DQN-resblocks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DQN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mikhail Papkov, 17.06.2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will train the original DQN model on our Pommerman data and compare results with the imitation pre-training and without it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DQN paper: https://www.nature.com/articles/nature14236.pdf\n",
    "\n",
    "Pre-training paper: https://arxiv.org/pdf/1709.04083.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "# GPU specifications\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"7\"\n",
    "\n",
    "sys.path.append('/path/to/pommerman/playground/')\n",
    "sys.path.append('/path/to/evaluation_utils.py/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, CSVLogger\n",
    "from keras.layers import Input, Dense, Flatten, Conv2D, Conv3D, BatchNormalization, Activation, Add, Convolution2D\n",
    "from keras.models import Model, Sequential, load_model\n",
    "from keras.utils import to_categorical\n",
    "from keras.optimizers import Adadelta, Adam\n",
    "from keras import backend as K\n",
    "\n",
    "import warnings\n",
    "from rl.agents.dqn import DQNAgent\n",
    "from rl.policy import BoltzmannQPolicy, LinearAnnealedPolicy, EpsGreedyQPolicy\n",
    "from rl.memory import SequentialMemory\n",
    "from rl.core import Env, Processor\n",
    "from rl.callbacks import FileLogger, ModelIntervalCheckpoint, Callback\n",
    "\n",
    "from pommerman.agents import SimpleAgent, RandomAgent, PlayerAgent, BaseAgent\n",
    "from pommerman.configs import ffa_v0_fast_env as ffa_v0_env\n",
    "from pommerman.configs import ffa_v0_fast_env \n",
    "from pommerman.envs.v0 import Pomme\n",
    "from pommerman.characters import Bomber\n",
    "from pommerman import utility\n",
    "from pommerman.constants import *\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from evaluation_utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "view_size = BOARD_SIZE * 2 - 1  # 21\n",
    "history_length = 4\n",
    "n_channels = 18\n",
    "ACTIONS = ['stop', 'up', 'down', 'left', 'right', 'bomb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the environment\n",
    "config = ffa_v0_fast_env()\n",
    "env = Pomme(**config[\"env_kwargs\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data for the pre-training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_path = './dataset/'\n",
    "train_data_obs = os.path.join(train_data_path, 'obs.npy')\n",
    "train_data_labels = os.path.join(train_data_path, 'labels.npy')\n",
    "train_data_reward = os.path.join(train_data_path, 'reward.npy')\n",
    "train_data_obs_map_history = os.path.join(train_data_path, 'obs_map_history.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(670725, 4, 21, 21, 18)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isdir(train_data_path):\n",
    "    observations = np.load(train_data_obs_map_history)\n",
    "    labels = np.load(train_data_labels)\n",
    "    labels = to_categorical(labels, env.action_space.n)\n",
    "    print('Loaded')\n",
    "else:\n",
    "    assert False\n",
    "\n",
    "observations.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenate time steps to the channel dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(670725, 21, 21, 72)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observations = np.array([np.concatenate(obs, axis=-1) for obs in observations])\n",
    "observations.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Featurization and evaluation functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class PretrainedAgent(EvaluatorAgent):\n",
    "    def __init__(self, n_actions, character, \n",
    "                 checkpoint_path, log_path, load_weights=False, **kwargs):\n",
    "        super(PretrainedAgent, self).__init__(character=character, n_actions=n_actions, **kwargs)\n",
    "        \n",
    "        self.current_epoch = 0\n",
    "        \n",
    "        # Logs\n",
    "        self.checkpoint_path = checkpoint_path\n",
    "        if not os.path.isdir(os.path.dirname(checkpoint_path)):\n",
    "            os.makedirs(os.path.dirname(checkpoint_path))    \n",
    "        self.log_path = log_path\n",
    "        if not os.path.isdir(os.path.dirname(log_path)):\n",
    "            os.makedirs(os.path.dirname(log_path))   \n",
    "        self.logger = Logger(self.log_path)\n",
    "        \n",
    "        # Networks\n",
    "        self.rl_network = self.create_rl_network()\n",
    "        self.pretraining_network = self.create_pretraining_network()\n",
    "        \n",
    "        if load_weights:\n",
    "            self.load_weights()\n",
    "    \n",
    "    def load_weights(self, weights_path=''):\n",
    "        def lw(path):\n",
    "            if os.path.isfile(path):\n",
    "                try:\n",
    "                    print(\"Trying to load weights\")\n",
    "                    self.pretraining_network.load_weights(path)\n",
    "#                     self.rl_network.load_weights(path)\n",
    "                    print(\"Weights were loaded successful\")\n",
    "                except:\n",
    "                    print(\"Weights load failed\")\n",
    "            else:\n",
    "                print('Wrong weights path')\n",
    "                    \n",
    "        if weights_path:\n",
    "            lw(weights_path)\n",
    "        else:\n",
    "            lw(self.checkpoint_path)\n",
    "            \n",
    "    def get_res_block(self, input):\n",
    "        # Res block 1        \n",
    "        x = Convolution2D(256, 3, padding='same')(input)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "        x = Convolution2D(256, 3, padding='same')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Add()([input, x])\n",
    "        x = Activation('relu')(x)\n",
    "        return x\n",
    "    \n",
    "    # Also, RL network uses Huber loss\n",
    "    @staticmethod\n",
    "    def huber_loss(y_true, y_pred):\n",
    "            return tf.losses.huber_loss(y_true,y_pred)\n",
    "    \n",
    "    def create_pretraining_network(self, input_shape=(11,11,18)):\n",
    "        inp = Input(input_shape)\n",
    "        x = Convolution2D(256, 3, padding='same')(inp)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "        \n",
    "        # 3 residual blocks\n",
    "        for i in range(3):\n",
    "            x = self.get_res_block(x)\n",
    "        \n",
    "        # Output block\n",
    "        # Should be 2 filters\n",
    "        x = Convolution2D(2, 1, padding='same')(x)\n",
    "        x = BatchNormalization()(x)   \n",
    "        x = Activation('relu')(x)\n",
    "        x = Flatten()(x)  \n",
    "        \n",
    "        probs  = Dense(self.n_actions, activation='softmax', name='actions')(x)\n",
    "        reward = Dense(1, name='reward')(x)\n",
    "        \n",
    "        model = Model(inputs = inp, outputs=[probs, reward])\n",
    "#         model = Model(inputs = inp, outputs=probs)\n",
    "        model.compile(optimizer='adam', loss=['categorical_crossentropy', 'mae'], metrics=['accuracy'])\n",
    "#         model.compile(optimizer='adam', loss=['categorical_crossentropy'], metrics=['accuracy'])\n",
    "        return model\n",
    "\n",
    "    \n",
    "    def create_rl_network(self, input_shape=(11,11,18)):\n",
    "        inp = Input(input_shape)\n",
    "        x = Convolution2D(256, 3, padding='same')(inp)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "        \n",
    "        # 3 residual blocks\n",
    "        for i in range(3):\n",
    "            x = self.get_res_block(x)\n",
    "        \n",
    "        # Output block\n",
    "        # Should be 2 filters\n",
    "        x = Convolution2D(2, 1, padding='same')(x)\n",
    "        x = BatchNormalization()(x)   \n",
    "        x = Activation('relu')(x)\n",
    "        x = Flatten()(x)  \n",
    "        \n",
    "        probs  = Dense(self.n_actions, activation='linear', name='actions')(x)\n",
    "#         reward = Dense(1, name='reward')(x)\n",
    "        \n",
    "#         model = Model(inputs = inp, outputs=[probs, reward])\n",
    "        model = Model(inputs = inp, outputs=probs)\n",
    "#         model.compile(optimizer='adam', loss=['categorical_crossentropy', 'mae'], metrics=['accuracy'])\n",
    "        model.compile(optimizer='adam', loss=['categorical_crossentropy'], metrics=['accuracy'])\n",
    "        return model\n",
    "    \n",
    "    # Loggers definition\n",
    "    def add_log(self, tag, value, step):\n",
    "        self.logger.log_scalar(tag, value, step)\n",
    "    \n",
    "    def log_history(self, history):\n",
    "        def log(history, name, text=None):\n",
    "            if text is None:\n",
    "                text = name\n",
    "            for ind, el in enumerate(history[name]):\n",
    "                self.add_log(text, el, self.current_epoch + ind + 1)\n",
    "        log(history.history, 'val_loss')\n",
    "        log(history.history, 'val_acc')\n",
    "        log(history.history, 'loss')\n",
    "        log(history.history, 'acc')\n",
    "        log(history.history, 'lr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_name = 'res_blocks_imitation'\n",
    "agent_id = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use SimpleAgent\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_2 (InputLayer)             (None, 11, 11, 18)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)                (None, 11, 11, 256)   41728       input_2[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNorm (None, 11, 11, 256)   1024        conv2d_9[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "activation_9 (Activation)        (None, 11, 11, 256)   0           batch_normalization_9[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)               (None, 11, 11, 256)   590080      activation_9[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNor (None, 11, 11, 256)   1024        conv2d_10[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_10 (Activation)       (None, 11, 11, 256)   0           batch_normalization_10[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)               (None, 11, 11, 256)   590080      activation_10[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNor (None, 11, 11, 256)   1024        conv2d_11[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "add_4 (Add)                      (None, 11, 11, 256)   0           activation_9[0][0]               \n",
      "                                                                   batch_normalization_11[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_11 (Activation)       (None, 11, 11, 256)   0           add_4[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)               (None, 11, 11, 256)   590080      activation_11[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNor (None, 11, 11, 256)   1024        conv2d_12[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_12 (Activation)       (None, 11, 11, 256)   0           batch_normalization_12[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)               (None, 11, 11, 256)   590080      activation_12[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNor (None, 11, 11, 256)   1024        conv2d_13[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "add_5 (Add)                      (None, 11, 11, 256)   0           activation_11[0][0]              \n",
      "                                                                   batch_normalization_13[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_13 (Activation)       (None, 11, 11, 256)   0           add_5[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)               (None, 11, 11, 256)   590080      activation_13[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNor (None, 11, 11, 256)   1024        conv2d_14[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_14 (Activation)       (None, 11, 11, 256)   0           batch_normalization_14[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)               (None, 11, 11, 256)   590080      activation_14[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNor (None, 11, 11, 256)   1024        conv2d_15[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "add_6 (Add)                      (None, 11, 11, 256)   0           activation_13[0][0]              \n",
      "                                                                   batch_normalization_15[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "activation_15 (Activation)       (None, 11, 11, 256)   0           add_6[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)               (None, 11, 11, 2)     514         activation_15[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNor (None, 11, 11, 2)     8           conv2d_16[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_16 (Activation)       (None, 11, 11, 2)     0           batch_normalization_16[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)              (None, 242)           0           activation_16[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "actions (Dense)                  (None, 6)             1458        flatten_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "reward (Dense)                   (None, 1)             243         flatten_2[0][0]                  \n",
      "====================================================================================================\n",
      "Total params: 3,591,599\n",
      "Trainable params: 3,588,011\n",
      "Non-trainable params: 3,588\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "agent = PretrainedAgent(env.action_space.n, \n",
    "                        checkpoint_path='./models/res/' + model_name + '.h5', \n",
    "                        log_path='./logs/res/' + model_name + '/', \n",
    "                        character=config[\"agent\"](agent_id, config[\"game_type\"]),\n",
    "                        use_history=False,    # Use previous observations for predictions\n",
    "                        use_2d=True,         # Use 2d convolutions\n",
    "                        patient=True,        # Wait to make initial observations (you don't need it if you don't use history)\n",
    "                        center_view=False,    # Use centering\n",
    "                        original_view=True, # Use 11x11 board, if false, use 21x21\n",
    "                        verbose=False        # Comment actions\n",
    "                       )\n",
    "agent.evaluation_model = agent.pretraining_network\n",
    "agent.pretraining_network.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying to load weights\n",
      "Weights were loaded successful\n"
     ]
    }
   ],
   "source": [
    "agent.load_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Copy weights to the RL network\n",
    "for i in range(len(agent.rl_network.layers)):\n",
    "    agent.rl_network.layers[i].set_weights(agent.pretraining_network.layers[i].get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 1024\n",
    "epochs = 15\n",
    "early_stopping = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(670725, 21, 21, 72) (670725, 6)\n",
      "Train on 536580 samples, validate on 134145 samples\n",
      "Epoch 1/15\n",
      "536580/536580 [==============================] - 86s - loss: 1.2235 - acc: 0.5088 - val_loss: 1.1002 - val_acc: 0.5529\n",
      "Epoch 2/15\n",
      "536580/536580 [==============================] - 73s - loss: 1.0291 - acc: 0.5811 - val_loss: 1.0211 - val_acc: 0.5812\n",
      "Epoch 3/15\n",
      "536580/536580 [==============================] - 67s - loss: 0.9295 - acc: 0.6179 - val_loss: 0.9755 - val_acc: 0.6016\n",
      "Epoch 4/15\n",
      "536580/536580 [==============================] - 68s - loss: 0.8516 - acc: 0.6492 - val_loss: 0.9294 - val_acc: 0.6139\n",
      "Epoch 5/15\n",
      "536580/536580 [==============================] - 69s - loss: 0.7674 - acc: 0.6843 - val_loss: 0.9761 - val_acc: 0.5952\n",
      "Epoch 6/15\n",
      "536580/536580 [==============================] - 71s - loss: 0.6901 - acc: 0.7181 - val_loss: 0.9666 - val_acc: 0.6093\n",
      "Epoch 7/15\n",
      "536580/536580 [==============================] - 71s - loss: 0.6028 - acc: 0.7575 - val_loss: 1.0559 - val_acc: 0.6000\n",
      "Epoch 8/15\n",
      "536580/536580 [==============================] - 68s - loss: 0.5054 - acc: 0.8004 - val_loss: 1.2340 - val_acc: 0.5967\n",
      "Epoch 9/15\n",
      "536580/536580 [==============================] - 71s - loss: 0.4046 - acc: 0.8439 - val_loss: 1.2675 - val_acc: 0.5961\n",
      "Epoch 10/15\n",
      "536576/536580 [============================>.] - ETA: 0s - loss: 0.3183 - acc: 0.8804\n",
      "Epoch 00009: reducing learning rate to 0.0004500000213738531.\n",
      "536580/536580 [==============================] - 71s - loss: 0.3183 - acc: 0.8804 - val_loss: 1.4483 - val_acc: 0.5944\n",
      "Epoch 11/15\n",
      "536580/536580 [==============================] - 70s - loss: 0.2225 - acc: 0.9204 - val_loss: 1.6318 - val_acc: 0.5924\n",
      "Epoch 12/15\n",
      " 67584/536580 [==>...........................] - ETA: 51s - loss: 0.1593 - acc: 0.9484Trying to load weights\n",
      "Weights was loaded successful\n",
      "learning stopped\n",
      "Trying to load weights\n",
      "Weights was loaded successful\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    agent.pretrain(observations, labels, batch_size=batch_size, epochs=epochs, early_stopping=early_stopping)\n",
    "except KeyboardInterrupt:\n",
    "    agent.load_weights()\n",
    "    print(\"learning stopped\")\n",
    "# Load the best model\n",
    "agent.load_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-training evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "100/100\r"
     ]
    },
    {
     "data": {
      "image/png": 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VQoAkz0xyx0DZvYFFtKEPJwDvr6rj+7IXA38CvGZg+MQdSbaeqgORJEmSpNkiVTXdddAaJsmjgMuA3wBvrapPTnN9XkO7JeS6wPZVdcV01keSJEmSZjITCZIkSZIkaWgObZAkSZIkSUMzkSBJkiRJkoZmIkGSJEmSJA3NRIKmXJJ5SarfOeHAGVCff0lyZ6/TnOmujyRJkiTNZCYSNJ02qqqjx1uYZJMkX+gn+VclecXKNphk7SSXJlkyzvL9esLgtSOxqjoU2GGVjkCSJEmS1jBefdVM9jHgbmAzYEfg9CQ/qKqFK1jnrcBSYIPRC5JsDLwDWNH6kiRJkqQVsEeCZqQk6wEvAd5VVXdU1XnAacCrVrDONsArgX8dp8i/AkcCN0xydSVJkiRpjWEiQTPVdsA9VfXTgdgPWPEQhH+j9Tj49egFSXYGdgL+fTIrKUmSJElrGhMJmqnWB24bFbuVMYYsACR5MbBWVX1hjGVrAR8HDq6q+ya7opIkSZK0JjGRoBkhyZn9Lg53JNkXuAPYcFSxDYHbx1h3PeADwJvG2fwbgR9W1fmTWWdJkiRJWhM52aJmhKraY/B1Tw7MSTK/qi7v4Scw9kSJ84F5wLeTAKwNPCzJtcAuwHOAZyV5fi+/CfDEJDtW1cGTfjCSJEmSNIulqqa7DlrDJJkH/Bx4cFXds4JyJwEFvJZ214YzgKeNvmtDkjnApgOhpwEfBZ7Esjs4rDuw/FTgFOCYqrp1InWSJEmSpDWdPRI0k70ROBa4HrgReMNIEiHJM4Ezq2r9fuJ/7chKSW4C7quqkdgtgxtNcjdw20gSQZIkSZI0PHskaMoleRRwGfAb4K1V9clprs+hwJuBdYD1qure6ayPJEmSJM1kJhIkSZIkSdLQvGuDJEmSJEkamokESZIkSZI0NBMJekAlmZekktyR5MAZUJ/n9rrcl+S5PfYvSe7s9ZzTYxcm2WF6aytJa6Yk30nyxOmux0yT5ENJ3jDd9ZBmsyRr9d+KW093XaSZzESCpspGVXX0eAuTbJLkC/2E/qokr1jZBpOsneTSJEtGxf88yXeT3JbkisEERlV9rarWB34xEDsUGJ00+CDwnmEPTtLMkORbSW5Oss4U7rOSbDtV+5vpBhLIq3RnqCR7ArdX1fcGYo9O8pUktye5IckHBpZ9Osk1vc3/aZLXjtrec5L8JMmvknyzT/g7smyTJJ9LcmPf7meSbLgKdf7W6P0+QD4IvCPJ2lOwL2m10E/6Rx73Jfn1wOt9J7q9qrq33xXsFysvPTmSvDbJt6Zqf6uDJO9Lctx010PjM5GgmeJjwN3AZsC+wFFD9Ah4K7B0MJDkwcAXgE8ADwP+CvhwkidMsD6nAX+W5BETXE/SNEkyD3gmUMBfTGtl9Pt4PfCpkRf9pPls4BvAI4AtgU8PlP9XYF5VbUj7u78vyZP7upsCpwLvAjYBFgCfG1j3fcDGwDbAY2jfQe9+IA5qMlTVNcBP8PMt/U4/6V9/4ELRngOxz4wuv6pJTknLM5GgaZdkPeAlwLuq6o6qOo92Iv+qFayzDfBK2g/IQZsAGwKfquYi4FJg+4nUqap+A1wM7DaR9SRNq/2A84HjgP0HFyR5eJIv96vWF/UrHecNLH9ckrOT3JTksiQvH1h2XJKPJTm9XxG/IMlj+rJze7Ef9KtffzW6Ukle3bvqH5Hklt5T6mk9vjjJ9Un2Hyj/sCQnJFnae2j9U5IHJVmnr//4gbJz+9W3P+ivX5jk+73cfyf544GyVyZ5a5If9t5fxyTZLMmZ/bi+lmTjgfK79G3ckuQHSZ49sOxbSd7bj+v2JF/tJ+0AI+/JLf09eWqSbZOck+TWfuV/8GR+8L1aG/hz4JyB8KuBX1bVh6vqzqr6TVX9cGRhVS2sqrtGXvbHY/rrvwQWVtX/6+36u4EnJHlcX74N8MWquq2qbqUlosdMYidZt/d+uLG/Jxf19+8wWgLro/14P9rLr+wz9e99+e39vXlUX5b+Wbm+f15/NPg3B74FvGCsOkq6v97efy7JiUluB17Z26Xz+//la5IcmXYxiiRz0npVzeuvP92Xj7SV/9N/h461r4cm+exAO3HhSNuYZKMk/9n3tyTJe3rb/kfAR4Fn9jbkhnG2fV5f5/zehn8x7bvtxN5WXJCB4RhJnpFkQW93L0zylB7fN8n5o7b91iSn9ufrJvlw2vfTdUk+nmTdvuy5/bvk7WnfUb9Msmfad8/lvb1728B2H5TkHUl+1tv+k0a+Z/r3QiXZr78fS5Mc0pe9EHgbsG9/Ty7u8QP6/m9P+y7de4IfB02mqvLh4wF7APNoP+rmrKDME4FfjYr9L+DLK1jnK8CLgWcDS0Yt+yxwELAW8FTgemCrUWWuBJ67onoCRwIfnu730IcPH8M9gEXAG4EnA78FNhtYdlJ/PJSWWFwMnNeXrddfvwaY09ukG4Dt+/LjgBuBnfvyzwAnDWy7gG1XUK9XA/f07a9Fuwr+C1pPrHWAXYHbgfV7+ROALwEb9Lbpp8ABfdmxwGED2z4I+K/+/Im9vXtK38/+va1bpy+/kpZo2QzYopf9bl9vXdoV/0N72S36MT+fdtHhef313L78W8DPgO2Ah/TXh/dlY7WnJwLv7NtaF3jGOO/VDsCdo2LH0noonNn/Lt8C/mhUmY8Dv+r7/e7Ae/kR4KhRZX8MvKQ/fyFwBq1Xwsb9Pfj7cer2N8CXaZ+htWifsw0H3o/XDpQd5jN1O/Cn/TPwEZZ9HnejJbI3AgL8IfDIgW3/JfDd6f7/5sPHTHww6vddj72P1ut1z94GPQT4k95WzgEeTWtnD+7l5/S2ZF5//en+/3cn4MG0Xk2fHmf/BwFf7PtYq68z0h59ubdVD6W1wxezrG1/LfCtlRzbecBlvb4b03onXQb8Wa/zZ4FP9rKbArcC+/Rlr6K14RsD6wN3Ao8e2Pb3gJf25/9GS6puTLs4dwbw3r7subTvs3f29+INtO+ST/ft/jHwG2DrXv4twHdo3ynrAv9Bu9gHsG1/n/+9L3sScBcwf+DvdtxAHTfsxzSy/JH0NtXH9DzskaCZYH3gtlGxW2k/ou8nyYuBtarqC+Ns70Tgn2mN0beBd1bV4lWo1+20H3KSZrgkzwAeBZxcVRfTTnJf0ZetRev1dGhV/aqqLgGOH1j9hcCVVfWfVXVPtbH5nwdeNlDmC1V1YVXdQ0sk7DjBKv68b/9e2o/QrYD3VNVdVfVV2o/cbXtd9wbeXlW3V9WVwIdY1kPrs335iFf0GMCBwCeq6oJqY3yPp7WDuwyU/7equq6qrqa1jxdU1feqXa3/Au2EF1qPrzOq6oyquq+qzqYNC3j+wLb+s6p+WlW/Bk5eyXvyW9rfZ/NqPQrOG6fcRrS2d9CW/ZiPBDYHTge+lIF5AqrqjbTvjGfShjKM9FBYn/Z9Mmjw++W7wNq0H9g3AvfSfuiPdwwPpyWN7q2qi6tq9HfXiGE+U6dX1bnVelO8E3hqkq36fjYAHgekqi6tNqRhhN9N0sSdV1Vf7u3Zr6vqot5W3lNVVwBHA89awfqnVNWCqvotK/4O+C3tJH6knVhQVXck2YJ2Ev4P/XvoOuD/snx7Poxjq+qKqroZOAv4aVV9s383/T+WteF70npjndiP8VPAFcALquoOWlJjb2i9p2jJia8keRDwOlpC9ebexv3rqHr+hpY4/i0tQT8XOKJar+If0pIbI73hXg+8o6qu7t8z/wK8rO9nxLv798J3gYXAioYjF/D4JOtW1TX9+1zTxESCplzvGjY4Cc4dtCzjoA25/4/JkWEQHwDeNM62H0dr1Paj/TjcAXhbklXpBroBcMsqrCdp6u0PfLWqRrqEfpZlwxvm0q7IDCYUB58/CnhK74Z6S5JbaHO1DM6Rcu3A81/RTlAn4rqB578G6D8kB2Pr036APhi4amDZVbSrOQDfBB6a5Cm92+2OtATAyHG8ZdRxbEU7+R6vHmPVYWRbLxu1rWfQrgCNmMh78jba1fULkyxM8tfjlLuZ+yeRf007CTizqu6mTTj4cNqV+t/pP9rPoyUeRu5ssLLvl5NpVyI36PGfsfz8C4M+RfvhflLvzvuBka7QYxjmM/W7z2D/YX8TLdHyDVo3548B1yc5OstPAOl3kzRxy11QSht6dHqSa5PcRptge9OxVwWGb++OA74GnJzk6iSHp83J8Cha76PrBtqEj9F6JkzEsG345iz/PQLLf5d8ltZbAVrbdGo/0X9Er+cPBur5FeAPBrZzQ0+Kj+xzrHqN1GNr4MsD2/pRj/9ue1U11Hvbkxr70Hp9XJs2Ae92Y5XV1DCRoClXVXvU8pPg/BSYk2T+QLEn0LKSo82ndZv9dpJraVeeHtm/COYBj6dlZ8/qWefLaFev9liFqv4h8INVWE/SFEryEODlwLN6W3At8A+0sfBPoE3Keg/tBHPEVgPPFwPnVNVGA4/1q2o6brN3A8uu3o/YGrga2sky7eR3n/74SlWNnBQvpg17GDyOh1bViatQj8W07qeD21qvqg4fYt26X6Dq2qp6XVVtThsi8PGMfaeLRbRpArYYiP1wrG2uwByWzZGw3NWtnox+DMu+X3ak9eK4s5/M/zvL97oYPIbfVtW/VNX2wNNovQ72G1k8qvgwn6nffQaTrE+b4+eXfV9HVtWTacNwtqNNLjzC7yZp4kb/H/0EbZjTttUmav1nWrLz99tJ1d1V9e6q+kNa8vXFtBP1xbST5E0G2oQNq2rkyv1E2rhh/JLlv0dg4LsE+C9giz4/wz4s69l2Ha2H3GMH6vmwqnrYKtZjCfC8UW3huqOSB+MZ67vkzKp6Li2pvYj2d9Q0MZGgaVdVd9ISAu9Jsl6SpwN7MTBr94Af03587dgfr6U1ejvSGunvAfPTbgGZtAnRXkj7ITq0PqnMk2kzhUua2V5E65K+Pcvahj+kdd3fr598nwq8O20irMex7AQQ2tWW7ZK8KsmD++NPkvwhw7mO1i309zaQKDgsyQZpE/C9meWvkn+WdkeafVn24w/gk8Dre2+F9Pb0BUnGHCa2Ep8G9kyyW9o91ddN8uwkW650zZa4uY+B9yTJywbWvZn2A/G+0Sv2HgdfY/kuxp8Gdkmb5Gst4O9pCZdLk/xBkr2TrN/ruRvtR/HX+7pfoHWDfUlv1/8Z+GFV/aQvvwh4bZKH9ITUgYzzfZHkz5L8Ua/DbbSEz8gxjP4MDPOZen7aZGhrA+8Fzq+qxb3cU3pvhztp3YgH36tn0eaLkLTqNqANc7qz/7/8m8nYaP/9+fjedf937US1IbbnAB9MsmHaJITbJvnTvup1wJYr6OU0UV8BdkjyV2mTR76CNifB6fC7tvbzwIdpc7p8o8fvpc1j8H/TJvNNki2T7LqK9fh34H+nTwLZ2+xh7zpzHTAvSfq6j0yb2PGhtGTHnYzxPaKpYyJBM8UbaRPTXE+b4+ANVbUQIMkzk9wB0Md5XTvyoHUFva+/vreqfgb8NW0s7W20RvvztEZxIvakTXrzy8k4OEkPqP1p4/V/Map9+Chtxuc5wMG0W8JeS0tSnkgfR9+v6O9KGwP6y17m/bTuncN4N3B877r58pUVHsLf0n4gXUGbXOuztAkH6fW9oC/fnIETyqpaQBvb+lHayfoi2kSPE9Z/9O4FvIOWGFhMuyq+0t8NVfUr4DDgO/092YU2sdkFvS0/Dfi7Pi55LJ9g4K49vWfZK2k/SG/u9fqL/kO4aMMYlvRlH6SN7T2tr7uUNj/GYX35U1h+rO9f03q5LaFdqXs0o+74MeARwCm075ZLad8vIwnvjwAvTXJzkiOH/Ex9FjiU9j325H6M0IZYfLLX9yra3A3/B9oPaVrC7Ivj1FHScN5C+79+O63NGfNOMqtgc1ri+jZaz6evsSzh+0raSfsltP/f/49lw53OBi6nDX0Y5mr9CvW27y+Af6S1If8AvLDPrTDis7R5G04eGKoA7b25CriQlmz5Kq1H8Kr4MK33w9fT7pjx37Tvg2F8jjZM+aYkF9Imr3wrcE0/pqfRhjlomqRqsnvSSMv0q2mX0a6ovLWqPjnN9XkOLbGwDvD8qvpmkkNpV/zWAdarqnuTXECbSffH01hdSQ+QJO8HHlFV4500ahol+Q5tBvUMAvR/AAAgAElEQVTvTXddHghJjqPdceifJrjeh4CfVdV4E0JKkjQlTCRIkma9PpxhbdpET39Cu53Va6vKK7uacquaSJAkaaaYM90VkCRpCmxAG86wOW3c5YeAL01rjSRJklZT9kiQJEmSJElDc7JFSZIkSZI0tCkd2rDpppvWvHnzpnKXkrRSF1988Q1VNXe66zEVbIclzVS2xZI0vSbSDk9pImHevHksWLBgKncpSSuV5KrprsNUsR2WNFPZFkvS9JpIO+zQBkmSJEmSNDQTCZIkSZIkaWgmEiRJkiRJ0tBMJEiSJEmSpKGZSJAkSZIkSUMzkSBJkiRJkoZmIkGSJEmSJA3NRIIkSZIkSRqaiQRJkiRJkjQ0EwmSJEmSJGloJhIkSZIkSdLQ5kx3BTS7zTvk9OmuwoRdefgLprsK0owzE/8v+39V0prEdljSTGKPBEmSJEmSNDQTCZIkSVqtJTk2yfVJfjwq/rdJfpJkYZIPDMTfnmRRksuS7DYQ373HFiU5ZCC+TZILevxzSdbu8XX660V9+bwH/mglafqZSJAkSdLq7jhg98FAkj8D9gKeUFU7AB/s8e2BvYEd+jofT7JWkrWAjwF7ANsD+/SyAO8HjqiqbYGbgQN6/ADg5h4/opeTpFnPRIIkSZJWa1V1LnDTqPAbgMOr6q5e5voe3ws4qaruqqqfA4uAnftjUVVdUVV3AycBeyUJ8OfAKX3944EXDWzr+P78FOA5vbwkzWomEiRJkjQbbQc8sw85OCfJn/T4FsDigXJLemy8+MOBW6rqnlHx5bbVl9/ay99PkgOTLEiyYOnSpb/3wUnSdDKRIEmSpNloDrAJsAvwVuDk6ewtUFVHV9VOVbXT3Llzp6sakjQpTCRIkiRpNloCnFrNhcB9wKbA1cBWA+W27LHx4jcCGyWZMyrO4Dp9+cN6eUma1UwkSJIkaTb6IvBnAEm2A9YGbgBOA/bud1zYBpgPXAhcBMzvd2hYmzYh42lVVcA3gZf27e4PfKk/P62/pi//Ri8vSbPanJUXkSRJkmauJCcCzwY2TbIEOBQ4Fji23xLybmD/fpK/MMnJwCXAPcBBVXVv387BwFnAWsCxVbWw7+IfgZOSvA/4HnBMjx8DfCrJItpkj3s/4AcrSTOAiQRJkiSt1qpqn3EWvXKc8ocBh40RPwM4Y4z4FbS7OoyO/wZ42YQqK0mzgEMbJEmSJEnS0EwkSJIkSZKkoZlIkCRJkiRJQzORIEmSJEmShmYiQZIkSZIkDc1EgiRJkiRJGpqJBEmSJEmSNDQTCZI0xZJsleSbSS5JsjDJ3/X4u5NcneT7/fH8gXXenmRRksuS7DYQ373HFiU5ZDqOR5IkSWuWOdNdAWl1Nu+Q06e7ChN25eEvmO4qCO4B3lJV302yAXBxkrP7siOq6oODhZNsD+wN7ABsDnwtyXZ98ceA5wFLgIuSnFZVl0zJUUiSJGmNZCJBkqZYVV0DXNOf357kUmCLFayyF3BSVd0F/DzJImDnvmxRVV0BkOSkXtZEgiRJkh4wDm2QpGmUZB7wROCCHjo4yQ+THJtk4x7bAlg8sNqSHhsvPnofByZZkGTB0qVLJ/kIJEmStKYxkSBJ0yTJ+sDngb+vqtuAo4DHADvSeix8aDL2U1VHV9VOVbXT3LlzJ2OTkiRJWoM5tEGSpkGSB9OSCJ+pqlMBquq6geWfBL7SX14NbDWw+pY9xgrikiRJ0gPCHgmSNMWSBDgGuLSqPjwQf+RAsRcDP+7PTwP2TrJOkm2A+cCFwEXA/CTbJFmbNiHjaVNxDJIkSVpzrbRHQpKtgBOAzYACjq6qjyR5N/A6YGTA7Tuq6owHqqKSNIs8HXgV8KMk3++xdwD7JNmR1tZeCfwNQFUtTHIybRLFe4CDqupegCQHA2cBawHHVtXCqTwQSZIkrXmGGdowoduUSZJWrKrOAzLGonGTsVV1GHDYGPEzVrSeJEmSNNlWmkhYhduUSZIkSZKkWWpCcyQMeZuy0et42zFJkiRJkmaJoRMJq3qbMm87JkmSJEnS7DFUImG825RV1b1VdR/wSWDnB66akiRJkiRpJlhpImEVblMmSZIkSZJmqWHu2jCh25RJkiRJkqTZa5i7Nkz4NmWSJEmSJGl2mtBdGyRJkqSZpt9B7Pok9xtqm+QtSSrJpv11khyZZFG/+9iTBsrun+Ty/th/IP7kJD/q6xzZh/6SZJMkZ/fyZ493FzNJmm1MJEiSJGl1dxyw++hgkq2AXYFfDIT3AOb3x4G0O5GRZBPgUOAptEnEDx1IDBwFvG5gvZF9HQJ8varmA1/vryVp1jORIEmSpNVaVZ0L3DTGoiOAt9Hm9BqxF3BCNecDG/VJxHcDzq6qm6rqZuBsYPe+bMOqOr+qCjgBeNHAto7vz48fiEvSrDbMZIt6AM075PTprsKEXXn4C6a7CpIkSSuUZC/g6qr6QR+JMGILYPHA6yU9tqL4kjHiAJtV1TX9+bXAZiuoz4G0HhBsvfXWEz0cSZpR7JEgSZKkWSXJQ2l3Gfvnqdpn761QK1h+dFXtVFU7zZ07d6qqJUkPCBMJkiRJmm0eA2wD/CDJlcCWwHeTPAK4GthqoOyWPbai+JZjxAGu60Mf6P9eP+lHIkkzkIkESZIkzSpV9aOq+oOqmldV82jDEZ5UVdcCpwH79bs37ALc2ocnnAXsmmTjPsnirsBZfdltSXbpd2vYD/hS39VpwMjdHfYfiEvSrGYiQZIkSau1JCcC/wM8NsmSJAesoPgZwBXAIuCTwBsBquom4L3ARf3xnh6jl/mPvs7PgDN7/HDgeUkuB57bX0vSrOdki5IkSVqtVdU+K1k+b+B5AQeNU+5Y4Ngx4guAx48RvxF4zgSrK0mrPXskSJIkSZKkoZlIkCRJkiRJQzORIEmSJEmShmYiQZIkSZIkDc1EgiRJkiRJGpqJBEmSJEmSNDQTCZIkSZIkaWgmEiRJkiRJ0tBMJEiSJEmSpKGZSJAkSZIkSUMzkSBJkiRJkoZmIkGSJEmSJA3NRIIkSZIkSRqaiQRJkiRJkjS0OdNdAUmSJEmz17xDTp/uKtzPlYe/YLqrIK3W7JEgSZIkSZKGZiJBkiRJkiQNzUSCJEmSJEkamokESZIkSZI0NBMJkiRJkiRpaCYSJEmSJEnS0EwkSJIkSZKkoZlIkCRJkiRJQ5sz3RWQpDVNkq2AE4DNgAKOrqqPJNkE+BwwD7gSeHlV3ZwkwEeA5wO/Al5dVd/t29of+Ke+6fdV1fFTeSySNBMkORZ4IXB9VT2+x/4PsCdwN/Az4DVVdUtf9nbgAOBe4E1VdVaP705rb9cC/qOqDu/xbYCTgIcDFwOvqqq7k6xDa8+fDNwI/FVVXTklBy2NY94hp093Fe7nysNfMFS51bnuaxp7JEjS1LsHeEtVbQ/sAhyUZHvgEODrVTUf+Hp/DbAHML8/DgSOAuiJh0OBpwA7A4cm2XgqD0SSZojjgN1Hxc4GHl9Vfwz8FHg7QG9v9wZ26Ot8PMlaSdYCPkZrc7cH9ullAd4PHFFV2wI305IQ9H9v7vEjejlJmvVMJEjSFKuqa0Z6FFTV7cClwBbAXsBIj4LjgRf153sBJ1RzPrBRkkcCuwFnV9VNVXUz7Ufz6B/SkjTrVdW5wE2jYl+tqnv6y/OBLfvzvYCTququqvo5sIiWjN0ZWFRVV1TV3bQeCHv1XmF/DpzS1x/dPo+026cAz+nlJWlWM5EgSdMoyTzgicAFwGZVdU1fdC1t6AO0JMPigdWW9Nh48dH7ODDJgiQLli5dOqn1l6TVxF8DZ/bnE21THw7cMpCUGGxrf7dOX35rL38/tsWSZhMTCZI0TZKsD3we+Puqum1wWVUVbf6E31tVHV1VO1XVTnPnzp2MTUrSaiPJO2lDyj4znfWwLZY0m6w0kZBkqyTfTHJJkoVJ/q7HN0lydpLL+7+Oy5WkISV5MC2J8JmqOrWHr+tDFuj/Xt/jVwNbDay+ZY+NF5ckAUleTZuEcd+eoIWJt6k30oaUzRkVX25bffnDenlJmtWG6ZEw0UnBJEkr0MfPHgNcWlUfHlh0GrB/f74/8KWB+H5pdgFu7UMgzgJ2TbJxT+bu2mOStMbrd2B4G/AXVfWrgUWnAXsnWaffjWE+cCFwETA/yTZJ1qZNyHhaT0B8E3hpX390+zzSbr8U+MZAwkKSZq2V3v6x/1i9pj+/PcngpGDP7sWOB74F/OMDUktJml2eDrwK+FGS7/fYO4DDgZOTHABcBby8LzuDduvHRbTbP74GoKpuSvJe2o9fgPdU1XKTjUnSmiDJibTfpZsmWUK7o83bgXWAs/v8h+dX1euramGSk4FLaBfMDqqqe/t2DqYlZNcCjq2qhX0X/wiclOR9wPdoyWD6v59Ksog22ePeD/jBStIMsNJEwqAhJwUbvc6BtNuVsfXWW69qPSVp1qiq84DxZvV+zhjlCzhonG0dCxw7ebWTpNVPVe0zRviYMWIj5Q8DDhsjfgYteTs6fgXtrg6j478BXjahykrSLDD0ZIurOimYE8tIkiRJkjR7DJVImOCkYJIkSZIkaZYa5q4NE50UTJIkSZIkzVLDzJEw0UnBJEmSJEnSLDXMXRsmNCmYJEmSJEmavSZ01wZJkiRJWhPMO+T06a7C/Vx5+AumuwoSMIG7NkiSJEmSJJlIkCRJkiRJQzORIEmSJEmShuYcCZLGNRPHBq6MYwclSZKkB5Y9EiRJkiRJ0tBMJEiSJEmSpKGZSJAkSZIkSUMzkSBJkiRJkoZmIkGSJEmSJA3NRIIkSZIkSRqaiQRJkiRJkjQ0EwmSJEmSJGloJhIkSZIkSdLQTCRIkiRJkqShmUiQJEmSJElDM5EgSZIkSZKGZiJBkiRJq7Ukxya5PsmPB2KbJDk7yeX93417PEmOTLIoyQ+TPGlgnf17+cuT7D8Qf3KSH/V1jkySFe1DkmY7EwmSJEla3R0H7D4qdgjw9aqaD3y9vwbYA5jfHwcCR0FLCgCHAk8BdgYOHUgMHAW8bmC93VeyD0ma1UwkSJIkabVWVecCN40K7wUc358fD7xoIH5CNecDGyV5JLAbcHZV3VRVNwNnA7v3ZRtW1flVVcAJo7Y11j4kaVYzkSBJkqTZaLOquqY/vxbYrD/fAlg8UG5Jj60ovmSM+Ir2cT9JDkyyIMmCpUuXrsLhSNLMYSJBkiRJs1rvSVDTuY+qOrqqdqqqnebOnftAVkWSHnAmEiRJkjQbXdeHJdD/vb7Hrwa2Gii3ZY+tKL7lGPEV7UOSZjUTCZIkSZqNTgNG7rywP/Clgfh+/e4NuwC39uEJZwG7Jtm4T7K4K3BWX3Zbkl363Rr2G7WtsfYhSbPanOmugCRJkvT7SHIi8Gxg0yRLaHdfOBw4OckBwFXAy3vxM4DnA4uAXwGvAaiqm5K8F7iol3tPVY1M4PhG2p0hHgKc2R+sYB+SNKuZSJAkSdJqrar2GWfRc8YoW8BB42znWODYMeILgMePEb9xrH1I0mzn0AZJkiRJkjQ0EwmSJEmSJGloJhIkSZIkSdLQTCRIkiRJkqShmUiQJEmSJElDM5EgSZIkSZKGZiJBkiRJkiQNzUSCJE2xJMcmuT7Jjwdi705ydZLv98fzB5a9PcmiJJcl2W0gvnuPLUpyyFQfhyRJktZMJhIkaeodB+w+RvyIqtqxP84ASLI9sDewQ1/n40nWSrIW8DFgD2B7YJ9eVpIkSXpAzZnuCkjSmqaqzk0yb8jiewEnVdVdwM+TLAJ27ssWVdUVAElO6mUvmeTqSpIkScuxR4IkzRwHJ/lhH/qwcY9tASweKLOkx8aL30+SA5MsSLJg6dKlD0S9JUmStAZZaSJhomN5JUmr5CjgMcCOwDXAhyZrw1V1dFXtVFU7zZ07d7I2K0mSpDXUMD0SjmPIsbySpFVTVddV1b1VdR/wSZYNX7ga2Gqg6JY9Nl5ckiRJekCtNJFQVecCN01BXSRpjZXkkQMvXwyM9AI7Ddg7yTpJtgHmAxcCFwHzk2yTZG3ahIynTWWdJUmStGb6fSZbPDjJfsAC4C1VdfMk1UmSZrUkJwLPBjZNsgQ4FHh2kh2BAq4E/gagqhYmOZk2ieI9wEFVdW/fzsHAWcBawLFVtXCKD0WSJElroFVNJBwFvJf2g/e9tLG8fz1WwSQHAgcCbL311qu4O0maPapqnzHCx6yg/GHAYWPEzwAcWiZJkqQptUp3bVjBWN6xyjrJlyRJkiRJs8QqJRJWMJZXkiRJkiTNYisd2jCRsbySJEmSJGl2W2kiYaJjeSVJkiRJ0uy1SkMbJEmSJEnSmslEgiRJkiRJGpqJBEmSJEmSNDQTCZIkSZq1kvxDkoVJfpzkxCTrJtkmyQVJFiX5XJK1e9l1+utFffm8ge28vccvS7LbQHz3HluU5JCpP0JJmnomEiRJkjQrJdkCeBOwU1U9HlgL2Bt4P3BEVW0L3Awc0Fc5ALi5x4/o5UiyfV9vB2B34ONJ1kqyFvAxYA9ge2CfXlaSZjUTCZIkSZrN5gAPSTIHeChwDfDnwCl9+fHAi/rzvfpr+vLnJEmPn1RVd1XVz4FFwM79saiqrqiqu4GTellJmtVMJEiSJGlWqqqrgQ8Cv6AlEG4FLgZuqap7erElwBb9+RbA4r7uPb38wwfjo9YZL34/SQ5MsiDJgqVLl/7+BydJ08hEgiRJkmalJBvTeghsA2wOrEcbmjDlquroqtqpqnaaO3fudFRBkiaNiQRJkiTNVs8Ffl5VS6vqt8CpwNOBjfpQB4Atgav786uBrQD68ocBNw7GR60zXlySZjUTCZIkSZqtfgHskuShfa6D5wCXAN8EXtrL7A98qT8/rb+mL/9GVVWP793v6rANMB+4ELgImN/vArE2bULG06bguCRpWs1ZeRFJkiRp9VNVFyQ5BfgucA/wPeBo4HTgpCTv67Fj+irHAJ9Ksgi4iZYYoKoWJjmZloS4Bzioqu4FSHIwcBbtjhDHVtXCqTo+SZouJhIkSZI0a1XVocCho8JX0O64MLrsb4CXjbOdw4DDxoifAZzx+9dUklYfDm2QJEmSJElDM5EgSZIkSZKGZiJBkiRJkiQNzUSCJEmSJEkamokESZIkSZI0NBMJkiRJkiRpaCYSJEmSJEnS0EwkSJIkSZKkoZlIkCRJkiRJQzORIEmSJEmShmYiQZIkSZIkDc1EgiRJkiRJGpqJBEmSJEmSNDQTCZIkSZIkaWgmEiRJkiRJ0tBMJEiSJEmSpKGZSJAkSZIkSUMzkSBJkiRJkoZmIkGSJEmSJA3NRIIkSZIkSRqaiQRJkiRJkjQ0EwmSJEmSJGloJhIkSZIkSdLQTCRI0hRLcmyS65P8eCC2SZKzk1ze/924x5PkyCSLkvwwyZMG1tm/l788yf7TcSySNNMl2SjJKUl+kuTSJE+dzDY3yZOT/Kivc2SSTMdxStJUMpEgSVPvOGD3UbFDgK9X1Xzg6/01wB7A/P44EDgKWuIBOBR4CrAzcOjID2FJ0nI+AvxXVT0OeAJwKZPb5h4FvG5gvdHtuyTNOiYSJGmKVdW5wE2jwnsBx/fnxwMvGoifUM35wEZJHgnsBpxdVTdV1c3A2fjjVZKWk+RhwJ8CxwBU1d1VdQuT1Ob2ZRtW1flVVcAJA9uSpFlrpYmEiXTBlSStss2q6pr+/Fpgs/58C2DxQLklPTZe/H6SHJhkQZIFS5cundxaS9LMtg2wFPjPJN9L8h9J1mPy2twt+vPR8fuxLZY0m8wZosxxwEdpGdYRI93BDk9ySH/9j5NfPUla81RVJalJ3N7RwNEAO+2006RtVw+ceYecPt1VGNOVh79guqsgTdQc4EnA31bVBUk+wrJhDMDkt7njsS2WNJustEfCBLvgSpJWzXW9iyz93+t7/Gpgq4FyW/bYeHFJ0jJLgCVVdUF/fQotsTBZbe7V/fnouCTNaqs6R8J43cHux25ckjSU04CRWcD3B740EN+vzyS+C3Brb3/PAnZNsnEfXrZrj0mSuqq6Flic5LE99BzgEiapze3LbkuyS79bw34D25KkWWuYoQ0rtLLuYHbjkqTlJTkReDawaZIltJnADwdOTnIAcBXw8l78DOD5wCLgV8BrAKrqpiTvBS7q5d5TVaN7j0mS4G+BzyRZG7iC1o4+iMlrc99IGwr8EODM/pCkWW1VEwnXJXlkVV0zqjuYJGklqmqfcRY9Z4yyBRw0znaOBY6dxKpJ0qxTVd8Hdhpj0aS0uVW1AHj871lNSVqtrOrQhvG6g0mSJEmSpFlsmNs/ngj8D/DYJEt6F7DDgecluRx4bn/9/9u78zA7qjr/4+8PJARCRLbAIAHCYFyiQIAQYYCRRZBFCAoKGRdkdPgxLIojM8YVBFFwAWV0ZFAi6wgMi0SIQAaDgLKFLQsRiBiGIJsiCDKs+f7+OOcm1Tf3dld337X783qefrr2+z33Vp06derUKTMzMzMzMzMb4vp8tKE/TXDNzMzMzMzMbGgb6KMNZmZmZmZmZjYMuSLBzMzMzMzMzEpzRYKZmZmZmZmZleaKBDMzMzMzMzMrzRUJZmZmZmZmZlaaKxLMzMzMzMzMrDRXJJiZmZmZmZlZaa5IMDMzMzMzM7PSRrQ7ADMzs2YZP/2adoewkiWn7tfuEMzMzMwGxS0SzMzMzMzMzKw0VySYmZmZmZmZWWmuSDAzMzMzMzOz0lyRYGZmZmZmZmaluSLBzMzMzMzMzEpzRYKZmZmZmZmZleaKBDMzMzMzMzMrbUS7AzAzMzNrt/HTr2l3CCtZcup+7Q7BzMysJrdIMDMzMzMzM7PSXJFgZmZmZmZmZqW5IsHMzMzMzMzMSnNFgpmZmZkNaZJWlXSPpKvz+OaSbpe0WNIlklbL00fl8cV5/vjCNj6fpz8g6b2F6XvnaYslTW912szM2sEVCWZmZmY21H0aWFQYPw04IyLeDPwZ+ESe/gngz3n6GXk5JE0EDgXeAewN/EeunFgV+AGwDzARmJaXNTMb0lyRYGZmZmZDlqRxwH7Aj/O4gN2By/Ii5wEH5uGpeZw8f4+8/FTg4oh4OSJ+DywGpuS/xRHxcES8AlyclzUzG9JckWBmZmZmQ9l3gX8DluXx9YBnI+K1PL4U2DgPbww8CpDnP5eXXz69ap1601ci6QhJcyXNffrppwebJjOztnJFgpmZmZkNSZLeBzwVEXe1O5aIODsiJkfE5LFjx7Y7HDOzQRnR7gDMzMzMzJpkJ+AASfsCqwNrAd8D1pY0Irc6GAc8lpd/DNgEWCppBPBG4E+F6RXFdepNNzMbstwiwczMzMyGpIj4fESMi4jxpM4SfxkRHwbmAAfnxQ4DrsrDM/M4ef4vIyLy9EPzWx02ByYAdwB3AhPyWyBWy58xswVJMzNrK7dIMDMzM7Ph5nPAxZK+BtwDnJOnnwNcIGkx8AypYoCIWCjpUuB+4DXg6Ih4HUDSMcB1wKrAjIhY2NKUmJm1gSsSzMzMzGzIi4gbgRvz8MOkNy5UL/MS8ME6658CnFJj+ixgVgNDNTPreH60wczMzMzMzMxKc0WCmZmZmZmZmZXmigQzMzMzMzMzK80VCWZmZmZmZmZWmisSzMzMzMzMzKw0v7XBzMzMGmb89GvaHcJKlpy6X7tDMDMzG1LcIsHMrINIWiJpvqR7Jc3N09aVNFvSQ/n/Onm6JJ0pabGkeZK2bW/0ZmZmZjYcuCLBzKzz7BYRkyJich6fDtwQEROAG/I4wD7AhPx3BPDDlkdqZmZmZsPOoCoSat05MzOzhpsKnJeHzwMOLEw/P5LbgLUlbdSOAM3MzMxs+GhEHwm7RcQfG7AdMzODAK6XFMB/RsTZwIYR8Xie/wSwYR7eGHi0sO7SPO1xzMzMzMyaxJ0tmpl1lp0j4jFJGwCzJf22ODMiIlcylCbpCNKjD2y66aaNi9TMzMzMhqXBViTUunPWgwuwZmblRcRj+f9Tkq4EpgBPStooIh7Pjy48lRd/DNiksPq4PK16m2cDZwNMnjy5X5UQZtbZ/JYMMzNrh8F2trhzRGxL6vDraEl/X71ARJwdEZMjYvLYsWMH+XFmZkOXpDUlvaEyDOwFLABmAoflxQ4DrsrDM4GP5bc37AA8V3gEwszMzMysKQbVIqHOnbObGhGYmdkwtCFwpSRI+fN/RcS1ku4ELpX0CeAR4EN5+VnAvsBi4EXg8NaHbGZmZmbDzYArEvLdslUi4vnCnbOTGhaZmdkwExEPA1vXmP4nYI8a0wM4ugWhmZmZmZktN5gWCTXvnDUkKjMzMzMzMzPrSAOuSKh358zMzMzMzMzMhq7BdrZoZmZmZmZmZsOIKxLMzMzMzMzMrLRBvbXBzMzMzMzMbDgbP/2adoewkiWn7tfU7btFgpmZmZmZmZmV5ooEMzMzMzMzMyvNFQlmZmZmNiRJ2kTSHEn3S1oo6dN5+rqSZkt6KP9fJ0+XpDMlLZY0T9K2hW0dlpd/SNJhhenbSZqf1zlT+d3oZmZDmSsSzMzMzGyoeg34bERMBHYAjpY0EZgO3BARE4Ab8jjAPsCE/HcE8ENIFQ/ACcC7gCnACZXKh7zMPxXW27sF6TIzaytXJJiZmZnZkBQRj0fE3Xn4eWARsDEwFTgvL3YecGAengqcH8ltwNqSNgLeC8yOiGci4s/AbGDvPG+tiLgtIgI4v7AtM7MhyxUJZmZmZjbkSRoPbAPcDmwYEY/nWU8AG+bhjYFHC6stzdN6m760xvRan3+EpLmS5j799NODSouZWbu5IsHMzMzMhjRJY4DLgeMi4i/FebklQTQ7hog4OyImR8TksWPHNvvjzMyayhUJZmZmZjZkSRpJqkS4KCKuyJOfzI8lkP8/lac/BmxSWH1cntbb9HE1ppuZDWmuSDAzMzOzISm/QeEcYFFEnF6YNROovHnhMOCqwvSP5bc37AA8lx+BuA7YS9I6uZPFvYDr8ry/SNohf9bHCtsyM0EbU1YAACAASURBVBuyRrQ7gL6Mn35Nu0PotyWn7tfuEMzMzMwMdgI+CsyXdG+e9gXgVOBSSZ8AHgE+lOfNAvYFFgMvAocDRMQzkk4G7szLnRQRz+Tho4BzgTWAX+Q/M7MhreMrEszMzMzMBiIibgFUZ/YeNZYP4Og625oBzKgxfS7wzkGEaWbWdfxog5mZmZmZmZmV5ooEMzMzMzMzMyvNFQlmZmZmZmZmVporEszMzMzMzMysNFckmJmZmZmZmVlprkgwMzMzMzMzs9JckWBmZmZmZmZmpbkiwczMzMzMzMxKc0WCmZmZmZmZmZXmigQzMzMzMzMzK80VCWZmZmZmZmZWmisSzMzMzMzMzKw0VySYmZmZmZmZWWmuSDAzMzMzMzOz0lyRYGZmZmZmZmaluSLBzMzMzMzMzEpzRYKZmZmZmZmZleaKBDMzMzMzMzMrzRUJZmZmZmZmZlaaKxLMzMzMzMzMrDRXJJiZmZmZmZlZaa5IMDMzMzMzM7PSXJFgZmZmZmZmZqUNqiJB0t6SHpC0WNL0RgVlZmblOS82M2sv58NmNtwMuCJB0qrAD4B9gInANEkTGxWYmZn1zXmxmVl7OR82s+FoMC0SpgCLI+LhiHgFuBiY2piwzMysJOfFZmbt5XzYzIYdRcTAVpQOBvaOiE/m8Y8C74qIY6qWOwI4Io++FXhg4OE23PrAH9sdRBM5fd3N6WudzSJibLuDGIgyeXGH5cOd9Lv3V7fG7rhbr1tjb3fcXZkXd2mZuN2/9UA57tbr1tgd98CUzodHNDuSiDgbOLvZnzMQkuZGxOR2x9EsTl93c/qsUTopH+7m371bY3fcrdetsXdr3N3CefHgOe7W69bYHXfzDebRhseATQrj4/I0MzNrHefFZmbt5XzYzIadwVQk3AlMkLS5pNWAQ4GZjQnLzMxKcl5sZtZezofNbNgZ8KMNEfGapGOA64BVgRkRsbBhkbVGRzQvayKnr7s5fdanLsyLu/l379bYHXfrdWvs3Rp3W3VhPgzd+1s77tbr1tgdd5MNuLNFMzMzMzMzMxt+BvNog5mZmZmZmZkNM65IMDMzMzMzM7PShmVFgqS9JT0gabGk6e2Op9EkzZD0lKQF7Y6l0SRtImmOpPslLZT06XbH1EiSVpd0h6T7cvq+2u6YBkrSepLuzX9PSHqsMP6bdsdnzSfpOEmj2x3HUCbpREnHtzuOgZL0QollPiVpkaSLJO0q6e9aEVtfJM2StHYfy9woaaXXeEmaJGnf5kUHksY3uhyQv/+rG7lNaz7nxc3nvLg9nA+317CrSJC0KvADYB9gIjBN0sT2RtVw5wJ7tzuIJnkN+GxETAR2AI4eYr/fy8DuEbE1MAnYW9IObY5pQCLiTxExKSImAWcBZ1TGI6LtJx9rieMAF15tsI4C9oyIDwO7Am3PPyQJeF9EPDvATUwCmlqANStwXmyN0FF5sfPh9ht2FQnAFGBxRDwcEa8AFwNT2xxTQ0XETcAz7Y6jGSLi8Yi4Ow8/DywCNm5vVI0TSaVWeGT+G3I9ohZrviX9q6Q7Jc3r5hYYw52kNSVdk1vTLJB0AvAmYI6kOXmZaZLm5/mnFdZ9QdIZuRXODZLGtisdOZ4edxAkHZ/vNt0o6Xu5Vc0CSVPaFN8XJT0o6RbgrXnaJEm35ePoSknrSNpA0l15/taSQtKmefx3kkZLOlfSmZJ+I+lhSQe3I005ppXyAklnAX8L/ELSZ4Ajgc/k32CXFsc3Xqk14/nAAuB1SevneV/O826R9NOqO5MfzC3NHpS0i9LrAU8CDsnpOKSJYY/Idw8XSbos/+Z7SLonH4szJI3KaVgi6Rs5prmStpV0Xd5Xjixsc618rD8g6SxJw7Es2bGcF7c0PufFLc6LnQ8v1xH58HDM/DcGHi2ML2UIXYgOJ5LGA9sAt7c3ksaStKqke4GngNkRMaTSVyRpL2ACqYJvErCdpL9vb1Q2QHsDf4iIrSPincB3gT8Au0XEbpLeBJwG7E76rbeXdGBed01gbkS8A/gVcELrwy9tdG5lcxQwo9UfLmk70jvqK3dSts+zzgc+FxFbAfOBEyLiKWB1SWsBuwBzgV0kbQY8FREv5nU3AnYG3gec2rLEFNTLCyLiSFbsR2fQs3XTzW0IdQLwH3lffSTHvj1wELA1qbVjdRPaERExhXRX+IR8E+MrwCU5HZc0Md635njfDvwF+BdSq8VDImJL0mvA/7mw/P/m/fvmvNzBpNZ/xUreKcCxpFadWwAfaGL81n/Oi1vAeXFb82Lnwx2SDw/HigQbAiSNAS4HjouIv7Q7nkaKiNdzBjIOmCLpne2OqYn2yn/3AHcDbyOdIKz7zAf2lHSapF0i4rmq+dsDN0bE0xHxGnARUKk0WgZUTuIXkgpSneqnsLzl11rq49nMJtgFuDIiXsx530xS4X/tiPhVXuY8Vny3vwF2yuNfz/93IRVQKn4WEcsi4n5gwxakoZZuyQseiYjbqqbtBFwVES/llnI/r5p/Rf5/FzC+yfFVezQifp2HLwT2AH4fEQ/macV9BdL+BOl4vj0ino+Ip4GXC/v6HblV5+uk46GTj9fhyHlxazgvbh/nwx2SD49ox4e22WPAJoXxcXmadQlJI0mVCBdFxBV9Ld+tIuLZ3Axxb1LzraFIwDci4j/bHYgNTkQ8KGlb0p2Zr0m6YTCba1BYA/UaPSvaVy8MV8fW7lj7chOpsLoZcBXwOVLM1xSWebkwrNaF1kO35AV/HcA6le/3dVpf7qreP58F1utl+Uqsy+i5XyxjRezddgwMK86LO5bz4sZxPtwh+/9wbJFwJzBB0ub5+ZhDWVHzYx1OkoBzgEURcXq742k0SWMrtY2S1gD2BH7b3qia6jrgH3MLEyRtLGmDNsdkA5Cby74YERcC3wK2BZ4H3pAXuQN4t6T1lTq9nUZqOgvpXFR5HvQfgFtaFnhtTwIbKL15ZBSpmWnFIQCSdgaeq3G3r9luAg6UtIakNwD7kwpVfy48p/pRVny3NwMfAR6KiGWk/nP2pf3fcbWyeUFxn+oUvwb2V3rrzhh67i/1tCodm0raMQ//A6lJ9XhJb87TivtKWVNyGWoV0vHQafvSsOa8uGWcF3dWXux8uA2GXYuEiHhN0jGkA2VVYEZELGxzWA0l6aek3lTXl7SU9CzQOe2NqmF2Ih1w83M/AgBfiIhZbYypkTYCzssn91WASyOiI17x0gwRcb2ktwO3pjoiXiCdaJ9qa2A2EFsC35K0DHiV9LzfjsC1kv6Qn82dDswh3fG4JiKuyuv+lXRS/BLpt29mp0d9iohXJZ1EKnA/Rs/KvJck3UPqCPUf2xDb3ZIuAe4jfVd35lmHAWcpveLtYeDwvPySXAF7U17uFmBcRPy5tZH3rh95wc+ByyRNBY5tUz8JPUTEnZJmAvNIFz7zgb4uauYA0/N57BtNfD73AdLbjWYA9wOfAm4D/lvSCNL+c1Y/t3kn8H3gzaR0XNm4cK0BnBe3JjbnxR2UFzsfbk8+rIhObwlkZmZDmaQXImJMu+Poi6QbgeMjYm67Y7HOImlMRLyQLx5uAo6I/IYhs27hvNi6mfPh1ht2LRLMzMzMGuxsSRNJz3Cf58KrmVnLOR9uMbdIMDMzMzMzM7PShmNni2ZmZmZmZmY2QF1fkZB7S/2VpM0k3S3pXkkLJR1ZZ/l1Jc2W9FD+v06eflBe72ZJ6+VpW+SOVCrrribpptwxRivStJ2kW3Nc8ySt1OmNpDMlvdDLtj4vabGkByS9N08bK+kWSQskHVhY9qrc229l/NuSdm9H+iTtUfg9byn0bDpU0idJp0h6UNIiSZ+qs63D8r76kKTD8rRRkq7N6TuqsOzZSq98qowfI6nhHRAV0rdqjuNZSVdXLbN7/v0WSDqv3jHTiemznoq/dx5fS9JSSd8vLHOtpPvyvn5WZdmq7SjnV4vz8VD8LU/Lv/eCquPkorzs1wvTvlR1XL9PqTOuunH3sp+ek+OeJ+ky5V6qa2xrpXwmT18iaX7Op+YWpp+Wt3l+YdpHJB1XGN9S0rkDjPtcSb/Pn3uvpEl14t5U0vU5j7lf0vje0i3p2PwbzFJ6qxGSdpZ0RmGbYyVdW+vz6qlK0zfzfrIo7w8rveJM0iWFtC1R7lhX0k455rmSJuRpa+c0rlJY/3+Uz+0DiK/ed35M3gdC0vp1trNbIe57Jb1U2Vfz/r9Snq8GlT0KadijXgxVy59RWOZBSc+WSMOAj0cbHDWoXChpSuG3vU/S+/P0TSTNyfnEQkmfLqzT7Pysz/JePgZ+opTf3idp18K8aXn6vPwZ6w827n7EXvO4rrGtXvM9STMlLSiMDyr2fqahzPe/p6S78vd8l3LZWQ0srzUw1vXyvvyCepZTRku6RtJv829xamFew8596v916YmSHtOK43Lfqvmb5rQcX4ijbdc4K4mIrv4DjgY+DawGjMrTxgBLgDfVWP6bwPQ8PB04LQ/fCIwm9Ux6bJ72U2BC1fonAB9uUZreUvl84E3A48DaheUmAxcAL9TZzkRSb7KjgM2B35HeVPGpnM7RwI152f2BE6vW3wy4vh3pAx4E3p6HjwLOHWLpOxw4H1glj29QYzvrknr8XRdYJw+vAxwAfIlUEXhrXnZr4Jyq9UcD9zQrfXl4j/zdXl2YvwrwKPCWPH4S8IluSZ//6v/eefx7wH8B3y9MWyv/F3A5cGiN7ewL/CIvswNwe56+HzCb1GfPmqSeiNcCtgJ+nJeZDbyR9FaTn1dtV8A9wOj+7KfFuPPw6eRzQ9UyNfOZPG8JsH7V8m8EZufhH5N6UF8DuAEYWbXs/wCbDiDuc4GDS/x2NwJ75uExle+oXrpJvUivko+//fN3ex2wbtV2fwLs1N99CPg70iu6Vs1/twK79rHud4Cv5OErgHHAzsB38rRvV2+D1Gv6Fweyj/fynW8DjK/1m9fZ5rqk17tVvvOaeT4NKntQdZzWiqGXdY8lvcGqbhoY5PHov8H90bhy4WhgRB7eiNQT/4g8vG2e/gZSGWwircnPypT3jgZ+koc3AO4i5VUjchrWz/O+CZw42Lj7EXuZslyv+R7wAdI5dUEeH3TsTfj+tyFfUwHvBB7Lww0rrzUw1jVJ54gj6VlOGQ3slodXI72Oc5883rBzH/2/Lj2R1HFove1dBvx3ZRnafI1T/df1LRKADwNXRcQrEfFynjaK+q0tpgLn5eHzgEptzrK83mjgVaV3wD4REQ9Vrf+z/JnNVEnTg5XPj4g/kDLLsQBKd/y+BfxbL9uZClwcES9HxO+BxcAU0uuARpPS+7rSXY7jSBnwchHxCLCepL9pZOIokT4gSBcTkDLVP9TYTjen75+BkyK9S5iIqPW6w/eSTibPRHo90Gxg70L6RpIyO4CTgS8XV46IF4ElkqY0MnHk9OXPuIH0Ht6i9YBXIuLBPD4bOKjGdjo1fdbT8t9b0nbAhsD1xQUi4i95cATp5Bk1tjMVOD+S24C1JW1EKqzeFBGvRcRfSa9uquwHayjdaR4JvE6qlDqh6rODdDFW/c7ovvbT5XHnu0Nr9BJ3rXymnmXAyLzN0TkdxwP/HhGvVi37c+DQ/sZdhlKHUyMiYnbe1gv5mOkt3SJ915W4PwL8IiKeqdp8f8+DlTQFqROs1Uj580jSa7rqpUHAh0gX1rAib6icp7cANomIG6tWnQlMG0B8ve0r90TEkn5s82DSd/diHq+X5zeq7LE8Db3EUM80VnzH9dYf7PFog9OQcmFEvBgRr+XR1cnHfkQ8Hrljuoh4HlgEbExr8rMy5b2JwC/zdp4CniVVmij/rZljXCuvP9i4y8ZepixXN99Tag32L8DXCss3Ivb+pKHP7z/nf5XpC0l5wSgaW15rVKx/jYhbgJeqY4iIOXn4FeBuUsU0NPbc19/r0rpyi4Pfk77zinZf4/TQ1RUJufnJ31ZO7kpNs+aR7oaeVtjpizaMiMfz8BOkQjHAN0g1e/uTTqhfJh0M1RYA2zcsEVWq01SYPoWUCf0uTzoGmFlISy0bk76LiqV52n+RCsezga+TavUuqFPYuBvYqf8pqa0f6fskMEvSUuCjwKmsrJvTtwVwiFIT3V8oN9OtUi99s0l3xm4DzpR0AHB3nf19LrDLwFPUU730VfkjMELS5Dx+MLBJjeU6Ln3WU/H3zhcQ3yEVaGotex2pUPs8qQa9Wr3f+z5g79zscH1gN9LF4SLgadIx+nPSu5JXidq9MPfYD0rup5Vlf0I6F7wN+Pd+xA2pUHN9bup5BCwvhM8i3ZV9nPQe63dFxM8aGTdwilLT1zNyga7aW4BnJV0h6R5J31LhkZM66f4+6bjblHQH7XDgB33F3ZtimiLiVtL7rh/Pf9fl37meXYAnCxfV3yDd/ft8jvUU0h2kHnLF5CjlRwXKxlcmPf1wKD0vzuvl+YMue/SShuoYaq27GamlzS97S8NgjkcbnAaXC5H0LkkLgfnAkYWKhcr88aQ70Le3KD8rU967DzhA0ghJmwPbkc4Tr5Iu5ueTLionku6GDzjufsbeZ1muj3zvZNJ59cXC8oOKfQBpKPP9Fx1EKpO9TIPKa02MtSZJa5Py3BvypIac+wZ4XQpwTD6fz9CKR+7HAJ8Dvlq1bNuucWrp6ooEYH1SrSQAEfFoRGxFOsEdJmnDumuyvOa8Uhs7OyK2i4j9ST/QLOAtSs+P/kjpnaRExOvAK5Le0Jwk9UwTQL5rdwFweEQsy8/AfJDahd4+RcRzEbFfREwm7WT7A5V0XiZpx8LiT5GazzVKn+nLkz8D7BsR40hNiU4v+wFdkr5RwEs5xh8BM8p+QL5z+w8RsQ2pudNxwHcknZ7Td0Bh8aanr0Z8QSqAniHpDtKF5etlP6DN6bOeir/3UcCsiFhaa8GIeC+peewooPRzeRFxPSm//Q3pouVW8v4SEcdFxKSI+A75LoekL0q6VNI/FTZTvR/0uZ8WPv/wvO4iYKXnjfuwc0RsC+wDHC3p7/M2v5nj/myO+yuSPpnjLl74DjTuz5MqALYnNT//XI1lRpAKPMfn5f4W+HhlZq10R8QFEbFNRHyElAefCeyTj7sztKIfgv4cd8vTpPQ869tJd4E2BnbPd+Dr6XGnPCLujYgdImK3nJ7H02Z1iaQLq875ZWMsva+UlfP8LUlNYytq5vkNKnvUO+9Ux1DLocBlefu9rj+I49EGp6Hlwoi4PSLeQcoXPi9p9cJ2x5AeTzsucsulFuRnZcp7M0iVuHOB75LOF69LGkmqSNgmf/Y8Uv44mLj7E3ufZbl6+Z5S3zZbRMSV1esMMvb+pqF0eVvSO4DTgP+X42xUea3hsfaShhGk88qZEfFwTkejzn0DuS79IalCahLpnPadPP1E4IyI6NHfSZuvcVbS7RUJ/0dqLtRDrvFZQO1aoydzBlzJiHs0Q8on7Y+TaqK+SnrW8hZ6NmUZRVWTmQbqkSZJawHXkJ73vC1P3oa0Uy6WtAQYLWlxjW09Rs87wePytKIvk+7qTCOl8zDSzluxeo6pUfpMn6SxwNYRcXte7BLSM2bVujJ92VLS874AV5KeP61WJn1Hke7Q7UCqtT4E+GxhflPTV09E3BoRu0TEFOAm0nNt1ToxfdZT8ffekVRrvoT0XPrHVOisCCAiXiI1TZxaY1t1f++IOCUXmvYkNTHssb9Imkp6JnYMqeD1IeDgykUWK+8HpfbTQtyvAxdT+xGc3uKu/H+KdBz3aLYpaZucngeAD+a4tyjctRpQ3JGaIUe+I/ST6s/NlgL3RsTD+Y7jz4Btq7ZTM935omRKvgP2WdJx9yzpudVacfemmKb3A7dFesziBVKfGTvWWikX9j5Ayv+r54nUEuFkUtP6fyMV4osdnZWNsV/7SkkfAq6Mns2Qe83zB1n2qJWGWjHUUq/VQt31B3A82uA0sly4XKS74i+QnnknX5RfDlwUEVdUL9+M/KxseS9fsH4mnyemAmuTzhOT8vzf5ZsYl1avP4C4S8WelSnL1cv3dgQm59/rFlIF4o0NiL10GvpR3kbSuJzGj0XE72osMpjyWkNj7cPZwEMR8d0anzHYc1+/r0sj4smIeD3faPwRK87n7wK+mfeP44AvSDqmavVWX+OspKsrEiI1X1xV0uqSxklaAyA3C9mZdOBVm0n6osn/q58p/FdSLdWrrHh2dBnpeRSUmkr+scTJeUCq0rQa6aA9PyIuKyxzTUT8TUSMj4jxwIsRUeutBjOBQ5V6Vd0cmADcUZmZM6JxkZ4vHZ3TGaR0V7yFtPO3LH3An4E3SnpLHt+TdNesWremD1Khfrc8/G5qX2hfB+wlaZ28T+9F4e5QnvY+Usbd8vT1tpykDfL/UaS7pWfVWKzj0mc9FX/viPhwRGya85zjSfv1dEljCpWzI0idJ/62xuZmkiofJGkH4LmIeFyph+ZKb/VbkQpiy/tgyIXbyvN/xef5VyU164Wq/aDMfprjeHNlmNRpVL24V8pnJK1ZuTssaU3S/lu9L1aeFR2Z44XC+WQgcefPq3zfIvXzU+sYuJPUD0WlX5bdgftLpvtk4Ct5eKXzYHXcvalK0/8C71ZqnjySlPfVe7ThPcBvo3YLmI+RWsdUOgJcRs/ztIC/IXVu1Z/4GqVWnwN95fkDLnvUSUO9fg+Wk/Q2Uge3t5ZMw4CORxucRpYLJW2e8+nKYy1vIz27LuAcYFFE1LvL24z8rFR5T+nRtzXz8J7AaxFxP6lSd2Ihn6u1fr/i7kfsUK4sVzPfi4gfRsSb8u+1M/BgROw62Nj7mYay3//apMqr6RHx6xrzB1Vea2SsvZH0NVLfCsfVWWRQ576BXJdWzufZ+yvbzzfjKsfzd4GvR0TxLRQtv8apKZrcm2Oz/0gZ33tIO9Q80nNU84AjCsv8GJich9cjPRPzEOm5xHULy70JuKYw/kFSBxe/BsbmaQeTe4tuQZo+QupU497C36Qay79QGD6A1PFLZfyLpOfnHiD3TlqYdykrev/dgNRUbCFwUJ42knSQjmh1+kgH0/z8e95IeuZoKKWvkinPJxXits7TJ5N7xs7j/0jq3G0xqQlj8XPOIPf8S6p1vD6n79jCMncD6zUjfXn4ZtJzs/9Hqpl/b57+rfzdPkBqIkm3pM9/9X/vwrSPk3tDJvUzcycp311Aalpb6RX8SNIzuJDuqvwgH6/zWZEnrw7cn/9uoyqPI53wP17Yxk/z+qcVlrka2LI/+ympIv3XeVsLgItY8faJPvMZUtP6+/LfQqreEkC6wD+xMP7t/FkXFaZ9H9i/P3Hn6b8sxH0hMCZPrz6+KufF+aQ3PazWW7rzOttQ6HE7f/8LgWtZ0QP18cXjsOw+RCoI/ycpb7gfOL2wzPLzdB4/t7LvVG1rNOl545F5fJeclruAtxa+h8sHso/38p1/Ko+/RnoW+8eFzyp+5+NJFzerVH1GzTw/zxt02aMqDfViOAk4oDB+InBqjW3VXH8wx6P/BvdHg8qFpOfKF+b17gYOzNN3Jl2EzCtsd9/CNpqZn/VZ3sv75AOkvON/gM0Kn3Fknj6P1H/HeoONux+x91mWo5d8r/BZ48lvbWhE7E34/r8E/JWe+90Ghc8ZdHmtUbHm8SWkN868kNefSGpNGPl3qKThk4V1GnLuo//XpRfkNM0j3bjYqMY2T6TqzQ606RpnpdiaufFW/JGaal7Qws+7gvxau6GSpl7ieD9wstPXtenbphlxDPX0+a8zf+9e4tsQuKEL4x5FqjgZUTW9o+POMd4ErNOP5Vt9nv4esEenxjfANPVa9uiUNNQ7Hv036O+1I37fXuLryvysXtzdHnunpaFMea1TYu0jxj7PfZ2SjmZd41T/dfWjDQCRegyeo0Jv1M2Sm5T9LFa81q4pWpmmPoxgRacfDeP0tcz6VL2CpxGGevqspw76vevZlJ7PYQJdE/f0qOoxvdPjzk2IT4/UhLOUNqRpQaTXh5XSBd95n2WPDkpDzePRBqeDft96ujI/o07c0N2xV3RQGvosr3VQrDWVPfd1UDqaco1TTbnWwszMzMzMzMysT13fIsHMzMzMzMzMWscVCWZmZmZmZmZWmisSzMzMzMzMzKw0VyRYy0g6UFLkd1c3Y/uTJO1bZ96ukq7Owx+X9LSkeyQ9JOk6SX/XjJjMzNol57cXFsZH5Lzv6nbGVVZvebqZWTdwPmxDmSsSrJWmAbfk/80wCSib2V0SEdtExATgVOAKSW9vUlxmZu3wV+CdktbI43sCj7Uxnv7qT55uZtaJnA/bkOWKBGsJSWOAnYFPAIcWpq8i6T8k/VbSbEmzJB2c520n6VeS7sqtBjbK02+UdJqkOyQ9KGmX/Hqsk4BDJN0r6ZCysUXEHOBs4IgGJtnMrBPMAvbLw9OAn1ZmSFpX0s8kzZN0m6Stcp68RNLaheUekrShpLGSLpd0Z/7bKc8/UdJ5km6W9IikD0j6pqT5kq6VNDIv15I83cyswzgftiHJFQnWKlOBa/N7sP8kabs8/QPAeGAi8FFgR4Cc4f07cHBEbAfMAE4pbG9EREwBjgNOiIhXgK+QWhpMiohL+hnf3UBTHrkwM2uji4FDJa0ObAXcXpj3VeCeiNgK+AJwfkQsA64C3g8g6V3AIxHxJPA94IyI2B44CPhxYVtbALsDBwAXAnMiYkvg/4D92pCnm5l1CufDNiSNaHcANmxMI2V+kDLUacBdpFYK/50zzSckzcnLvBV4JzBbEsCqwOOF7V2R/99FqogYLDVgG2ZmHSUi5kkaT8pzZ1XN3plUECUifilpPUlrAZeQCo8/IbUgqxQe3wNMzHkywFq5tRnALyLiVUnzSfn1tXn6fFIe3eo83cysIzgftqHKFQnWdJLWJdWQbikpSBlXSPrX3lYDFkbEjnXmv5z/v05j9uNtgEUN2I6ZWaeZCXwb2BVYr8TytwJvljQWOBD4Wp6+CrBDRLxUXDgXSF8GiIhlkl6NiMizB/U43wAAAXRJREFUl5Hy6Fbn6WZmncT5sA05frTBWuFg4IKI2CwixkfEJsDvgV2AXwMH5efBNiRlsAAPAGMlLX/UQdI7+vic54E39Dc4Se8m9Y/wo/6ua2bWBWYAX42I+VXTbwY+DOnNNsAfI+IvufB5JXA6sCgi/pSXvx44trKypEn9iKFlebqZWQdyPmxDjisSrBWmkTLDosvz9MuBpcD9pOe57gaey89lHQycJuk+4F6gr1c0ziE19yrTIUyl45gHSc+kHRQRbpFgZkNORCyNiDNrzDoR2E7SPNLbaw4rzLsE+AgrmtMCfAqYnDsFux84sh8xNDtPNzPrWM6HbSjSilYvZu0haUxEvCBpPeAOYKeIeKLdcZmZmZmZmdnK/PyLdYKr8ytuVgNOdiWCmZmZmZlZ53KLBDMzMzMzMzMrzX0kmJmZmZmZmVlprkgwMzMzMzMzs9JckWBmZmZmZmZmpbkiwczMzMzMzMxKc0WCmZmZmZmZmZX2/wGIUDIVtooMPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "info, rewards, lengths, elapsed = agent.evaluate_agent(labels, iterations=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "agent.pretraining_network.save('./models/res/' + model_name + '_full_model.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reinforcement training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TensorforceAgent(BaseAgent):\n",
    "    def act(self, obs, action_space):\n",
    "        pass\n",
    "    \n",
    "class TensorboardLogger(Callback):\n",
    "    \"\"\"Logging in tensorboard without tensorflow ops.\"\"\"\n",
    "    def __init__(self, log_dir):\n",
    "        # Some algorithms compute multiple episodes at once since they are multi-threaded.\n",
    "        # We therefore use a dictionary that is indexed by the episode to separate episodes\n",
    "        # from each other.\n",
    "        self.observations = {}\n",
    "        self.rewards = {}\n",
    "        self.actions = {}\n",
    "        self.metrics = {}\n",
    "        self.step = 0\n",
    "        \"\"\"Creates a summary writer logging to log_dir.\"\"\"\n",
    "        self.writer = tf.summary.FileWriter(log_dir)\n",
    "\n",
    "    def log_scalar(self, tag, value, step):\n",
    "        \"\"\"Log a scalar variable.\n",
    "        Parameter\n",
    "        ----------\n",
    "        tag : basestring\n",
    "            Name of the scalar\n",
    "        value\n",
    "        step : int\n",
    "            training iteration\n",
    "        \"\"\"\n",
    "        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])\n",
    "        self.writer.add_summary(summary, step)\n",
    "\n",
    "    def on_train_begin(self, logs):\n",
    "        self.metrics_names = self.model.metrics_names\n",
    "\n",
    "    def on_episode_begin(self, episode, logs):\n",
    "        self.observations[episode] = []\n",
    "        self.rewards[episode] = []\n",
    "        self.actions[episode] = []\n",
    "        self.metrics[episode] = []\n",
    "\n",
    "    def on_episode_end(self, episode, logs):\n",
    "        episode_steps = len(self.observations[episode])\n",
    "        variables = {\n",
    "            'step': self.step,\n",
    "            'nb_steps': self.params['nb_steps'],\n",
    "            'episode_steps': episode_steps,\n",
    "            'episode_reward': np.sum(self.rewards[episode]),\n",
    "            'reward_mean': np.mean(self.rewards[episode]),\n",
    "            'reward_min': np.min(self.rewards[episode]),\n",
    "            'reward_max': np.max(self.rewards[episode]),\n",
    "            'action_mean': np.mean(self.actions[episode]),\n",
    "            'action_min': np.min(self.actions[episode]),\n",
    "            'action_max': np.max(self.actions[episode]),\n",
    "            'obs_mean': np.mean(self.observations[episode]),\n",
    "            'obs_min': np.min(self.observations[episode]),\n",
    "            'obs_max': np.max(self.observations[episode]),\n",
    "        }\n",
    "\n",
    "        # Format all metrics.\n",
    "        metrics = np.array(self.metrics[episode])\n",
    "        with warnings.catch_warnings():\n",
    "            warnings.filterwarnings('error')\n",
    "            for idx, name in enumerate(self.metrics_names):\n",
    "                try:\n",
    "                    value = np.nanmean(metrics[:, idx])\n",
    "                except Warning:\n",
    "                    value = -1\n",
    "                variables[name] = value\n",
    "        for key, value in variables.items():\n",
    "            self.log_scalar(key, value, episode + 1)\n",
    "\n",
    "        # Free up resources.\n",
    "        del self.observations[episode]\n",
    "        del self.rewards[episode]\n",
    "        del self.actions[episode]\n",
    "        del self.metrics[episode]\n",
    "\n",
    "    def on_step_end(self, step, logs):\n",
    "        episode = logs['episode']\n",
    "        self.observations[episode].append(logs['observation'])\n",
    "        self.rewards[episode].append(logs['reward'])\n",
    "        self.actions[episode].append(logs['action'])\n",
    "        self.metrics[episode].append(logs['metrics'])\n",
    "        self.step += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Redefine environment wrapper for the reinforcement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class EnvWrapper(Env):\n",
    "    \"\"\"The abstract environment class that is used by all agents. This class has the exact\n",
    "        same API that OpenAI Gym uses so that integrating with it is trivial. In contrast to the\n",
    "        OpenAI Gym implementation, this class only defines the abstract methods without any actual\n",
    "        implementation.\n",
    "        To implement your own environment, you need to define the following methods:\n",
    "        - `step`\n",
    "        - `reset`\n",
    "        - `render`\n",
    "        - `close`\n",
    "        Refer to the [Gym documentation](https://gym.openai.com/docs/#environments).\n",
    "        \"\"\"\n",
    "    reward_range = (-1, 1)\n",
    "    action_space = None\n",
    "    observation_space = None\n",
    "\n",
    "    def __init__(self, gym, board_size):\n",
    "        self.gym = gym\n",
    "        self.action_space = gym.action_space\n",
    "        self.observation_space = gym.observation_space\n",
    "        self.reward_range = gym.reward_range\n",
    "        self.board_size = board_size\n",
    "#         self.observations_history = []\n",
    "#         self.step = 0\n",
    "        \n",
    "#     def make_observation(self, obs, i):\n",
    "#         if i == 0: # If it is a first observation\n",
    "#             return np.array([obs for _ in range(history_length)])\n",
    "#         elif i < 3: # If there are less than 3 observations in a history\n",
    "#             n_first = 3 - i\n",
    "#             return np.concatenate([np.array([self.observations_history[0] for _ in range(n_first)]), # Repeat the first observation\n",
    "#                                    np.array(self.observations_history[:i]).reshape(i, view_size, view_size, n_channels), # Add next observations\n",
    "#                                    obs.reshape(1, view_size, view_size, n_channels)], # Current observation\n",
    "#                                    axis=0)\n",
    "#         else:\n",
    "#             return np.concatenate([np.array(self.observations_history[i-history_length+1:i]).reshape(history_length-1, view_size, view_size, n_channels), # Add next observations\n",
    "#                                    obs.reshape(1, view_size, view_size, n_channels)], # Current observation\n",
    "#                                    axis=0)\n",
    "\n",
    "    def step(self, action):\n",
    "        \"\"\"Run one timestep of the environment's dynamics.\n",
    "        Accepts an action and returns a tuple (observation, reward, done, info).\n",
    "        # Arguments\n",
    "            action (object): An action provided by the environment.\n",
    "        # Returns\n",
    "            observation (object): Agent's observation of the current environment.\n",
    "            reward (float) : Amount of reward returned after previous action.\n",
    "            done (boolean): Whether the episode has ended, in which case further step() calls will return undefined results.\n",
    "            info (dict): Contains auxiliary diagnostic information (helpful for debugging, and sometimes learning).\n",
    "        \"\"\"\n",
    "        obs = self.gym.get_observations()\n",
    "        all_actions = self.gym.act(obs)\n",
    "        all_actions.insert(self.gym.training_agent, action)\n",
    "        state, reward, terminal, info = self.gym.step(all_actions)\n",
    "        agent_state = self.featurize(state[self.gym.training_agent])\n",
    "#         print(agent_state.shape)\n",
    "        \n",
    "#         agent_state_history = self.make_observation(agent_state, self.step)\n",
    "        agent_reward = reward[self.gym.training_agent]\n",
    "        \n",
    "#         self.step += 1\n",
    "        return agent_state, agent_reward, terminal, info\n",
    "\n",
    "    def reset(self):\n",
    "        \"\"\"\n",
    "        Resets the state of the environment and returns an initial observation.\n",
    "        # Returns\n",
    "            observation (object): The initial observation of the space. Initial reward is assumed to be 0.\n",
    "        \"\"\"\n",
    "        obs = self.gym.reset()\n",
    "#         self.step = 1\n",
    "        agent_obs = self.featurize(obs[self.gym.training_agent])\n",
    "#         self.observations_history = [agent_obs]\n",
    "        return agent_obs\n",
    "\n",
    "    def render(self, mode='human', close=False):\n",
    "        \"\"\"Renders the environment.\n",
    "        The set of supported modes varies per environment. (And some\n",
    "        environments do not support rendering at all.)\n",
    "        # Arguments\n",
    "            mode (str): The mode to render with.\n",
    "            close (bool): Close all open renderings.\n",
    "        \"\"\"\n",
    "        self.gym.render(mode=mode, close=close)\n",
    "\n",
    "    def close(self):\n",
    "        \"\"\"Override in your subclass to perform any necessary cleanup.\n",
    "        Environments will automatically close() themselves when\n",
    "        garbage collected or when the program exits.\n",
    "        \"\"\"\n",
    "        self.gym.close()\n",
    "\n",
    "    def seed(self, seed=None):\n",
    "        \"\"\"Sets the seed for this env's random number generator(s).\n",
    "        # Returns\n",
    "            Returns the list of seeds used in this env's random number generators\n",
    "        \"\"\"\n",
    "        raise self.gym.seed(seed)\n",
    "\n",
    "    def configure(self, *args, **kwargs):\n",
    "        \"\"\"Provides runtime configuration to the environment.\n",
    "        This configuration should consist of data that tells your\n",
    "        environment how to run (such as an address of a remote server,\n",
    "        or path to your ImageNet data). It should not affect the\n",
    "        semantics of the environment.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def featurize(self, obs):\n",
    "        return featurize(obs, center=False, crop=True)\n",
    "\n",
    "    def __del__(self):\n",
    "        self.close()\n",
    "\n",
    "    def __str__(self):\n",
    "        return '<{} instance>'.format(type(self).__name__)\n",
    "\n",
    "\n",
    "class CustomProcessor(Processor):\n",
    "    def process_state_batch(self, batch):\n",
    "        \"\"\"Processes an entire batch of states and returns it.\n",
    "        # Arguments\n",
    "            batch (list): List of states\n",
    "        # Returns\n",
    "            Processed list of states\n",
    "        \"\"\"\n",
    "        batch = np.squeeze(batch, axis=1)\n",
    "#         batch = np.array([np.concatenate(obs, axis=-1) for obs in batch])\n",
    "        return batch\n",
    "\n",
    "    def process_info(self, info):\n",
    "        \"\"\"Processes the info as obtained from the environment for use in an agent and\n",
    "        returns it.\n",
    "        \"\"\"\n",
    "        info['result'] = info['result'].value\n",
    "        return info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying to load weights\n",
      "Weights were loaded successful\n"
     ]
    }
   ],
   "source": [
    "agent.load_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_env(agent_id=0):\n",
    "    # Instantiate the environment\n",
    "    config = ffa_v0_env()\n",
    "    env = Pomme(**config[\"env_kwargs\"])\n",
    "\n",
    "    np.random.seed(0)\n",
    "    env.seed(0)\n",
    "    # Add 3 random agents and one Tensorforce\n",
    "    agents = [TensorforceAgent(config[\"agent\"](agent_id, config[\"game_type\"])) if i == agent_id else SimpleAgent(config[\"agent\"](i, config[\"game_type\"])) for i in range(4)]\n",
    "    env.set_agents(agents)\n",
    "    env.set_training_agent(agents[agent_id].agent_id)\n",
    "    env.set_init_game_state(None)\n",
    "    \n",
    "    return env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_dqn(model,\n",
    "            log_interval=100000,\n",
    "            model_name='res_rl_2d_1',\n",
    "            file_log_path='./logs/res/rl_2d/log.txt',\n",
    "            tensorboard_path='./logs/res/rl_2d/'):\n",
    "    \n",
    "    model_path = './models/res/' + model_name + '.h5'\n",
    "    file_logger = FileLogger(file_log_path, interval=log_interval)\n",
    "    checkpoint = ModelIntervalCheckpoint(model_path, interval=log_interval)\n",
    "    tensorboard = TensorboardLogger(tensorboard_path)\n",
    "    callbacks=[file_logger, checkpoint, tensorboard]\n",
    "\n",
    "    # We use 4 last observations\n",
    "    memory=SequentialMemory(limit=500000, window_length=1)\n",
    "\n",
    "    # Select a policy. We use eps-greedy action selection, which means that a random action is selected\n",
    "    # with probability eps. We anneal eps from 1.0 to 0.1 over the course of 1M steps. This is done so that\n",
    "    # the agent initially explores the environment (high eps) and then gradually sticks to what it knows\n",
    "    # (low eps). We also set a dedicated eps value that is used during testing. Note that we set it to 0.05\n",
    "    # so that the agent still performs some random actions. This ensures that the agent cannot get stuck.\n",
    "    policy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr='eps', value_max=1., value_min=.1, value_test=.05,\n",
    "                                  nb_steps=500000)\n",
    "    #policy=BoltzmannQPolicy()\n",
    "\n",
    "    # enable the dueling network\n",
    "    # you can specify the dueling_type to one of {'avg','max','naive'}\n",
    "    dqn = DQNAgent(model=model, \n",
    "                   nb_actions=env.action_space.n, \n",
    "                   memory=memory, \n",
    "                   policy=policy,\n",
    "                   processor=CustomProcessor(),\n",
    "                   nb_steps_warmup=512,\n",
    "                   enable_dueling_network=True, \n",
    "                   dueling_type='avg', \n",
    "                   target_model_update=5e2,  \n",
    "                   batch_size=32)\n",
    "\n",
    "    dqn.compile(Adam(lr=5e-4), metrics=['mae'])\n",
    "    \n",
    "    return dqn, callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "agent.evaluation_model = agent.rl_network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dqn, callbacks = get_dqn(agent.rl_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env_wrapper = EnvWrapper(get_env(agent_id=0), BOARD_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "history = dqn.fit(env_wrapper, nb_steps=5e6, visualize=False, verbose=2,\n",
    "                  nb_max_episode_steps=env._max_steps,\n",
    "                  callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dqn.model.save('./models/res/res_rl_2d_8_1M_full_model.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reward shaping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will continue the training with the reward shaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isBetween(a, b, c):\n",
    "    crossproduct = (c.y - a.y) * (b.x - a.x) - (c.x - a.x) * (b.y - a.y)\n",
    "    epsilon = 0.0001\n",
    "    # compare versus epsilon for floating point values, or != 0 if using integers\n",
    "    if abs(crossproduct) > epsilon:\n",
    "        return False\n",
    "\n",
    "    dotproduct = (c.x - a.x) * (b.x - a.x) + (c.y - a.y)*(b.y - a.y)\n",
    "    if dotproduct < 0:\n",
    "        return False\n",
    "\n",
    "    squaredlengthba = (b.x - a.x)*(b.x - a.x) + (b.y - a.y)*(b.y - a.y)\n",
    "    if dotproduct > squaredlengthba:\n",
    "        return False\n",
    "\n",
    "    return True\n",
    "\n",
    "class Point:\n",
    "    def __init__(self, x, y):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "    \n",
    "    def scale(self, s):\n",
    "        self.x *= s\n",
    "        self.y *= s\n",
    "        return self\n",
    "# Test\n",
    "# A = Point(0,0)\n",
    "# B = Point(5,5)\n",
    "# C = Point(2,2.0001)\n",
    "\n",
    "# isBetween(A,B,C)\n",
    "\n",
    "class RewardShaping:\n",
    "    \"\"\" It takes care of everything just add in the environment loop and make\n",
    "        sure that you reset it either by creating a new calling reset at the end\n",
    "        of each episode otherwise last observation from prev episode is used which\n",
    "        can raise some errors!\n",
    "    \"\"\"\n",
    "    def __init__(self, obs_prev = None, action_prev = None):\n",
    "        self.reset(obs_prev, action_prev)\n",
    "        \n",
    "    def reset(self, obs_prev = None, action_prev = None):\n",
    "        self.obs_prev = obs_prev\n",
    "        self.action_prev = action_prev\n",
    "        # for debug purpose\n",
    "        self.obs_cur = None\n",
    "        self.action_cur = None\n",
    "        self.conseqActionCounter = 0\n",
    "        self.dist2bombs_prev = 0\n",
    "        self.notUsingAmmoCount = 0\n",
    "        # catch enemy\n",
    "        self.closestEnemyIdPrev = -1\n",
    "        self.closestEnemyDistPrev = float(\"inf\")\n",
    "        \n",
    "    def shape_it(self, obs_now, action_now, reward):\n",
    "        \"\"\" Shape the reward based on the current and previous observation\n",
    "            input: current observation, current action, and received reward\n",
    "            output: the shaped reward, sum of each criteria for the final reward\n",
    "        \"\"\"\n",
    "        self.obs_cur = obs_now\n",
    "        self.action_cur = action_now\n",
    "        if reward == -1 or reward == 1: # nothing to do with won/lost agent reward\n",
    "            return reward, None\n",
    "        #\n",
    "        if self.obs_prev is None:\n",
    "            #print(\"first iteration\")\n",
    "            self.obs_prev = obs_now\n",
    "            self.action_prev = action_now\n",
    "            return reward, None\n",
    "        #\n",
    "        reward_tmp = {}\n",
    "        # REWARD VALUES (NB: some of them used as factors not directly!)\n",
    "        MOBILITY_RWRD = 0.1\n",
    "#         CONSEQ_ACT_RWRD = -0.5\n",
    "        CONSEQ_ACT_RWRD = -0.0001\n",
    "        \n",
    "        PLNT_BOMB_NEAR_WOOD_RWRD = 0.05\n",
    "        PLNT_BOMB_NEAR_ENEM_RWRD = 0.1\n",
    "#         ON_FLAMES_RWRD = -0.8\n",
    "        ON_FLAMES_RWRD = -0.0001\n",
    "        \n",
    "        INCRS_DIST_WITH_BOMBS_RWRD = 0.05\n",
    "        PICKED_POWER_RWRD = 0.1\n",
    "#         CATCH_ENEMY_RWRD = 0.1\n",
    "        CATCH_ENEMY_RWRD = 0.001\n",
    "        #\n",
    "        # movement: + for mobility\n",
    "        pose_t = np.array(obs_now['position']) # t\n",
    "        pose_tm1 = np.array(self.obs_prev['position']) # t-1\n",
    "        moveDist = np.linalg.norm(pose_t-pose_tm1)\n",
    "        reward_tmp['mobility'] = MOBILITY_RWRD if moveDist > 0 else 0 # give reward\n",
    "        #\n",
    "        # consequative action: - for conseq actions\n",
    "        if self.action_prev == action_now:\n",
    "            self.conseqActionCounter += 1\n",
    "        else:\n",
    "            self.conseqActionCounter = 0\n",
    "        if self.conseqActionCounter > 11:\n",
    "            reward_tmp['conseqact'] = CONSEQ_ACT_RWRD\n",
    "        # keeping ammo: - for not using its ammo\n",
    "        if obs_now['ammo'] == self.obs_prev['ammo']:\n",
    "            self.notUsingAmmoCount += 1\n",
    "        else:\n",
    "            self.notUsingAmmoCount = 0\n",
    "        if self.notUsingAmmoCount > 11:\n",
    "            reward_tmp['ammousage'] = CONSEQ_ACT_RWRD\n",
    "        #\n",
    "        # plant a bomb: + based on value of the bombing position\n",
    "        bombs_pose = np.argwhere(obs_now['bomb_life'] != 0)\n",
    "        if obs_now['ammo'] < self.obs_prev['ammo']:\n",
    "            surroundings = [(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1),(-1,0)]\n",
    "            mybomb_pose = self.obs_prev['position'] # equal to agent previous position\n",
    "            # validate if the bomb actually exists there\n",
    "            found_the_bomb = False\n",
    "            for bp in bombs_pose:\n",
    "                if np.equal(bp, mybomb_pose).all():\n",
    "                    found_the_bomb = True\n",
    "                    break\n",
    "            assert found_the_bomb # end of validation\n",
    "            nr_woods = 0\n",
    "            nr_enemies = 0\n",
    "            for p in surroundings:\n",
    "                cell_pose = (mybomb_pose[0] + p[0], mybomb_pose[1] + p[1])\n",
    "                if cell_pose[0] > 10 or cell_pose[1] > 10: # bigger than board size\n",
    "                    continue\n",
    "                #print(obs_now['board'][cell_pose])\n",
    "                nr_woods += obs_now['board'][cell_pose] == Item.Wood.value\n",
    "                nr_enemies += obs_now['board'][cell_pose] in [e.value for e in obs_now['enemies']]\n",
    "            #print(\"nr woods: \", nr_woods)\n",
    "            #print(\"nr enemies: \", nr_enemies)\n",
    "            assert nr_woods + nr_enemies < 10\n",
    "            reward_tmp['plantbomb'] = \\\n",
    "                PLNT_BOMB_NEAR_WOOD_RWRD * nr_woods \\\n",
    "                + PLNT_BOMB_NEAR_ENEM_RWRD * nr_enemies # give reward\n",
    "        #\n",
    "        # on Flames: - if agent on any blast direction \n",
    "        for bp in bombs_pose:\n",
    "            def rot_deg90cw(point):\n",
    "                new_point = [0, 0]\n",
    "                new_point[0] = point[1]\n",
    "                new_point[1] = -point[0]\n",
    "                return new_point\n",
    "            \n",
    "            #print(type(bp))\n",
    "            factor = 1/obs_now['bomb_life'][tuple(bp)] # inverse of time left\n",
    "            blast_strength = obs_now['bomb_blast_strength'][tuple(bp)]\n",
    "\n",
    "            # blast directions\n",
    "            blast_N = Point(0,1).scale(blast_strength)\n",
    "            blast_S = Point(0,-1).scale(blast_strength)\n",
    "            blast_W = Point(-1,0).scale(blast_strength)\n",
    "            blast_E = Point(1,0).scale(blast_strength)\n",
    "\n",
    "            # agent on blast direction?\n",
    "            bpPose = rot_deg90cw(bp)\n",
    "            myPose = rot_deg90cw(obs_now['position'])\n",
    "            myPose = Point(myPose[0]-bpPose[0], myPose[1]-bpPose[1]) # my pose relative to the bomb!\n",
    "            onBlastDirect = isBetween(blast_N, blast_S, myPose) or \\\n",
    "                            isBetween(blast_W, blast_E, myPose)\n",
    "            if onBlastDirect:\n",
    "                #print(\"time: \", obs_now['bomb_life'][tuple(bp)])\n",
    "                #print(\"on blast: \", factor)\n",
    "                reward_tmp['onflame'] = ON_FLAMES_RWRD * factor\n",
    "        #\n",
    "        # Bombs distance: + if total distance from bombs increased\n",
    "        dist2bombs = 0\n",
    "        for bp in bombs_pose:\n",
    "            dist2bombs += np.linalg.norm(obs_now['position']-bp)\n",
    "        dist_delta = dist2bombs - self.dist2bombs_prev\n",
    "        self.dist2bombs_prev = dist2bombs\n",
    "        #print(dist_delta)\n",
    "        # TODO: this may not be good if the delta oscillates all the time\n",
    "        if (dist_delta > 0 and moveDist):\n",
    "            reward_tmp['bombsdistance'] = dist_delta * INCRS_DIST_WITH_BOMBS_RWRD\n",
    "        # picked power: + for every picked power\n",
    "        potentialPower = self.obs_prev['board'][obs_now['position']]\n",
    "        picked_power = (potentialPower == Item.ExtraBomb.value) or\\\n",
    "                       (potentialPower == Item.IncrRange.value) or\\\n",
    "                       (potentialPower == Item.Kick.value)\n",
    "        if picked_power:\n",
    "            reward_tmp['pickedpower'] = PICKED_POWER_RWRD\n",
    "        \n",
    "        # catch enemy: + if closing distance with the nearest enemy\n",
    "        def closestEnemy():\n",
    "            myPose = obs_now['position']\n",
    "            closestEnemyId = -1\n",
    "            closestEnemyDist = float(\"inf\")\n",
    "            for e in obs_now['enemies']:\n",
    "                enemyPose = np.argwhere(obs_now['board'] == e.value)\n",
    "                if len(enemyPose) == 0:\n",
    "                    continue\n",
    "                dist2Enemy = np.linalg.norm(myPose-enemyPose)\n",
    "                if dist2Enemy <= closestEnemyDist:\n",
    "                    closestEnemyId = e.value\n",
    "                    closestEnemyDist = dist2Enemy\n",
    "            return closestEnemyId, closestEnemyDist\n",
    "\n",
    "        closestEnemyId_cur, closestEnemyDist_cur = closestEnemy()\n",
    "        if self.closestEnemyIdPrev != closestEnemyId_cur:\n",
    "            self.closestEnemyIdPrev = closestEnemyId_cur\n",
    "            self.closestEnemyDistPrev = closestEnemyDist_cur\n",
    "        else:\n",
    "            CATCHING_TRHE = 4 # consider catching when close at most this much to the enemy\n",
    "            if closestEnemyDist_cur < self.closestEnemyDistPrev and\\\n",
    "                closestEnemyDist_cur < CATCHING_TRHE:\n",
    "                reward_tmp['catchenemy'] = CATCH_ENEMY_RWRD\n",
    "                self.closestEnemyDistPrev = closestEnemyDist_cur\n",
    "            if closestEnemyDist_cur <= 1.1: # got that close\n",
    "                self.closestEnemyDistPrev = float(\"inf\")\n",
    "        #print(\"catching: \", closestEnemyIdPrev)\n",
    "        \n",
    "        # update previous obs and action\n",
    "        self.obs_prev = obs_now\n",
    "        self.action_prev = action_now\n",
    "        \n",
    "        # just a notice :\n",
    "        for k,v in reward_tmp.items():\n",
    "            if v >= 1.0 or v <= -1.0:\n",
    "                print(\"reward for criteria '%s' is %f\" % (k, v))\n",
    "        \n",
    "        # sum up rewards\n",
    "        reward_shaped = reward + sum(reward_tmp.values())\n",
    "        return np.clip(reward_shaped, -0.9, 0.9), reward_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class EnvWrapperRS(EnvWrapper):\n",
    "    def __init__(self, gym, board_size):\n",
    "        super(EnvWrapperRS, self).__init__(gym, board_size)\n",
    "        self.rewardShaping = RewardShaping()\n",
    "    \n",
    "    def step(self, action):\n",
    "        \"\"\"Run one timestep of the environment's dynamics.\n",
    "        Accepts an action and returns a tuple (observation, reward, done, info).\n",
    "        # Arguments\n",
    "            action (object): An action provided by the environment.\n",
    "        # Returns\n",
    "            observation (object): Agent's observation of the current environment.\n",
    "            reward (float) : Amount of reward returned after previous action.\n",
    "            done (boolean): Whether the episode has ended, in which case further step() calls will return undefined results.\n",
    "            info (dict): Contains auxiliary diagnostic information (helpful for debugging, and sometimes learning).\n",
    "        \"\"\"\n",
    "        obs = self.gym.get_observations()\n",
    "        all_actions = self.gym.act(obs)\n",
    "        all_actions.insert(self.gym.training_agent, action)\n",
    "        state, reward, terminal, info = self.gym.step(all_actions)\n",
    "        \n",
    "        action = all_actions[self.gym.training_agent]\n",
    "        agent_state = self.featurize(state[self.gym.training_agent])\n",
    "        agent_reward = reward[self.gym.training_agent]\n",
    "        \n",
    "        agent_reward, reward_info = self.rewardShaping.shape_it(obs[self.gym.training_agent], \n",
    "                                                   action, agent_reward)\n",
    "        \n",
    "        return agent_state, agent_reward, terminal, info\n",
    "\n",
    "    def reset(self):\n",
    "        \"\"\"\n",
    "        Resets the state of the environment and returns an initial observation.\n",
    "        # Returns\n",
    "            observation (object): The initial observation of the space. Initial reward is assumed to be 0.\n",
    "        \"\"\"\n",
    "        obs = self.gym.reset()\n",
    "        self.rewardShaping.reset()\n",
    "        agent_obs = self.featurize(obs[self.gym.training_agent])\n",
    "        return agent_obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env_wrapper = EnvWrapperRS(get_env(agent_id=0), BOARD_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dqn_rs, callbacks = get_dqn(agent.rl_network,\n",
    "                 model_name='deep_mind_dqn_rl_2d_rs2_1',\n",
    "                 file_log_path='./logs/dqn/rl_2d_rs2/log.txt',\n",
    "                 tensorboard_path='./logs/dqn/rl_2d_rs2/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We may want to continue the previous training\n",
    "# dqn_rs.model.load_weights('./models/dqn/deep_mind_dqn_rl_2d_8_1M.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = dqn_rs.fit(env_wrapper, nb_steps=1e6, visualize=False, verbose=2,\n",
    "                  nb_max_episode_steps=env._max_steps,\n",
    "                  callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dqn_rs.model.save('./models/dqn/deep_mind_dqn_rl_2d_8_1M_rs_600k_full_model.hdf5')"
   ]
  }
 ],
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