    "            m, var, l, coeff = hyperparameters_estimator(self.gp_model, 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.coeffs.append(coeff)\n",
    "            ##########################\n",
    "            vi, vs = min(self.liste_lengthscales[self.indice_lengthscale:]), max(self.liste_lengthscales[self.indice_lengthscale:])\n",
    "            if vs-vi>3:\n",
    "                clean()\n",
    "                self.indice_lengthscale = len(self.liste_lengthscales)\n",
    "            ##########################\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 = update_gp_model(self.gp_model, self.x, self.y, var, l)\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",
    "        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": "9e9b3a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_iterations = 15000\n",
    "fc_layer_params = (128,64,)\n",
    "replay_buffer_capacity = 5000\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": "99c15320",
   "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, coeff, 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, coeff, 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",
    "    collect_policies[-1]._epsilon = 1.\n",
    "    \n",
    "    policies_checkpointers.append(common.Checkpointer(ckpt_dir='Collisions_Politique__'+str(i), policy=eval_policies[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6eeaf3c1",
   "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 random_compute_avg_return(environment, 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 = [random.randint(0, 3) for _ in range(nb_agents)]\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)\n",
    "            \n",
    "def random_collect_step(environment, environment_py):\n",
    "    time_step = environment.current_time_step()\n",
    "    actions = [random.randint(0, 3) for _ in range(nb_agents)]\n",
    "    actions = tf.convert_to_tensor([actions])\n",
    "    next_time_step = environment.step(actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e76e270f",
   "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",
    "    iterators.append(iter(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4babb8f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.1818743\n",
      "2.0901055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 500/500 [01:58<00:00,  4.24it/s]\n",
      "  0%|                                                                                        | 0/15000 [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",
      "  7%|████▉                                                                     | 1000/15000 [05:08<18:03:07,  4.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 1000: Average Return = 2.394273281097412\n",
      "Variances :  [400.13209152  79.97602493  33.08379406  12.30488007   5.5894541\n",
      "  82.26847178  62.06987946  52.87726766  22.82510174   6.55075362\n",
      "  41.76349736  50.65750093  40.29836657  33.53655018   9.43011846\n",
      "  17.32394518  24.57813961  31.98404745  28.11076141  16.97167606\n",
      "   5.70235367   8.65544766   8.81623349  15.39436747  10.66329465]\n",
      "MAE des variances :  2.8832516744950083\n",
      "(a, b) =  (0.3481505161220898, -1.233898153457365)\n",
      "Lengthscale :  [2.04053503]\n",
      "MSE de la moyenne :  0.9705174740283751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█████████▊                                                                | 2000/15000 [09:50<14:30:39,  4.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 2000: Average Return = 2.4510459899902344\n",
      "Variances :  [399.54969819  86.46514932  32.71783759  13.53057329   5.30929775\n",
      "  84.7171881   67.81709114  51.24865034  22.7995482    7.19094814\n",
      "  39.89591079  53.88686241  41.35709152  36.2683752   10.45020241\n",
      "  17.2056001   25.52143125  32.20419647  26.87242396  16.6913851\n",
      "   5.75242754   8.35173073   9.02958142  15.18275761   9.63973961]\n",
      "MAE des variances :  2.336596714771301\n",
      "(a, b) =  (0.35079578650995297, -1.2581004952162527)\n",
      "Lengthscale :  [2.03765648]\n",
      "MSE de la moyenne :  399.11249090841306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|███████████████▍                                                             | 2999/15000 [13:35<55:18,  3.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 3000: Average Return = 8.343979835510254\n",
      "Variances :  [412.61111111 157.45216603  94.66630194  56.7783556   93.73245188\n",
      " 143.38907026 107.74857782  95.4133173   77.10134209 102.20734789\n",
      "  84.09471896 107.65258534  87.30248611 100.87777158 105.51854047\n",
      "  65.35784018  81.03215633  96.33307007 102.52568195 118.15835136\n",
      "  86.65087123  80.77872707 101.57353002 116.33975386  10.00241556]\n",
      "MAE des variances :  63.90132167910236\n",
      "(a, b) =  (0.21316800393350466, 1.4756298149937024)\n",
      "Lengthscale :  [0.96935852]\n",
      "MSE de la moyenne :  2.1585965223168926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|███████████████████▋                                                      | 4001/15000 [18:17<11:40:24,  3.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 4000: Average Return = 3.6595444679260254\n",
      "Variances :  [366.67199192  78.90235983  28.94405071  14.47019377   5.29509558\n",
      "  80.2007612   58.60177701  58.16899794  22.57637114   7.18477174\n",
      "  32.2378242   53.74771023  43.18590006  27.16297547  10.16454412\n",
      "  16.23769027  24.00268591  34.4108573   23.62941764  14.60387782\n",
      "   5.33953285   6.93836786  10.16264344  15.39397261   8.52746762]\n",
      "MAE des variances :  2.1808476791076763\n",
      "(a, b) =  (0.33108759473635424, -7.617621266818892)\n",
      "Lengthscale :  [1.18593018]\n",
      "MSE de la moyenne :  1.4376436372156576\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|████████████████████████▋                                                 | 5000/15000 [23:07<17:36:38,  6.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 5000: Average Return = 4.620763301849365\n",
      "Variances :  [384.49829564  80.53543621  29.64787377  14.79873709   5.03516298\n",
      "  79.54949188  59.2996184   55.89335626  22.08572961   6.73357163\n",
      "  31.81305017  57.3408641   43.15230937  28.44084215  10.13142377\n",
      "  16.6908442   24.94095624  34.3662399   23.12443906  15.12978716\n",
      "   5.42141243   7.02120012   9.99489029  14.9772055    9.35465042]\n",
      "MAE des variances :  1.7510590933265453\n",
      "(a, b) =  (0.3513049623407983, -8.384768747593874)\n",
      "Lengthscale :  [1.80118101]\n",
      "MSE de la moyenne :  1.612723410346672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|█████████████████████████████▉                                             | 5999/15000 [27:53<1:10:16,  2.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 6000: Average Return = 5.525445461273193\n",
      "Variances :  [327.06165054  82.51214793  29.17615826  15.41044208   4.92825677\n",
      "  68.34557522  59.29664157  53.39431035  22.45620057   7.02733185\n",
      "  32.6883941   56.73221654  42.56313396  28.96062806   9.52687095\n",
      "  17.33993731  24.4641947   34.93448229  22.80194474  17.04751271\n",
      "   5.46315971   7.1014736    9.86050907  17.63362544   8.95683746]\n",
      "MAE des variances :  4.044741118951213\n",
      "(a, b) =  (0.3222710231214256, -7.312175146792813)\n",
      "Lengthscale :  [1.90176381]\n",
      "MSE de la moyenne :  1.08663524501398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|██████████████████████████████████▌                                       | 7000/15000 [34:02<15:36:11,  7.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 7000: Average Return = 4.4749860763549805\n",
      "Variances :  [391.56526649  82.20281423  34.11754117  16.27807332   4.51138507\n",
      "  84.60732043  67.45574707  52.66746864  21.6103557    7.13799553\n",
      "  30.99723646  52.59399415  42.82153139  35.16879025   9.9092664\n",
      "  15.76865353  23.7514181   33.55490498  25.87910397  15.71367639\n",
      "   5.47909833   7.92283846  10.78460064  15.84781489  10.08204403]\n",
      "MAE des variances :  1.4683317403324179\n",
      "(a, b) =  (0.3380489922268415, -7.8122973149396255)\n",
      "Lengthscale :  [2.49943557]\n",
      "MSE de la moyenne :  1.2391040238563822\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|███████████████████████████████████████▍                                  | 8000/15000 [39:20<13:56:53,  7.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 8000: Average Return = 5.2755327224731445\n",
      "Variances :  [342.72959204  81.89006899  35.02886995  15.95377427   4.59543595\n",
      "  95.91360063  64.77441354  53.04626964  23.48068624   7.39266899\n",
      "  30.24768514  53.8013204   44.47263129  34.72052917   9.54215518\n",
      "  15.20733055  24.11277525  34.1149082   26.22080475  15.29085868\n",
      "   5.34079606   7.95385628  10.45854274  16.1254042   10.05129421]\n",
      "MAE des variances :  3.007877585569787\n",
      "(a, b) =  (0.32797929950406374, -7.392098153022313)\n",
      "Lengthscale :  [1.93190336]\n",
      "MSE de la moyenne :  1.6932084473017586\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|█████████████████████████████████████████████                              | 9001/15000 [44:06<7:54:50,  4.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 9000: Average Return = 4.598736763000488\n",
      "Variances :  [341.12118564  78.07452556  33.39215229  16.24807959   4.44776729\n",
      "  75.91461598  59.45028263  53.70672522  24.50438204   7.39262586\n",
      "  30.49420203  53.00140616  44.38735699  34.34076482   9.50076526\n",
      "  15.32969845  24.11677643  34.12447435  25.95886893  15.35227755\n",
      "   5.47018284   7.76350405  10.39575929  16.02211725  10.24389727]\n",
      "MAE des variances :  3.2123916095924825\n",
      "(a, b) =  (0.3139984451135025, -6.979929849168465)\n",
      "Lengthscale :  [2.12119226]\n",
      "MSE de la moyenne :  1.2739648839954523\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|████████████████████████████████████████████████▋                        | 10000/15000 [49:09<11:10:58,  8.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 10000: Average Return = 6.717146396636963\n",
      "Variances :  [422.27880628  98.88024591  39.20563695  16.36520572   4.74436488\n",
      "  80.04450084  68.58516412  52.86971507  24.30872775   7.44969206\n",
      "  32.43890322  48.35826277  45.08616311  34.09530643   9.36060634\n",
      "  17.1926232   23.73993757  33.76624306  25.5785594   15.21699943\n",
      "   5.45501894   7.5456215   10.3314781   15.14222174  10.11662367]\n",
      "MAE des variances :  3.6548260501456706\n",
      "(a, b) =  (0.3638516802190659, -8.68089640836227)\n",
      "Lengthscale :  [1.69499968]\n",
      "MSE de la moyenne :  1.5587061938556945\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████████████████████████████████████████████████████▋                    | 10999/15000 [54:05<12:10,  5.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 11000: Average Return = 5.979046821594238\n",
      "Variances :  [323.07477531  76.77664355  42.59401891  16.76304494   4.92945462\n",
      "  66.57710795  67.62833615  53.41417234  24.0961659    7.60083925\n",
      "  27.98573532  52.39118222  44.60069255  33.5266888    9.50371635\n",
      "  18.56228504  23.97793978  34.09323281  24.85713859  14.83510848\n",
      "   5.54028589   7.63162651  10.3341804   15.33397682   9.6423922 ]\n",
      "MAE des variances :  4.560865522638392\n",
      "(a, b) =  (0.31077126267223376, -6.782679751736124)\n",
      "Lengthscale :  [1.86836584]\n",
      "MSE de la moyenne :  1.0755367674942686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|█████████████████████████████████████████████████████████▌                  | 11354/15000 [56:25<18:07,  3.35it/s]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Received a time_step input that converted to a nan array. Did you accidentally set some input value to None?.\nGot:\nnan",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14860/182469736.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mcollect_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_env\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_py_env\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcollect_policies\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnb_agents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14860/2868047350.py\u001b[0m in \u001b[0;36mcollect_step\u001b[1;34m(environment, environment_py, policies)\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions_step\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m     \u001b[0mactions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m     \u001b[0mnext_time_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menvironment\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m     \u001b[0mrewards\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredit_assignment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menvironment_py\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnext_time_step\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnb_agents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\tf_environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    239\u001b[0m           \u001b[0mcorresponding\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mobservation_spec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m     \"\"\"\n\u001b[1;32m--> 241\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mrender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\tf_py_environment.py\u001b[0m in \u001b[0;36m_step\u001b[1;34m(self, actions)\u001b[0m\n\u001b[0;32m    314\u001b[0m                 \u001b[1;34m'but saw action with shape %s:\\n   %s'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    315\u001b[0m                 (self.batch_size, action.shape, action))\n\u001b[1;32m--> 316\u001b[1;33m       outputs = tf.numpy_function(\n\u001b[0m\u001b[0;32m    317\u001b[0m           \u001b[0m_isolated_step_py\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m           \u001b[0mflat_actions\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    151\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\tf_py_environment.py\u001b[0m in \u001b[0;36m_isolated_step_py\u001b[1;34m(*flattened_actions)\u001b[0m\n\u001b[0;32m    301\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_isolated_step_py\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mflattened_actions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_step_py\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mflattened_actions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    304\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'step'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\tf_py_environment.py\u001b[0m in \u001b[0;36m_execute\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\tf_py_environment.py\u001b[0m in \u001b[0;36m_step_py\u001b[1;34m(*flattened_actions)\u001b[0m\n\u001b[0;32m    297\u001b[0m         packed = tf.nest.pack_sequence_as(\n\u001b[0;32m    298\u001b[0m             structure=self.action_spec(), flat_sequence=flattened_actions)\n\u001b[1;32m--> 299\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_time_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_env\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpacked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    300\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_time_step\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\py_environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    231\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_time_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    234\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_time_step\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\batched_py_environment.py\u001b[0m in \u001b[0;36m_step\u001b[1;34m(self, actions)\u001b[0m\n\u001b[0;32m    164\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_envs\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m       \u001b[0mactions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnest_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munbatch_nested_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m       \u001b[0mtime_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_envs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mnest_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_nested_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime_steps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\py_environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    231\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_time_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    234\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_time_step\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\wrappers.py\u001b[0m in \u001b[0;36m_step\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    115\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m     \u001b[0mtime_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_env\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_steps\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\environments\\py_environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    231\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_time_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    234\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_current_time_step\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14860/2378060803.py\u001b[0m in \u001b[0;36m_step\u001b[1;34m(self, abstract_actions)\u001b[0m\n\u001b[0;32m    123\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_episode_ended\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtermination\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiscount\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.97\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\trajectories\\time_step.py\u001b[0m in \u001b[0;36mtransition\u001b[1;34m(observation, reward, discount, outer_dims)\u001b[0m\n\u001b[0;32m    218\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mTimeStep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiscount\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m     \u001b[1;31m# Infer the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 220\u001b[1;33m     \u001b[0mreward\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_as_float32_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    221\u001b[0m     \u001b[0mfirst_reward\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m     \u001b[0mdiscount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_as_float32_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdiscount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tf_agents\\trajectories\\time_step.py\u001b[0m in \u001b[0;36m_as_float32_array\u001b[1;34m(a)\u001b[0m\n\u001b[0;32m     35\u001b[0m   \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m     raise ValueError('Received a time_step input that converted to a nan array.'\n\u001b[0m\u001b[0;32m     38\u001b[0m                      \u001b[1;34m' Did you accidentally set some input value to None?.\\n'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m                      'Got:\\n{}'.format(a))\n",
      "\u001b[1;31mValueError\u001b[0m: Received a time_step input that converted to a nan array. Did you accidentally set some input value to None?.\nGot:\nnan"
     ]
    }
   ],
   "source": [
    "print(compute_avg_return(eval_env, collect_policies, num_eval_episodes))\n",
    "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",
    "    if step%25==0:\n",
    "        for cp in collect_policies:\n",
    "            cp._epsilon = max(cp._epsilon*0.99, 0.1)\n",
    "    \n",
    "    #if step==3000:\n",
    "        #variances_lengthscale[1] += 1\n",
    "    if step==2000:\n",
    "        means = means + 20\n",
    "    \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(\"Variances : \", train_py_env.liste_variances[-1])\n",
    "        mae = np.mean(np.abs(train_py_env.liste_variances[-1]-variances_lengthscale[0]))\n",
    "        print(\"MAE des variances : \", mae)\n",
    "        print(\"(a, b) = \", train_py_env.coeffs[-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])\n",
    "        if mae>4:\n",
    "            clean()\n",
    "            for ele in variances:\n",
    "                ele[0], ele[1], ele[2] = 0, 0, 0\n",
    "            for _ in range(50):\n",
    "                collect_pilot_data(nb_agents, 50, graph, graph_dim, means, memory_window, gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "58175ea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000]\n",
      "##################\n",
      "[2.3942733, 2.451046, 8.34398, 3.6595445, 4.6207633, 5.5254455, 4.474986, 5.2755327, 4.598737, 6.7171464, 5.979047]\n",
      "##################\n",
      "[0.9705174740283751, 399.11249090841306, 2.1585965223168926, 1.4376436372156576, 1.612723410346672, 1.08663524501398, 1.2391040238563822, 1.6932084473017586, 1.2739648839954523, 1.5587061938556945, 1.0755367674942686]\n",
      "##################\n",
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array([2.16863528]), array([2.15900984]), array([2.15411418]), array([2.15455507]), array([2.15287239]), array([2.15344321]), array([2.15136912]), array([2.15254962]), array([2.1524567]), array([2.15275864]), array([2.1592352]), array([2.16543918]), array([2.16245434]), array([2.16251801]), array([2.1613047]), array([2.16090591]), array([2.16103131]), array([2.1607668]), array([2.1688025]), array([2.16855937]), array([2.15596399]), array([2.156362]), array([2.16267469]), array([2.16296173]), array([2.1779256]), array([2.17739112]), array([2.1794213]), array([2.18040543]), array([2.18045183]), array([2.18155275]), array([2.17869361]), array([2.18010293]), array([2.20522447]), array([2.20636489]), array([2.20602149]), array([2.20633084]), array([2.20675706]), array([2.20088517]), array([2.20432731]), array([2.20388436]), array([2.20378974]), array([2.20615143]), array([2.2070745]), array([2.20579836]), array([2.24087524]), array([2.28563406]), array([2.28404511]), array([2.26470541]), array([2.3651351]), array([2.38208359]), array([2.38166496]), array([2.40648427]), array([2.48634598]), array([2.49077987]), array([2.47434025]), array([2.47333108]), array([2.26165048]), array([2.25870272]), array([2.25882607]), array([2.19393364]), array([2.19375797]), array([2.19517497]), array([2.19474036]), array([2.19466586]), array([2.24024031]), array([2.24069653]), array([2.24058154]), array([2.24033915]), array([2.24086223]), array([2.24338307]), array([2.24358668]), array([2.24652837]), array([2.24499285]), array([2.2443399]), array([2.2929127]), array([2.29250139]), array([2.29191912]), array([3.174003]), 0, 0, array([0.38357284]), array([0.8531064]), array([0.85310615]), array([0.85309146]), array([0.8146446]), array([0.81497804]), array([1.06996454]), array([1.06998909]), array([1.07000282]), array([1.0698989]), array([1.55677518]), array([1.55710444]), array([1.47588645]), array([1.41617189]), array([1.27880586]), array([1.90957841]), array([1.84579418]), array([1.75035481]), array([1.64106768]), array([2.38749359]), array([2.34124931]), array([2.34585115]), array([2.2686192]), array([2.19285929]), array([2.19537275]), array([2.0813119]), array([1.99731345]), array([2.05848748]), array([2.09060695]), array([3.01865372]), 0, 0]\n"
     ]
    }
   ],
   "source": [
    "print(steps)\n",
    "print(\"##################\")\n",
    "print(avg)\n",
    "print(\"##################\")\n",
    "print(err)\n",
    "print(\"##################\")\n",
    "print(train_py_env.coeffs)\n",
    "print(\"##################\")\n",
    "print(train_py_env.liste_lengthscales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "634c8846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1656x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000]\n",
    "avg = [2.3942733, 2.451046, 8.34398, 3.6595445, 4.6207633, 5.5254455, 4.474986, 5.2755327, 4.598737, 6.7171464, 5.979047]\n",
    "err = [0.9705174740283751, 399.11249090841306, 2.1585965223168926, 1.4376436372156576, 1.612723410346672, 1.08663524501398, 1.2391040238563822, 1.6932084473017586, 1.2739648839954523, 1.5587061938556945, 1.0755367674942686]\n",
    "err_variances = [2.8832516744950083, 2.336596714771301, 63.90132167910236, 2.1808476791076763, 1.7510590933265453, 4.044741118951213, 1.4683317403324179, 3.007877585569787, 3.2123916095924825, 3.6548260501456706, 4.560865522638392]\n",
    "ccs = [2.04053503, 2.03765648, 0.96935852, 1.18593018, 1.80118101, 1.90176381, 2.49943557, 1.93190336, 2.12119226, 1.69499968, 1.86836584]\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize=(23, 7))\n",
    "axs[0].plot(steps, avg)\n",
    "axs[1].plot(steps, err)\n",
    "axs[2].plot(steps, err_variances)\n",
    "axs[3].plot(steps, ccs)\n",
    "\n",
    "axs[1].axhline(y=0, color='red', linestyle='--')\n",
    "axs[3].axhline(y=2, color='red', linestyle='--')\n",
    "\n",
    "axs[1].axvline(x=2000, color='green', linestyle='--')\n",
    "axs[2].axvline(x=2000, color='green', linestyle='--')\n",
    "axs[3].axvline(x=2000, color='green', linestyle='--')\n",
    "\n",
    "axs[0].set_title('(a)')\n",
    "axs[1].set_title('(b)')\n",
    "axs[2].set_title('(c)')\n",
    "axs[3].set_title('(d)')\n",
    "axs[0].set(xlabel='Steps', ylabel=\"Récompenses\")\n",
    "axs[1].set(xlabel='Steps', ylabel=\"MSE Moyennes\")\n",
    "axs[2].set(xlabel='Steps', ylabel=\"MAE Variances\")\n",
    "axs[3].set(xlabel='Steps', ylabel=\"Lengthscales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4125035",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8bd351b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reward :  24.165461855925976\n",
      "[6.26527879 6.09462284 5.85173557 5.95382529]\n",
      "Reward :  14.277524864343498\n",
      "[4.96624374 2.31758648 2.51421336 4.47948137]\n",
      "Reward :  9.867613349062694\n",
      "[3.86720685 1.27572923 1.25581047 3.46886725]\n",
      "Reward :  5.08209500699661\n",
      "[1.98520626 0.68308456 0.70150589 1.71229843]\n",
      "Reward :  5.189434961150596\n",
      "[2.01542317 0.70986803 1.28393984 1.18020396]\n",
      "Reward :  0.6924427035080072\n",
      "[0.29923962 0.10440146 0.18771232 0.10108932]\n",
      "Reward :  0.9483399204529945\n",
      "[0.46854284 0.16145663 0.16195974 0.15638072]\n",
      "Reward :  5.101016852671648\n",
      "[3.15823238 0.83547236 0.23355574 0.87375652]\n",
      "Reward :  9.402737582096549\n",
      "[5.66920686 1.23453679 0.36223297 2.136761  ]\n",
      "Reward :  6.370057310943636\n",
      "[3.80989798 0.64885767 0.60627443 1.30502703]\n",
      "Reward :  0.43838664482744605\n",
      "[0.26280096 0.04428664 0.04223552 0.08906354]\n",
      "Reward :  5.015820090663851\n",
      "[2.98302667 0.50517091 0.5049101  1.02271234]\n",
      "Reward :  6.590999267236114\n",
      "[3.73721281 0.59025597 0.57286785 1.69066251]\n",
      "Reward :  0.5540921216355343\n",
      "[0.3136876  0.05003318 0.04913562 0.14123571]\n",
      "Reward :  4.037740674530198\n",
      "[2.08849148 0.35161216 0.33960951 1.25802756]\n",
      "Reward :  0.3679167390535554\n",
      "[0.19014987 0.03225877 0.03142635 0.11408174]\n",
      "Reward :  5.229711912188492\n",
      "[2.91922639 0.42379987 0.41126331 1.47542244]\n",
      "Reward :  5.21354274409083\n",
      "[2.90670544 0.43357476 0.44618735 1.42707538]\n",
      "Reward :  0.6943341477678473\n",
      "[0.3618733  0.06350046 0.06346095 0.20549944]\n",
      "Reward :  3.881236919866552\n",
      "[2.00897543 0.35810023 0.35765593 1.15650545]\n",
      "Reward :  3.5597379391428774\n",
      "[1.83793451 0.32829246 0.34306418 1.05044677]\n",
      "Reward :  0.5890275449812918\n",
      "[0.30278429 0.05465097 0.05859739 0.17299487]\n",
      "Reward :  0.7300455102342482\n",
      "[0.37424653 0.06850024 0.07379618 0.21350255]\n",
      "Reward :  0.41635975936645764\n",
      "[0.2124957  0.03938742 0.04310488 0.12137175]\n",
      "Reward :  0.47796061645647114\n",
      "[0.         0.09298719 0.10158831 0.28338512]\n",
      "[[14, 9, 14, 13, 18, 23, 18, 13, 8, 7, 12, 7, 6, 11, 6, 11, 6, 1, 0, 1, 2, 3, 8, 3, 8, 3], [23, 24, 23, 24, 23, 22, 21, 22, 23, 24, 23, 23, 23, 22, 23, 22, 23, 22, 23, 24, 24, 24, 23, 24, 23, 24], [23, 18, 23, 18, 17, 16, 21, 22, 23, 18, 19, 18, 13, 18, 13, 18, 13, 18, 19, 19, 19, 14, 19, 14, 19, 14], [23, 22, 21, 20, 20, 21, 22, 17, 16, 15, 15, 15, 15, 10, 10, 5, 5, 5, 6, 7, 8, 13, 8, 13, 8, 13]]\n",
      "118.89365\n",
      "##################################\n",
      "Moyenne :  118.89365\n"
     ]
    }
   ],
   "source": [
    "total_return = 0.0\n",
    "episodes = 1\n",
    "for _ in 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",
    "        #print(credit_assignment(eval_py_env, time_step.reward.numpy()[0], eval_py_env.dones.copy()))\n",
    "        episode_return += time_step.reward\n",
    "    print(eval_py_env.paths)\n",
    "    print(episode_return.numpy()[0])\n",
    "    print(\"##################################\")\n",
    "    total_return += episode_return\n",
    "avg_return = total_return / episodes\n",
    "print(\"Moyenne : \", avg_return.numpy()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "592bec54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb90c689",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa12cb1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a530733",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(nb_agents):\n",
    "    policies_checkpointers[i].save(global_step=agents_tf[i].train_step_counter)"
   ]
  }
 ],
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