smartcab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teach a smartcab to drive\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project 4 for the Udacity Machine Learning Nanodegree\n",
    " In this project, we will use Q-learning to train a smartcab to follow traffic rules and reach it's destination in a timely manner.\n",
    " \n",
    " All code was developed in the accompanying Jupiter notebook, and exported directly to the agent.py, located in the smartcab directory. The project report may be viewed with or without code included inline, by choosing the appropriate pdf, or directly from the ipynb file as a notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup \n",
    "\n",
    "You need Python 2.7 and pygame for this project: https://www.pygame.org/wiki/GettingStarted\n",
    "For help with installation, it is best to reach out to the pygame community [help page, Google group, reddit]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in notebook\n",
      "Environment ready\n"
     ]
    }
   ],
   "source": [
    "# Import what we need, and setup the basic function to run from later.\n",
    "\n",
    "import math\n",
    "import string\n",
    "import sys\n",
    "import os\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "try: # attempt to determine if we are running within a notebook or on the console.\n",
    "    cfg = get_ipython().config \n",
    "    if cfg['IPKernelApp']['parent_appname'] == 'ipython-notebook':\n",
    "        print \"in notebook\"\n",
    "        from IPython.display import display # Allows the use of display() for DataFrames\n",
    "        %matplotlib inline    \n",
    "    else:\n",
    "        print \"in notebook\"\n",
    "        from IPython.display import display # Allows the use of display() for DataFrames\n",
    "        %matplotlib inline  \n",
    "    console=False # either condition above means that we are in a Notebook\n",
    "except NameError:\n",
    "    print \"in console\"\n",
    "    console=True\n",
    "    \n",
    "sys.path.append(\"./smartcab/\")\n",
    "from environment import Agent, Environment\n",
    "from planner import RoutePlanner\n",
    "from simulator import Simulator\n",
    "\n",
    "print \"Environment ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Redirector ready\n"
     ]
    }
   ],
   "source": [
    "# Several of the provided modules output unuseful information during each run. \n",
    "#  Here we provide a way to supress that output as needed. \n",
    "class outputRedirect():\n",
    "    def __init__(self):\n",
    "        self.stout_orig=sys.stdout  # save the current state\n",
    "    \n",
    "    def reset(self): #restore to the original state when initiated\n",
    "        sys.stdout = self.stout_orig\n",
    "        print \"stdout restored!\"\n",
    "        \n",
    "    def suppress_output(self): # a well formed name for the default of the redirect_output\n",
    "        self.redirect_output()\n",
    "        \n",
    "    def redirect_output(self,f= open(os.devnull, 'w')): # redirect to f, if provided, otherwise to null\n",
    "        try:\n",
    "            print \"redirecting stdout....\"\n",
    "            sys.stdout = f\n",
    "        except:\n",
    "            return \"couldn't open destination...\"\n",
    "            self.reset = f\n",
    "            \n",
    "redirector=outputRedirect()\n",
    "\n",
    "print \"Redirector ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run ready\n"
     ]
    }
   ],
   "source": [
    "def run(agentType,trials=10, gui=False, deadline=True, delay=0):\n",
    "    \"\"\"Run the agent for a finite number of trials.\"\"\"\n",
    "    \n",
    "    # Set up environment and agent\n",
    "    \n",
    "    if gui ==False:\n",
    "        redirector=outputRedirect()\n",
    "        redirector.suppress_output()\n",
    "        delay=0\n",
    "    \n",
    "    e = Environment()  # create environment (also adds some dummy traffic)\n",
    "    a = e.create_agent(agentType)  # create agent\n",
    "    e.set_primary_agent(a, enforce_deadline=deadline)  # specify agent to track\n",
    "    # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n",
    "\n",
    "    # Now simulate it\n",
    "    sim = Simulator(e, update_delay=delay, display=gui)  # create simulator (uses pygame when display=True, if available)\n",
    "    # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n",
    "\n",
    "    sim.run(n_trials=trials)  # run for a specified number of trials\n",
    "    # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n",
    "    \n",
    "    if gui ==False:\n",
    "        redirector.reset()    \n",
    "        \n",
    "    print \"Successful runs = {}\".format(a.goal)\n",
    "    print \"----------------------------------------------------------\"\n",
    "    features= [] # the state at each turn in the run\n",
    "    deadlines = [] # the deadline at each turn in the run\n",
    "    for i in range(len(a.features)):\n",
    "        features.append(pd.DataFrame(a.features[i]).T)\n",
    "        deadlines.append(a.deadline[i])\n",
    "        \n",
    "    rewards=[] # the total reward for each run\n",
    "    for i in range(len(a.total_reward)):\n",
    "        rewards.append(a.total_reward[i])\n",
    "\n",
    "\n",
    "    try:\n",
    "        print \"Qtable: \",len(a.Qtable)\n",
    "        print \"state=light, oncoming, right, left, next_waypoint  / actions= {}\\n\".format(a.availableAction)\n",
    "        for r in a.Qtable:\n",
    "            print \"state={}, {}, {}, {}, {} / action={}\".format(r[0],r[1],r[2],r[3],r[4], a.Qtable[r])\n",
    "    except:\n",
    "        print \"no Qtable\"\n",
    "\n",
    "    return features,deadlines,rewards\n",
    "    \n",
    "\n",
    "print \"run ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph display ready\n"
     ]
    }
   ],
   "source": [
    "# display the feedback from the prior runs graphically\n",
    "def statsFromRun(feat,DL,RW):\n",
    "    left=pd.Series()\n",
    "    light=pd.Series()\n",
    "    next_waypoint=pd.Series()\n",
    "    oncoming=pd.Series()\n",
    "    right=pd.Series()\n",
    "    for f in feat:\n",
    "        left= left.add(pd.value_counts(f.left.ravel()), fill_value=0)\n",
    "        light= light.add(pd.value_counts(f.light.ravel()), fill_value=0)\n",
    "        next_waypoint= next_waypoint.add(pd.value_counts(f.next_waypoint.ravel()), fill_value=0)\n",
    "        oncoming= oncoming.add(pd.value_counts(f.oncoming.ravel()), fill_value=0)\n",
    "        right= right.add(pd.value_counts(f.right.ravel()), fill_value=0)\n",
    "\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=3,figsize=(14,6))\n",
    "    fig.suptitle( \"Runs:{}\".format(len(feat)))\n",
    "\n",
    "    left.plot(kind='bar', title=\"Left\",ax=axes[0,0])\n",
    "    light.plot(kind='bar', title=\"light\",ax=axes[0,1])\n",
    "    next_waypoint.plot(kind='bar', title=\"next_waypoint\",ax=axes[0,2])\n",
    "    oncoming.plot(kind='bar', title=\"oncoming\",ax=axes[1,0])\n",
    "    right.plot(kind='bar', title=\"right\",ax=axes[1,2])\n",
    "    axes[1,1].plot(DL,label=\"Deadlines\")\n",
    "    axes[1,1].plot(RW,label=\"Rewards\")\n",
    "    avgDist=3\n",
    "    axes[1,1].plot(     #add a line to the graph representing the avg of all point within avgDist of the current run. \n",
    "        [(np.mean(DL[i-avgDist:i+avgDist])+np.mean(RW[i-avgDist:i+avgDist]))/2 for i in range(len(DL))],\n",
    "        label=\"Avg {:2.2f}\".format( # use the last half avg in the label\n",
    "            (np.mean(DL[len(DL)/2:len(DL)])+np.mean(RW[len(DL)/2:len(DL)]))/2)) \n",
    "    #axes[1,1].xlabel('Run')\n",
    "    axes[1,1].legend(loc=2)\n",
    "    #axes[1,1].title(\"Deadline and Rewards per Run\")\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    \n",
    "    \n",
    "def scorePerRun(DL,RW):\n",
    "    plt.figure(figsize=(14,6))\n",
    "    plt.plot(DL,label=\"Deadlines\")\n",
    "    plt.plot(RW,label=\"Rewards\")\n",
    "    avgDist=3\n",
    "    plt.plot(     #add a line to the graph representing the avg of all point within avgDist of the current run. \n",
    "        [(np.mean(DL[i-avgDist:i+avgDist])+np.mean(RW[i-avgDist:i+avgDist]))/2 for i in range(len(DL))],\n",
    "        label=\"Avg {:2.2f}\".format( # use the last half avg in the label\n",
    "            (np.mean(DL[len(DL)/2:len(DL)])+np.mean(RW[len(RW)/2:len(RW)]))/2)) \n",
    "    avgDist=6\n",
    "    plt.plot(     #add a line to the graph representing the avg of DL within avgDist of the current run. \n",
    "        [np.mean(DL[i-avgDist:i+avgDist]) for i in range(len(DL))],\n",
    "        label=\"DL Avg {:2.2f}\".format( # use the last half avg in the label\n",
    "            np.mean(DL[len(DL)/2:len(DL)]))) \n",
    "    plt.plot(     #add a line to the graph representing the avg of RW within avgDist of the current run. \n",
    "        [np.mean(RW[i-avgDist:i+avgDist]) for i in range(len(RW))],\n",
    "        label=\"RW Avg {:2.2f}\".format( # use the last half avg in the label\n",
    "            np.mean(RW[len(RW)/2:len(RW)]))) \n",
    "    \n",
    "    plt.xlabel('Run')\n",
    "    plt.legend()\n",
    "    plt.title(\"Deadline and Rewards per Run\")\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "print \"Graph display ready\"    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement a basic driving agent\n",
    "\n",
    "Implement the basic driving agent, which processes the following inputs at each time step:\n",
    "\n",
    "Next waypoint location, relative to its current location and heading,\n",
    "Intersection state (traffic light and presence of cars), and,\n",
    "Current deadline value (time steps remaining),\n",
    "And produces some random move/action (None, 'forward', 'left', 'right'). Don’t try to implement the correct strategy! That’s exactly what your agent is supposed to learn.\n",
    "\n",
    "Run this agent within the simulation environment with enforce_deadline set to False (see run function in agent.py), and observe how it performs. In this mode, the agent is given unlimited time to reach the destination. The current state, action taken by your agent and reward/penalty earned are shown in the simulator.\n",
    "\n",
    "In your report, mention what you see in the agent’s behavior. Does it eventually make it to the target location?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomAgent ready\n"
     ]
    }
   ],
   "source": [
    "class RandomAgent(Agent):\n",
    "    \"\"\"An agent that learns to drive in the smartcab world.\"\"\"\n",
    "\n",
    "    def __init__(self, env):\n",
    "        super(RandomAgent, self).__init__(env)  # sets self.env = env, state = None, next_waypoint = None, and a default color\n",
    "        self.color = 'red'  # override color\n",
    "        self.planner = RoutePlanner(self.env, self)  # simple route planner to get next_waypoint\n",
    "        # TODO: Initialize any additional variables here\n",
    "        self.availableAction = ['forward', 'left', 'right',None]   \n",
    "        self.goal=0\n",
    "        self.steps=0\n",
    "        self.features=[]\n",
    "        self.deadline=[]\n",
    "        self.total_reward=[0]\n",
    "\n",
    "    def reset(self, destination=None):\n",
    "        self.planner.route_to(destination)\n",
    "        # TODO: Prepare for a new trip; reset any variables here, if required\n",
    "        #print\"RESET, Final state:\\n\", self.state\n",
    "        try:\n",
    "            if self.deadline[len(self.features)-1] >0: #deadline less than zero\n",
    "                self.goal+=1 #FIXME - order\n",
    "                print \"PASS! {} steps to goal,Goal reached {} times out of {}!\".format(\n",
    "                                                        self.deadline[len(self.features)-1],self.goal,len(self.features))\n",
    "            else:\n",
    "                print \"FAIL! {} steps to goal,Goal reached {} times out of {}!\".format(\n",
    "                                                        self.deadline[len(self.features)-1],self.goal,len(self.features))\n",
    "                pass\n",
    "        except:\n",
    "            print \"Trial 0 - Goal reached {} times out of {}!\".format(self.goal,len(self.features))\n",
    "            pass\n",
    "        print \"----------------------------------------------------------\"\n",
    "        self.features.append({})\n",
    "        self.deadline.append(None)\n",
    "        self.total_reward.append(0)\n",
    "        self.steps=0\n",
    "\n",
    "    def update(self, t):\n",
    "        # Gather inputs\n",
    "        self.steps+=1\n",
    "        self.next_waypoint = self.planner.next_waypoint()  # from route planner, also displayed by simulator\n",
    "        inputs = self.env.sense(self)\n",
    "        #self.deadline[len(self.features)] = self.env.get_deadline(self)\n",
    "        self.state=inputs\n",
    "        self.features[len(self.features)-1][self.steps]=inputs\n",
    "        self.deadline[len(self.deadline)-1] = self.env.get_deadline(self)\n",
    "\n",
    "        # TODO: Select action according to your policy\n",
    "        action = self.availableAction[random.randint(0,3)]    \n",
    "        \n",
    "        # Execute action and get reward\n",
    "        reward = self.env.act(self, action)\n",
    "        self.lastReward=reward\n",
    "        # TODO: Learn policy based on state, action, reward\n",
    "        self.total_reward[len(self.total_reward)-1] =self.total_reward[len(self.total_reward)-1]+reward\n",
    "        #print \"LearningAgent.update():deadline{}, inputs{}, action = {}, reward = {}, next_waypoint = {}\".format(\n",
    "        #                                            deadline, inputs, action, reward,self.next_waypoint, )  # [debug]\n",
    "print \"RandomAgent ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "redirecting stdout....\n",
      "stdout restored!\n",
      "Successful runs = 0\n",
      "----------------------------------------------------------\n",
      "Qtable:  no Qtable\n",
      "RandomAgent, no deadlines, Done\n"
     ]
    }
   ],
   "source": [
    "if console == False:\n",
    "    features,deadlines, rewards=run(agentType=RandomAgent,trials=2, deadline=False) #Example of a random run, with no deadline\n",
    "    print \"RandomAgent, no deadlines, Done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "redirecting stdout....\n",
      "stdout restored!\n",
      "Successful runs = 0\n",
      "----------------------------------------------------------\n",
      "Qtable:  no Qtable\n",
      "RandomAgent, with deadlines, Done\n"
     ]
    }
   ],
   "source": [
    "if console == False:\n",
    "    features,deadlines, rewards=run(agentType=RandomAgent,trials=2, deadline=True) #Example of a random run\n",
    "    print \"RandomAgent, with deadlines, Done\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Agent - Discussion:\n",
    "\n",
    "When we run an agent with a random action policy, we see that it will move about the board with no pattern, and will eventually reach the destination, in most cases. If we allow the use of deadlines, we see that the agent rarely reaches the destination in time, although it may still occur."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Identify and update state\n",
    "\n",
    "Identify a set of states that you think are appropriate for modeling the driving agent. The main source of state variables are current inputs, but not all of them may be worth representing. Also, you can choose to explicitly define states, or use some combination (vector) of inputs as an implicit state.\n",
    "\n",
    "At each time step, process the inputs and update the current state. Run it again (and as often as you need) to observe how the reported state changes through the run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StateAgent Ready\n"
     ]
    }
   ],
   "source": [
    "class StateAgent(RandomAgent):\n",
    "    \"\"\"An agent that learns to drive in the smartcab world.\"\"\"\n",
    "\n",
    "    def __init__(self, env):\n",
    "        super(StateAgent, self).__init__(env)  # sets self.env = env, state = None, next_waypoint = None, and a default color\n",
    "        self.color = 'red'  # override color\n",
    "        self.planner = RoutePlanner(self.env, self)  # simple route planner to get next_waypoint\n",
    "        # TODO: Initialize any additional variables here\n",
    "        self.availableAction = ['forward', 'left', 'right',None]   \n",
    "        self.next_waypoint   = None\n",
    "        self.goal=0\n",
    "        self.steps=0\n",
    "        self.features=[]\n",
    "        self.total_reward=[0]\n",
    "\n",
    "        \n",
    "    def update(self, t):\n",
    "        # Gather inputs\n",
    "        self.steps+=1\n",
    "        \n",
    "        self.lastWaypoint = self.next_waypoint\n",
    "        self.next_waypoint = self.planner.next_waypoint()  # from route planner, also displayed by simulator\n",
    "        inputs = self.env.sense(self)\n",
    "        \n",
    "        deadline = self.env.get_deadline(self)\n",
    "        \n",
    "        # TODO: Update state\n",
    "        \n",
    "        inputs['next_waypoint']=self.next_waypoint\n",
    "        self.state= inputs    \n",
    "        self.deadline[len(self.deadline)-1] = self.env.get_deadline(self)\n",
    "        self.features[len(self.features)-1][self.steps]=inputs\n",
    "        # TODO: Select action according to your policy\n",
    "\n",
    "        action = self.availableAction[random.randint(0,3)]    \n",
    "    \n",
    "        # Execute action and get reward\n",
    "        reward = self.env.act(self, action)\n",
    "        # TODO: Learn policy based on state, action, reward\n",
    "\n",
    "        self.total_reward[len(self.total_reward)-1] =self.total_reward[len(self.total_reward)-1]+reward\n",
    "\n",
    "        #print \"LearningAgent.update(): self.state{}, action = {}, reward = {}, next_waypoint = {}\".format(\n",
    "        #                                        self.state, action, reward,self.next_waypoint, )  # [debug]\n",
    "print \"StateAgent Ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "redirecting stdout....\n",
      "stdout restored!\n",
      "Successful runs = 12\n",
      "----------------------------------------------------------\n",
      "Qtable:  no Qtable\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\numpy\\core\\_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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bWltbaW1tzf28EXG/pNEd0u6qePgA8Lfp/jjSEFNglqT2IaazqT7E9I7cAzazPrXaBouk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\nkoP2vQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8d6e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StateAgent done\n"
     ]
    }
   ],
   "source": [
    "if console == False:\n",
    "    # run the trials for the state\n",
    "    stateFeatures,StateDeadlines,StateRewards=run(agentType=StateAgent,trials=100)    \n",
    "    statsFromRun(stateFeatures,StateDeadlines,StateRewards)\n",
    "    #scorePerRun(StateDeadlines,StateRewards)\n",
    "    print \"StateAgent done\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify and update state - Discussion.\n",
    "When we sense our environment, we perceive 5 variables, with several possible states These include: left, light, next_waypoint, oncoming, and right. We can see right away that light and next_waypoint contains new information at every poll, while the others usually have no value. \n",
    "\n",
    "It's not readily apparent that the direction of travel information of the other cars (described by left/right/oncoming) is relevant to our agent. A case could be made to remove the direction information, and only retain information about another car being present at the light. This would have the benefit of reducing the number of possible states, increasing the learning speed of the agent. This may be a valuable approach in resource constrained environments. \n",
    "\n",
    "The downside is that the agent may pick an action that causes a longer trip. Early in the learning phase, it could also pick an action incorrectly. For instance, by proceeding through a light when the opposite car is turning left. In this case, it may have previously seen a positive reward for moving through the light, because the opposite car was not turning. This time through, it will receive a negative reward, and in the future when a car is at the oncoming light, it will always wait till the intersection is clear.\n",
    "\n",
    "In the interest of correct action, we will choose to use the state as returned from the sensor, with the addition of the next_waypoint.\n",
    "\n",
    "While I have tracked the deadline, it is not apparent that it will provide useful information to the agent. It is useful to  note that the agent does not see any change in the deadline value yet. We may expect this to adapt as we implement learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement Q-Learning\n",
    "\n",
    "Implement the Q-Learning algorithm by initializing and updating a table/mapping of Q-values at each time step. Now, instead of randomly selecting an action, pick the best action available from the current state based on Q-values, and return that.\n",
    "\n",
    "Each action generates a corresponding numeric reward or penalty (which may be zero). Your agent should take this into account when updating Q-values. Run it again, and observe the behavior.\n",
    "\n",
    "What changes do you notice in the agent’s behavior?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BasicLearningAgent Ready\n"
     ]
    }
   ],
   "source": [
    "class BasicLearningAgent(RandomAgent):\n",
    "    \"\"\"An agent that learns to drive in the smartcab world.\"\"\"\n",
    "\n",
    "    def __init__(self, env):\n",
    "        super(BasicLearningAgent, self).__init__(env)  # sets self.env = env, state = None, next_waypoint = None\n",
    "        self.color = 'red'  # override color\n",
    "        self.planner = RoutePlanner(self.env, self)  # simple route planner to get next_waypoint\n",
    "        # TODO: Initialize any additional variables here\n",
    "        self.availableAction = [None,'forward', 'left', 'right']   \n",
    "        self.next_waypoint   = None\n",
    "        self.goal=0\n",
    "        self.steps=0\n",
    "        self.features=[]\n",
    "        self.Qtable={}\n",
    "        self.epsilon=0.0\n",
    "        self.gamma=0.0\n",
    "        self.total_reward=[0]\n",
    "        self.alpha = .2\n",
    "\n",
    "    def get_state(self):\n",
    "        inputs = self.env.sense(self)\n",
    "        inputs['next_waypoint']=self.planner.next_waypoint()\n",
    "        return (inputs['light'], inputs['oncoming'], inputs['right'], inputs['left'],inputs['next_waypoint'])\n",
    "    \n",
    "    def set_action(self):\n",
    "        action = self.availableAction[random.randint(0,3)]    #take a random action\n",
    "   \n",
    "        # 1-epsilon % of time, refer to the q-table for an action. take the max value from the available actions\n",
    "        if self.epsilon < random.random() and  self.Qtable.has_key(self.state): \n",
    "            action=self.availableAction[self.Qtable[self.state].index(max(self.Qtable[self.state]))]\n",
    "        return action    \n",
    "        \n",
    "    def update_q_table(self,action,reward):\n",
    "        if not self.Qtable.has_key(self.state):\n",
    "            self.Qtable[self.state]=[0,0,0,0]\n",
    "       \n",
    "        new_state=self.get_state()\n",
    "        if not self.Qtable.has_key(new_state):\n",
    "            self.Qtable[new_state]=[0,0,0,0]\n",
    "        \n",
    "        #Set the new value in the Q table based on the Q-learning method\n",
    "        #Q[s,a] ←Q[s,a] + α(r+ γ*maxQ[s',a'] - Q[s,a])\n",
    "                           \n",
    "        self.Qtable[self.state][self.availableAction.index(action)]=(\n",
    "                        self.Qtable[self.state][self.availableAction.index(action)]+ # Q[s,a] +\n",
    "                        self.alpha*   # α\n",
    "                            (reward+self.gamma*max(self.Qtable[new_state])-   # r+maxQ[s',a'] -\n",
    "                             self.Qtable[self.state][self.availableAction.index(action)]) # Q[s,a]\n",
    "                        )                                                                                            \n",
    "        \n",
    "    def update(self, t):\n",
    "                                                                                          \n",
    "        # Gather inputs\n",
    "        self.steps+=1\n",
    "        self.next_waypoint = self.planner.next_waypoint()  # from route planner, also displayed by simulator\n",
    "        \n",
    "        # TODO: Update state\n",
    "        self.state = self.get_state()\n",
    "        \n",
    "        #store details of this run\n",
    "        self.deadline[len(self.deadline)-1] = self.env.get_deadline(self)\n",
    "        self.features[len(self.features)-1][self.steps]=self.env.sense(self)\n",
    "        self.features[len(self.features)-1][self.steps]['next_waypoint']=self.planner.next_waypoint()\n",
    "        \n",
    "        # TODO: Select action according to your policy\n",
    "        action = self.set_action()\n",
    "        # Execute action and get reward\n",
    "        reward = self.env.act(self, action)\n",
    "\n",
    "        # TODO: Learn policy based on state, action, reward\n",
    "        self.update_q_table(action,reward)\n",
    "        \n",
    "        #store details about our rewards\n",
    "        self.total_reward[len(self.total_reward)-1] =self.total_reward[len(self.total_reward)-1]+reward\n",
    "\n",
    "        \n",
    "print \"BasicLearningAgent Ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "redirecting stdout....\n",
      "stdout restored!\n",
      "Successful runs = 0\n",
      "----------------------------------------------------------\n",
      "Qtable:  50\n",
      "state=light, oncoming, right, left, next_waypoint  / actions= [None, 'forward', 'left', 'right']\n",
      "\n",
      "state=green, None, forward, None, forward / action=[0.0, 0, 0, 0]\n",
      "state=green, None, None, right, forward / action=[0, 0.976, 0, 0]\n",
      "state=red, None, forward, None, left / action=[0.0, 0, -0.2, 0]\n",
      "state=green, None, None, left, right / action=[0.0, 0, 0, 0]\n",
      "state=red, left, None, None, left / action=[0, 0, 0, -0.1]\n",
      "state=green, None, forward, None, left / action=[0.0, -0.1, 0, 0]\n",
      "state=green, None, right, None, right / action=[0.0, 0, -0.1, 0]\n",
      "state=red, forward, None, None, forward / action=[0.0, 0, 0, -0.1]\n",
      "state=green, None, None, None, left / action=[0.0, 0, 0, 0]\n",
      "state=red, left, None, None, forward / action=[0.0, 0, -0.2, 0]\n",
      "state=red, None, left, None, forward / action=[0.0, 0, -0.2, 0]\n",
      "state=red, left, None, None, right / action=[0.0, 0, 0, 0]\n",
      "state=red, None, None, forward, left / action=[0.0, 0, -0.2, 0]\n",
      "state=red, None, right, None, left / action=[0.0, 0, 0, 0]\n",
      "state=red, None, None, right, left / action=[0.0, 0, 0, 0]\n",
      "state=green, right, None, None, forward / action=[0, 0, -0.2, 0]\n",
      "state=green, None, right, None, forward / action=[0, 0, -0.1, 0]\n",
      "state=red, right, None, None, left / action=[0.0, 0, 0, 0]\n",
      "state=red, None, None, None, left / action=[0.0, 0, 0, -0.1]\n",
      "state=green, None, None, None, forward / action=[0.0, 0, 0, -0.1]\n",
      "state=green, None, right, None, left / action=[0.0, -0.1, 0, 0]\n",
      "state=green, None, None, forward, forward / action=[0, 0, -0.1, 0]\n",
      "state=red, None, forward, None, right / action=[0.0, 0, 0, 0]\n",
      "state=green, left, None, forward, right / action=[0, -0.1, 0, 0]\n",
      "state=red, None, forward, None, forward / action=[0, 0, 0, -0.1]\n",
      "state=green, right, None, None, right / action=[0, 0, -0.2, 0]\n",
      "state=red, forward, None, None, left / action=[0.0, -0.2, 0, 0]\n",
      "state=red, None, None, None, right / action=[0.0, -0.2, 0, 0]\n",
      "state=green, None, None, right, left / action=[0.0, -0.1, 0, 0]\n",
      "state=red, None, right, None, right / action=[0, 0, -0.2, 0]\n",
      "state=green, None, left, None, right / action=[0.0, 0, -0.1, 0]\n",
      "state=red, forward, None, None, right / action=[0.0, 0, -0.2, 0]\n",
      "state=green, None, left, None, left / action=[0, 0, 0.4, 0]\n",
      "state=green, None, forward, None, right / action=[0.0, -0.1, 0, 0]\n",
      "state=green, None, None, right, right / action=[0.0, 0, -0.1, 0]\n",
      "state=green, None, left, None, forward / action=[0, 0.7200000000000001, 0, 0]\n",
      "state=red, None, None, right, right / action=[0.0, 0, -0.2, 0]\n",
      "state=green, left, None, None, forward / action=[0.0, 0, 0, 0]\n",
      "state=red, None, None, None, forward / action=[0.0, 0, 0, -0.1]\n",
      "state=green, forward, None, None, left / action=[0.0, -0.1, 0, 0]\n",
      "state=green, None, None, forward, left / action=[0.0, -0.1, 0, 0]\n",
      "state=red, None, None, left, forward / action=[0, 0, 0, -0.1]\n",
      "state=red, None, None, forward, right / action=[0.0, 0, -0.2, 0]\n",
      "state=red, right, None, None, right / action=[0, 0, 0, 1.1808]\n",
      "state=green, None, None, forward, right / action=[0.0, -0.1, 0, 0]\n",
      "state=green, left, None, None, left / action=[0.0, 0, 0, 0]\n",
      "state=green, None, None, None, right / action=[0.0, 0, -0.1, 0]\n",
      "state=green, forward, None, None, right / action=[0.0, -0.1, 0, 0]\n",
      "state=green, left, None, None, right / action=[0.0, 0, 0, 0]\n",
      "state=red, None, None, right, forward / action=[0, -0.2, 0, 0]\n"
     ]
    },
    {
     "data": {
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kvK/Ro0dz2mmncfvtt+Pz+diyZQvr1q1rdzwrgajuyCOPZPr06fzoRz+ioaEBy7LYtWtX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CAAAg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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99fb5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic Q Learning Agent done\n"
     ]
    }
   ],
   "source": [
    "if console == False:\n",
    "    # run the trials for the Basic Q learning agent\n",
    "    basicLearnFeatures,BLdeadlines,BLrewards=run(agentType=BasicLearningAgent,trials=100, deadline=True) \n",
    "    #statsFromRun(basicLearnFeatures,BLdeadlines,BLrewards)\n",
    "    scorePerRun(BLdeadlines,BLrewards)\n",
    "    print \"Basic Q Learning Agent done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "redirecting stdout....\n",
      "stdout restored!\n",
      "Successful runs = 97\n",
      "----------------------------------------------------------\n",
      "Qtable:  44\n",
      "state=light, oncoming, right, left, next_waypoint  / actions= ['forward', 'left', 'right', None]\n",
      "\n",
      "state=green, None, None, forward, forward / action=[0.4, -0.1, 0, 0]\n",
      "state=green, None, right, None, forward / action=[0.7200000000000001, 0, 0, 0]\n",
      "state=red, None, forward, None, left / action=[0, 0, 0, 0]\n",
      "state=red, left, None, None, left / action=[-0.2, -0.2, -0.1, 0]\n",
      "state=green, None, None, right, right / action=[0, 0, 0.4, 0]\n",
      "state=red, forward, None, None, forward / action=[-0.2, -0.2, -0.1, 0.0]\n",
      "state=green, None, None, None, left / action=[-0.1, 3.0168148658099456, 0, 0]\n",
      "state=red, left, None, None, forward / action=[-0.2, -0.2, -0.1, 0.0]\n",
      "state=green, None, None, left, forward / action=[0.7200000000000001, 0, -0.1, 0]\n",
      "state=red, None, left, None, forward / action=[0, 0, -0.1, 0]\n",
      "state=red, None, None, forward, left / action=[0, -0.2, 0, 0]\n",
      "state=red, None, right, None, left / action=[0, 0, -0.1, 0]\n",
      "state=green, right, None, None, forward / action=[0.4, 0, 0, 0]\n",
      "state=red, None, None, None, left / action=[-0.2, -0.2, -0.1, 0.0]\n",
      "state=red, forward, None, None, None / action=[0, 0, 0, 0]\n",
      "state=green, None, None, None, forward / action=[5.57232284192208, 0, 0, 0.0]\n",
      "state=green, None, forward, None, forward / action=[0.4, 0, 0, 0]\n",
      "state=red, None, forward, None, forward / action=[-0.2, 0, 0, 0]\n",
      "state=green, None, None, None, None / action=[0, 0, 0, 0]\n",
      "state=red, forward, None, None, left / action=[-0.2, -0.2, 0, 0]\n",
      "state=red, None, None, None, right / action=[-0.2, -0.2, 2.661575312205817, 0]\n",
      "state=red, None, left, None, left / action=[0, 0, 0, 0]\n",
      "state=green, None, None, right, left / action=[-0.1, 0, 0, 0.0]\n",
      "state=red, left, None, right, forward / action=[0, 0, 0, 0]\n",
      "state=green, None, left, None, right / action=[-0.1, 0, 0, 0]\n",
      "state=red, forward, None, None, right / action=[-0.2, -0.2, 0.4, 0]\n",
      "state=green, None, None, left, left / action=[-0.1, 0, 0, 0]\n",
      "state=green, None, None, forward, right / action=[-0.1, 0, 0, 0]\n",
      "state=green, None, None, right, forward / action=[0.4, -0.1, 0, 0]\n",
      "state=green, None, right, None, right / action=[-0.1, 0, 0, 0]\n",
      "state=green, None, left, None, forward / action=[2.2048, 0, 0, 0]\n",
      "state=red, None, None, None, None / action=[0, 0, 0, 0]\n",
      "state=green, left, None, None, forward / action=[4.4608, 0, 0, 0.0]\n",
      "state=green, forward, None, None, forward / action=[2.7199999999999998, -0.2, 0, 0]\n",
      "state=green, right, None, None, None / action=[0, 0, 0, 0]\n",
      "state=red, None, None, None, forward / action=[-0.2, -0.2, -0.1, 0.0]\n",
      "state=red, right, None, None, forward / action=[0, 0, -0.1, 0]\n",
      "state=red, None, None, left, left / action=[-0.2, 0, 0, 0]\n",
      "state=green, None, forward, None, left / action=[-0.1, 0, 0, 0.0]\n",
      "state=red, None, None, right, forward / action=[-0.2, -0.2, 0, 0]\n",
      "state=red, None, left, None, None / action=[0, 0, 0, 0]\n",
      "state=green, None, None, None, right / action=[-0.1, -0.1, 3.9038998422963, 0]\n",
      "state=green, left, None, None, right / action=[-0.1, 0, 0, 0]\n",
      "state=red, None, right, None, forward / action=[-0.2, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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C81UJIYQQQgjRChMnTiQ2Npb09HQWLFjAo48+6hyJbNGiRfTr14/hw4cTFxfHhAkTOHjw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CAAAg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0xI6MDFT39+PC4mKTbRnkoK+P1hlKrC0B+vxfeYVq7uQkUCoB2yI1enHOGRk0iYWEmFBrdDqK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\nUaL9fy4FDnwxOY9qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x974c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic Q Learning Agent2 done\n"
     ]
    }
   ],
   "source": [
    "class BasicLearningAgent2(BasicLearningAgent):\n",
    "    \"\"\"An agent that learns to drive in the smartcab world.\"\"\"\n",
    "\n",
    "    def __init__(self, env):\n",
    "        super(BasicLearningAgent2, self).__init__(env)  # sets self.env = env, state = None, next_waypoint = None\n",
    "        self.color = 'red'  # override color\n",
    "        self.planner = RoutePlanner(self.env, self)  # simple route planner to get next_waypoint\n",
    "        # TODO: Initialize any additional variables here\n",
    "        self.availableAction = ['forward', 'left', 'right',None]   \n",
    "        self.next_waypoint   = None\n",
    "        self.goal=0\n",
    "        self.steps=0\n",
    "        self.features=[]\n",
    "        self.Qtable={}\n",
    "        self.epsilon=0.0\n",
    "        self.gamma=0.0\n",
    "        self.total_reward=[0]\n",
    "        \n",
    "if console == False:\n",
    "    # run the trials for the Basic Q learning agent\n",
    "    basicLearn2Features,BL2deadlines,BL2rewards=run(agentType=BasicLearningAgent2,trials=100, deadline=True) \n",
    "    #statsFromRun(basicLearn2Features,BL2deadlines,BL2rewards)\n",
    "    scorePerRun(BL2deadlines,BL2rewards)\n",
    "    print \"Basic Q Learning Agent2 done\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implement Q-Learning - Discussion\n",
    "With a basic Qlearning algorithm, we note that the agent quickly learns a set of rules that allow the agent to move toward the objective. Generally  speaking the agent, is moving to the destination, but does not always make optimal choices.  We see the agent begin to obey some traffic rules and make moves toward its destination. We can see that now the reward in each run is generally positive.\n",
    "\n",
    "We don't see a large continued increase in the agent speed toward the destination after the initial runs. We still see strange behavior such as repeated right turns back to the original destination, or staying in a no action state for extended periods, without much change in pattern through the run. With Epsilon set to 0, we don't see the agent select new behaviors, and with Gamma at 0 we don't consider our future state.\n",
    "\n",
    "One interesting note is the difference made by re-ordering the available action list. Placing the 'None' value at the end plays to the bias of the 'max' function, in which if all values are the same, will select the first match. Since we initialize the list to zero, if 'None' is first in the list, the agent tends to take no action, and never reaches the goal. By reordering the list, we bias the agent toward action, allowing the agent to perform significantly better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enhance the driving agent\n",
    "\n",
    "Apply the reinforcement learning techniques you have learnt, and tweak the parameters (e.g. learning rate, discount factor, action selection method, etc.), to improve the performance of your agent. Your goal is to get it to a point so that within 100 trials, the agent is able to learn a feasible policy - i.e. reach the destination within the allotted time, with net reward remaining positive.\n",
    "\n",
    "Report what changes you made to your basic implementation of Q-Learning to achieve the final version of the agent. How well does it perform?\n",
    "\n",
    "Does your agent get close to finding an optimal policy, i.e. reach the destination in the minimum possible time, and not incur any penalties?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LearningAgent Ready\n"
     ]
    }
   ],
   "source": [
    "class LearningAgent(BasicLearningAgent):\n",
    "    \"\"\"An agent that learns to drive in the smartcab world.\"\"\"\n",
    "\n",
    "    def __init__(self, env):\n",
    "        super(LearningAgent, self).__init__(env)  # sets self.env = env, state = None, next_waypoint = None\n",
    "        self.color = 'red'  # override color\n",
    "        self.planner = RoutePlanner(self.env, self)  # simple route planner to get next_waypoint\n",
    "        # TODO: Initialize any additional variables here\n",
    "        self.availableAction = [ None,'forward', 'left', 'right']   \n",
    "        self.next_waypoint = None\n",
    "        self.goal=0\n",
    "        self.steps=0\n",
    "        self.features=[]\n",
    "        self.Qtable={}\n",
    "        self.epsilon=0.95\n",
    "        self.epsilon_end=0.03125\n",
    "        self.gamma=0.05\n",
    "        self.gamma_end=0.125\n",
    "        self.total_reward=[0] \n",
    "        self.alpha = 0.5\n",
    "    \n",
    "    def set_action(self):\n",
    "        #initially we want to prefer a random action, but later we would like to trust our experience.\n",
    "        exploration_rate=32 #rate at which we approach final epsilon-> higher is slower\n",
    "        self.epsilon = self.epsilon-(self.epsilon-self.epsilon_end)/exploration_rate\n",
    "        \n",
    "        action = self.availableAction[random.randint(0,3)]    #take a random action\n",
    "        # 1-epsilon % of time, refer to the q-table for an action. take the max value from the available actions\n",
    "        if self.epsilon < random.random() and  self.Qtable.has_key(self.state): \n",
    "            action=self.availableAction[self.Qtable[self.state].index(max(self.Qtable[self.state]))]\n",
    "        return action  \n",
    "        \n",
    "    def update_q_table(self,action,reward):\n",
    "        # if we haven't seen this state yet, initialize it\n",
    "        if not self.Qtable.has_key(self.state):\n",
    "            self.Qtable[self.state]=[random.uniform(-1,1),random.uniform(-1,1),random.uniform(-1,1),random.uniform(-1,1)]\n",
    "        #we took an action, so can now observe our new state.\n",
    "        new_state=self.get_state()\n",
    "        if not self.Qtable.has_key(new_state):\n",
    "            self.Qtable[new_state]=[random.uniform(-1,1),random.uniform(-1,1),random.uniform(-1,1),random.uniform(-1,1)]\n",
    "        \n",
    "        #initially we should have a very low gamma, as we can't trust our knowledge.\n",
    "        # as time goes by we should give more weight to our knowledge and grow gamma.\n",
    "        rate=32 #-> higher is slower\n",
    "        self.gamma=self.gamma+(self.gamma_end-self.gamma)/rate\n",
    "                \n",
    "        #Set the new value in the Q table based on the Q-learning method\n",
    "        #Q[s,a] ←Q[s,a] + α(r+ γ*maxQ[s',a'] - Q[s,a])\n",
    "                           \n",
    "        self.Qtable[self.state][self.availableAction.index(action)]=(\n",
    "                        self.Qtable[self.state][self.availableAction.index(action)]+ # Q[s,a] +\n",
    "                        self.alpha*   # α\n",
    "                            (reward+self.gamma*max(self.Qtable[new_state])-   # r+maxQ[s',a'] -\n",
    "                             self.Qtable[self.state][self.availableAction.index(action)]) # Q[s,a]\n",
    "                        )                                  \n",
    "                                                                                          \n",
    "    def update(self, t):\n",
    "                                                                                          \n",
    "        # Gather inputs\n",
    "        self.steps+=1\n",
    "        self.next_waypoint = self.planner.next_waypoint()  # from route planner, also displayed by simulator\n",
    "        \n",
    "        # TODO: Update state\n",
    "        self.state = self.get_state()\n",
    "        \n",
    "        #store details of this run\n",
    "        self.deadline[len(self.deadline)-1] = self.env.get_deadline(self)\n",
    "        self.features[len(self.features)-1][self.steps]=self.env.sense(self)\n",
    "        self.features[len(self.features)-1][self.steps]['next_waypoint']=self.planner.next_waypoint()\n",
    "        \n",
    "        # TODO: Select action according to your policy\n",
    "        action = self.set_action()\n",
    "        # Execute action and get reward\n",
    "        reward = self.env.act(self, action)\n",
    "\n",
    "        # TODO: Learn policy based on state, action, reward\n",
    "        self.update_q_table(action,reward)\n",
    "        \n",
    "        #store details about our rewards\n",
    "        self.total_reward[len(self.total_reward)-1] =self.total_reward[len(self.total_reward)-1]+reward\n",
    "\n",
    "        \n",
    "print \"LearningAgent Ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runGrid ready\n"
     ]
    }
   ],
   "source": [
    "def runGrid(agentType,trials=10, gui=False, deadline=True, delay=0):\n",
    "    \"\"\"Run the agent for a finite number of trials.\"\"\"\n",
    "    from time import time\n",
    "    # Set up environment and agent\n",
    "    \n",
    "    if gui ==False:\n",
    "        redirector=outputRedirect()\n",
    "        redirector.suppress_output()\n",
    "        delay=0 \n",
    "\n",
    "    # change these values with caution, as they significantly affect run time. \n",
    "    # A fast run will use values=3, iterations=3, and take 1.5 minutes or so with 30 trials\n",
    "    values=32\n",
    "    iterations=16\n",
    "    all_successcounts=[]\n",
    "    for r in range(values):       \n",
    "        for v in range(values):\n",
    "            for s in range(values):\n",
    "                # Now simulate it\n",
    "                success_counts=[]\n",
    "                timer=[]\n",
    "                for i in range(iterations):     \n",
    "                    starttime=time()\n",
    "                    e = Environment()  # create environment (also adds some dummy traffic)\n",
    "                    a = e.create_agent(agentType)  # create agent\n",
    "                    e.set_primary_agent(a, enforce_deadline=deadline)  # specify agent to track\n",
    "                   \n",
    "                    #agent parameter to evaluate\n",
    "                    a.alpha=(1./values)*(r+1)\n",
    "                    a.gamma_end=(1./values)*(v+1)\n",
    "                    a.epsilon_end=(1./values)*(s+1)\n",
    "                    \n",
    "                    sim = Simulator(e, update_delay=delay, display=gui)  # create simulator (uses pygame when display=True)\n",
    "                    sim.run(n_trials=trials)  # run for a specified number of trials      \n",
    "                    success_counts.append(a.goal)\n",
    "                    timer.append(time()-starttime)\n",
    "                    \n",
    "                all_successcounts.append({\n",
    "                        'time_avg':np.mean(timer),\n",
    "                        'success_counts_avg':np.mean(success_counts), \n",
    "                        'alpha':a.alpha,\n",
    "                        'gamma':a.gamma,\n",
    "                        'epsilon' :a.epsilon_end})\n",
    "\n",
    "    if gui ==False:\n",
    "        redirector.reset()  \n",
    "        \n",
    "    return pd.DataFrame(all_successcounts)\n",
    "    \n",
    "print \"runGrid ready\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run some tests searching for a best value.\n",
      "redirecting stdout....\n",
      "stdout restored!\n",
      "run time:98536.4175005 seconds\n",
      "best success:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>epsilon</th>\n",
       "      <th>gamma</th>\n",
       "      <th>success_counts_avg</th>\n",
       "      <th>time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15456</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.125</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.063125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       alpha  epsilon  gamma  success_counts_avg  time_avg\n",
       "15456    0.5  0.03125  0.125                27.5  0.063125"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if console == False:\n",
    "    trials=30 # enough that the agent has enough time to learn to success at least a few times, in most cases.\n",
    "    print \"run some tests searching for a best value.\"\n",
    "    successCounts=runGrid(agentType=LearningAgent,trials=trials, deadline=True,gui=console, delay=.1)\n",
    "    print \"run time:{} seconds\".format(successCounts.time_avg.sum()*trials)\n",
    "    print \"best success:\"\n",
    "    #display(successCounts)\n",
    "    display(successCounts[successCounts.success_counts_avg==max(successCounts.success_counts_avg)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data sample:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>epsilon</th>\n",
       "      <th>gamma</th>\n",
       "      <th>success_counts_avg</th>\n",
       "      <th>time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>13.3125</td>\n",
       "      <td>0.110375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.06250</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>15.2500</td>\n",
       "      <td>0.096000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>9.9375</td>\n",
       "      <td>0.117375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.12500</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>15.6250</td>\n",
       "      <td>0.099562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>16.3750</td>\n",
       "      <td>0.096625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     alpha  epsilon    gamma  success_counts_avg  time_avg\n",
       "0  0.03125  0.03125  0.03125             13.3125  0.110375\n",
       "1  0.03125  0.06250  0.03125             15.2500  0.096000\n",
       "2  0.03125  0.09375  0.03125              9.9375  0.117375\n",
       "3  0.03125  0.12500  0.03125             15.6250  0.099562\n",
       "4  0.03125  0.15625  0.03125             16.3750  0.096625"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data description:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>epsilon</th>\n",
       "      <th>gamma</th>\n",
       "      <th>success_counts_avg</th>\n",
       "      <th>time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32768.000000</td>\n",
       "      <td>32768.000000</td>\n",
       "      <td>32768.000000</td>\n",
       "      <td>32768.000000</td>\n",
       "      <td>32768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>14.548086</td>\n",
       "      <td>0.100236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.288539</td>\n",
       "      <td>0.288539</td>\n",
       "      <td>0.288539</td>\n",
       "      <td>6.002941</td>\n",
       "      <td>0.014488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>0.062312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.273438</td>\n",
       "      <td>0.273438</td>\n",
       "      <td>0.273434</td>\n",
       "      <td>9.187500</td>\n",
       "      <td>0.089250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.515623</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.102313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.757812</td>\n",
       "      <td>0.757812</td>\n",
       "      <td>0.757812</td>\n",
       "      <td>19.625000</td>\n",
       "      <td>0.111375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.168250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              alpha       epsilon         gamma  success_counts_avg  \\\n",
       "count  32768.000000  32768.000000  32768.000000        32768.000000   \n",
       "mean       0.515625      0.515625      0.515625           14.548086   \n",
       "std        0.288539      0.288539      0.288539            6.002941   \n",
       "min        0.031250      0.031250      0.031250            4.125000   \n",
       "25%        0.273438      0.273438      0.273434            9.187500   \n",
       "50%        0.515625      0.515625      0.515623           14.000000   \n",
       "75%        0.757812      0.757812      0.757812           19.625000   \n",
       "max        1.000000      1.000000      1.000000           27.500000   \n",
       "\n",
       "           time_avg  \n",
       "count  32768.000000  \n",
       "mean       0.100236  \n",
       "std        0.014488  \n",
       "min        0.062312  \n",
       "25%        0.089250  \n",
       "50%        0.102313  \n",
       "75%        0.111375  \n",
       "max        0.168250  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if console == False:   \n",
    "    print \"data sample:\"\n",
    "    display(successCounts.head(5))\n",
    "    print \"data description:\"\n",
    "    display(successCounts.describe())\n",
    "    #display(successCounts[successCounts.success_counts_avg==max(successCounts.success_counts_avg)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enhance the driving agent - Discussion\n",
    "We immediately  see the agent begin learning when we begin using epsilon to explore new states. The addition of gamma provides many of the same benefits, and we see that the agent learns to reach the destination as quickly as the first or second run. Following this, the agent will quickly begin to reach it's destination well before the deadline, with a positive score, the majority of times. This is despite biasing the action list to 'None', as in the previous example. Even at the end of the run, we do still see odd behaviors, as the agent tries new methods according to epsilon, or encounters new states.\n",
    "\n",
    "In addition to tuning the final epsilon and gamma, I have added the ability for each to adjust the amount they affect the outcome over time. Initially we want to prefer a random action, as our table is now initialized with random values, and we want to explore the available states, and record their effects. The opposite is true for gamma. In it's case, we would like to highly discount any initial knowledge initially, and over time grow to trust what we know. My implementation allows us to control the rate of change over time of each. I have selected starting/ending values and rates based on experimentation, and optimized these numbers further by implementing a grid search to test the values over multiple runs, etc. These were found to be:\n",
    "\n",
    "      alpha = 0.5  epsilon = 0.03125  gamma = 0.125\n",
    "\t\t\n",
    "We could also implement methods to adjust these rates based on factors other than time, for example based on the difference between the current and new values in the Q table for the state, but my initial attempts haven't been successful in finding a model that learns as fast as the current implementations.\n",
    "\n",
    "I have also changed the Q-table initialization to use random values from -1 to 1. This is intended to assist early phase learning of each state taking random actions, increasing state exploration.\n",
    "\n",
    "It should be noted that the change that most affects success from the initial algorithm was to re-order the selection of available actions, such that forward is preferred over inaction. This led to an agent that was successful  in reaching it's goal at least as often as any other enhancement, usually reaching the goal more than 96% of the time. This may be a quirk of the environment, and not something that is true generally, especially  when the learning is moved to environment's that may have a larger state space, or with a more complex reward system. We do see that the final model has a higher average reward per run.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running....\n",
      "redirecting stdout....\n",
      "stdout restored!\n",
      "Successful runs = 97\n",
      "----------------------------------------------------------\n",
      "Qtable:  49\n",
      "state=light, oncoming, right, left, next_waypoint  / actions= [None, 'forward', 'left', 'right']\n",
      "\n",
      "state=green, None, forward, None, forward / action=[-0.3635912147321301, 6.437383250078879, -0.5020974302111894, -0.3654379201029975]\n",
      "state=green, None, right, None, forward / action=[-0.0845570311950814, 0.0642223392352157, 0.24467563892434568, 0.16512051365774344]\n",
      "state=red, left, None, None, left / action=[0.03019302758930772, 0.049183349404575805, 0.012255058526398743, -0.8960775789378916]\n",
      "state=green, right, None, None, right / action=[0.25423625403028116, -0.025301789305003153, -0.3992922726437642, -0.31095738292310027]\n",
      "state=green, None, left, forward, forward / action=[0.6239323954634175, 0.22636507667847128, 0.6542537800166637, 0.35760950109026945]\n",
      "state=red, forward, None, None, forward / action=[0.051180642557227665, 0.05086749468705598, 0.02769021256586046, -0.6259582515568733]\n",
      "state=green, None, None, None, left / action=[0.1497341161681095, 0.1852307888105027, 2.280113603015881, -0.6877716652526096]\n",
      "state=red, left, None, None, forward / action=[0.22353272482538794, -0.29472619084372065, -0.7761958628561976, -0.526914032033432]\n",
      "state=red, None, None, left, right / action=[0.9660723851434092, 0.578167680527127, -0.8292240776241082, 1.4324395512923616]\n",
      "state=red, None, left, None, forward / action=[-0.8088803328613854, -0.974178084430469, -0.4107414857506908, -0.4253004664951832]\n",
      "state=green, None, left, None, None / action=[0.41272883826699025, 0.496734316890344, 0.04989483666195138, 0.29923996185548085]\n",
      "state=red, None, right, None, left / action=[-0.08893140675131672, -0.9110664367944059, -0.00026395854420147025, 0.8164668553421834]\n",
      "state=red, None, None, right, left / action=[0.09216440585494046, -0.28227672803608583, 0.20967144968371443, -0.803624212711892]\n",
      "state=green, right, None, None, forward / action=[-0.46756164478548956, -0.6631808915272088, 0.2715153374610326, -0.7541066682489286]\n",
      "state=red, left, None, None, None / action=[0.17485826831630025, 0.04491791609573936, -0.8579257827009499, 0.4249536141790691]\n",
      "state=red, None, None, None, left / action=[-0.42426259222433943, -0.9399769249025145, -0.8466712822545339, -0.033070993593163234]\n",
      "state=red, forward, None, None, None / action=[0.44440468630251995, -0.4344538952077541, -0.1831924716840907, -0.936834600632027]\n",
      "state=green, None, None, None, forward / action=[0.6125929212997375, 7.308483513482218, -0.22676726610775455, -0.23890236100070913]\n",
      "state=green, None, None, forward, forward / action=[0.24505090800479734, 1.5785036478962637, 0.5305026362486256, -0.09019146855462745]\n",
      "state=red, None, forward, None, right / action=[-0.38118233119055245, 0.06739790222356912, -0.8615463047430687, -0.76193658057926]\n",
      "state=red, None, forward, None, forward / action=[0.5261940581402335, 0.3177799975225575, -0.7078785290174755, -0.6568831879653991]\n",
      "state=green, None, None, None, None / action=[-0.5730617951877293, 0.06673909163274616, 0.690921250124382, 0.323292328280264]\n",
      "state=red, None, None, left, None / action=[0.5545531257275129, 0.5389286208703823, 0.8793377978152745, 0.9037871559295687]\n",
      "state=red, None, None, None, right / action=[0.24223813252366933, -0.7957357244663198, -0.4363672191260721, 2.1733579769176847]\n",
      "state=green, None, None, right, left / action=[0.6500541171520686, -0.15602212068286508, 0.6261929018460974, -0.271178458965454]\n",
      "state=red, forward, left, None, forward / action=[0.5548057939989419, -0.08432171659392362, 0.4731575969765649, -0.4100548516763572]\n",
      "state=green, None, left, None, right / action=[-0.568084878054574, -0.14489489272481149, 0.0009063572789473617, 1.7211584693263355]\n",
      "state=red, right, None, None, forward / action=[-0.18521236305683408, -0.7911652631728858, -0.8877299990803711, -0.7249838258160566]\n",
      "state=red, forward, None, None, right / action=[0.14281497398790455, -0.9582598447018242, 0.29219116069371376, 0.4220320422324302]\n",
      "state=green, None, None, left, left / action=[0.5335196560493751, 0.29508003261771454, 0.6169888997283162, -0.425232464632995]\n",
      "state=green, None, forward, None, right / action=[0.02005784082808566, -0.6011291955564777, -0.15444375050714276, -0.09651836554447613]\n",
      "state=green, None, None, right, forward / action=[0.11637746272406924, -0.7393222997281981, -0.38894561355084356, -0.5216516742414219]\n",
      "state=green, None, right, None, right / action=[0.4570261082489111, 0.036717749531377075, 0.5190388503303556, -0.20600194134750316]\n",
      "state=green, None, left, None, forward / action=[0.3233844547866149, 3.9596499294170555, -0.1665451538941629, 0.3840747957397912]\n",
      "state=red, None, None, None, None / action=[0.9668231673573537, -0.8778607749481608, -0.8507373995917347, -0.310620895387695]\n",
      "state=green, None, None, left, forward / action=[0.10230412234984376, -0.3159365092620734, -0.71866092131856, -0.14217893035608997]\n",
      "state=red, None, right, None, right / action=[-0.061462515809244334, 0.9915060596980136, 0.06584705088995357, -0.1598560939397986]\n",
      "state=green, left, None, None, forward / action=[-0.11816766951055824, 2.0848236114610694, -0.45539237536216115, 0.39651790492247574]\n",
      "state=red, None, None, forward, forward / action=[0.5893331183721446, 0.007148431652295173, 0.6656621483056944, -0.4181257748835987]\n",
      "state=red, None, None, None, forward / action=[2.1367924758400378e-165, -0.9414315895642199, -0.9955911711544557, -0.24727901890650322]\n",
      "state=red, None, right, None, None / action=[-0.4567989491215323, -0.44892631568055497, -0.40191414511783763, -0.6750849373261603]\n",
      "state=red, None, left, None, right / action=[-0.45239470305294516, -0.1353441039374037, 0.02793142849950958, -0.838301025107314]\n",
      "state=green, None, None, forward, left / action=[0.33246734551283974, -0.09110055699965813, 1.453911818203149, 0.9802495671770806]\n",
      "state=red, None, None, left, forward / action=[0.2656257367880628, -0.8215077890936747, 0.5938410622108752, -0.3718862172337647]\n",
      "state=red, None, None, forward, right / action=[0.2703317185836551, 0.2696497377031577, -0.8348061645466515, 0.18820413212007026]\n",
      "state=red, None, None, right, forward / action=[0.23152266757593568, -0.39561213035900544, -0.07409840418154023, -0.0006509242711556507]\n",
      "state=green, None, None, forward, right / action=[-0.5086879575111212, -0.17149344062572658, 0.4684583478797164, 1.5946659903736369]\n",
      "state=green, None, None, None, right / action=[0.4407627671401592, 0.03584992356754771, -0.14647871335142415, 2.7132239084032284]\n",
      "state=red, None, right, None, forward / action=[-0.10220468044948411, -0.100805050292472, -0.30466548645179237, -0.2685253381851326]\n"
     ]
    },
    {
     "data": {
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zkcBHiA6sqLwIdyd3ujh0AWRWNyGEEJZRO/Cx1szSiYmGcUVCtBUJfITowGqP\n7wEZ4yOEEKL1qqshOxvCwsDFBfLyrHPdxEQYMMA61xKdkwQ+QnRgtcf3gIzxEUII0Xo5OeDlBU5O\nEBxsvXI3CXxEW5PAR4gOrPbipQCeXT05V3GOiqoKG/ZKCCFER5aZCd26GR5L4FNfjx49cHNzw9vb\nGz8/P8aOHcu7776LrlUTOH/+fJYsWdLkNouLi/Hw8OCGG25oiy4DUFFRwYwZM+jZsycODg7Ex8fX\n2T958mQ8PT3x8vLCy8sLZ2dnBg8ebLa9f//73/Tt2xcvLy8mT57M2bNn6+z/9ddfGT9+PJ6enoSE\nhPCPf/yjTV5Xc0jgI0QHdmHGRymFr6uvZH2EEEK0mC0CH60hKaljjPFRSrFx40by8/M5deoUixcv\nZtmyZdx1110tbvPzzz/HxcWFrVu3kpGRYcHe1hUVFcVHH31ESEhIvX2bNm2isLCQgoICCgoKGD16\nNDNnzmywnbi4OJ599lk2bNhATk4OPXr0YPbs2ab92dnZXH/99SxcuJDc3FyOHTvGpEmT2ux1NZUE\nPkJ0YHmleabFS2v4uvjKBAdCCCFaLDMTAgMNj60V+KSlgbMz+Pu3/bUsoSa74+npyY033sgnn3zC\nihUrOHz4cIvaW7FiBQsXLuTyyy/nww8/NG1/+eWXmTFjRp1jH374YRYtWgTAyZMnGT9+PN7e3kya\nNIkHH3yQuXPnNngNJycnHnroIUaPHo2DQ+MhwMmTJ9m2bZvZtjZu3MiMGTMYMGAAjo6OPPfcc8TH\nx/PHH38A8Nprr3Hdddcxa9YsHB0dcXd3p387iGol8BGiA8svq5vxAcM4H5ngQAghREvZIuPT0Wd0\nGz58ON27d2fbtm3NPvfUqVPExcVx2223ERMTw4oVK0z7Zs2axebNmykuLgagurqatWvXcttttwEQ\nExPDyJEjyc7O5vnnn2fVqlUopVr9elauXMm4ceOIiIho0vHV1dUAHDx4EIBdu3bh6+vLmDFjCAoK\n4uabb+Z0O1ikSQIfITqw/NK6Y3wAKXUTQgjRKhcGPtZYxLSjjO9pTGhoKDk5zf/icdWqVQwePJgB\nAwYwa9YsDh8+zP79+wGIiIhg6NChrF+/HoDvvvsOd3d3hg8fTnJyMr/88gsvvPACjo6OjBkzhptu\nuskir2XVqlXMnz/f7P7rrruOtWvXcvDgQc6dO8eLL76Ig4MDJSUlAJw5c4aVK1fyj3/8g9OnT9cr\nhbMVR1uIe5JwAAAgAElEQVR3QAjRcpLxEUIIYWmZmdC7t+GxNTM+zQl8VFxcq6+po6Nb3UZtKSkp\n+Pn5Nfu8VatWcc899wCG4GncuHGsWLGC1157DYDZs2ezZs0a5syZw5o1a4iJiQHg7Nmz+Pn54eLi\nYmorPDycM2fOtOp1bN++nfT0dG655Razx0ycOJHY2FimT59OYWEhixYtwtPTk+7duwPg6urKtGnT\nGDp0KADPP/88AQEBFBYW4unp2ar+tYYEPkJ0YHmleUR6113mWsb4CCGEaI3MTBg50vDYWoFPUhI0\nZ+y7pYOW1vr5559JTU0lKiqqWef99NNPHD16lKVLl7J8+XIAioqKOHToEMuXL8fBwYEZM2bw+OOP\nk5KSwvr169m1axcAISEh5OTkUFpaagp+Tp8+3epSt5UrVzJ9+nTc3NwaPW7hwoUsXLgQgKNHj/LX\nv/6VSy+9FIDLL7+8Xj8sUYLXWlLqJkQHduF01iAZHyGEEK1jqzE+HbHUrbCwkK+//prZs2czd+5c\nBg0aZNpXWVlJWVmZ6aeiov5SE//5z3+YNGkSv//+O/v372f//v0cOHCAc+fOsXnzZgACAgIYP348\n8+fPp1evXqZJAiIiIrjyyiuJjY2loqKCn376iQ0bNjTa3/LyckpLSwFM/aqttLSUTz/9tNEyt5pz\nDx06BEBycjL33HMPixYtwtvb8Jlk/vz5rF+/nt9++42Kigpeeuklxo4da9NsD0jgI0SHduF01mDM\n+MgYHyGEEC1UO/Dp1g2ys6Gqqu2uV1JiGEfUo0fbXcPSpkyZgre3NxERESxdupTHH3+cDz74oM4x\ny5Ytw83NzfQzceLEOvvLysr47LPPeOihh+jWrRuBgYEEBgbSo0cP5s6dW2eSg5iYGL777jvTpAY1\nPvroI3bu3ElAQABLlixh1qxZODs7m+13//79cXd3JzU1leuuuw43NzeSk5NN+7/44gt8fX0ZP358\nvXMvvfRS1qxZAxgCpJiYGDw9PRk5ciRjxozhxRdfNB07YcIE/va3vzF58mSCg4M5ceIEq1evbsKd\nbVuq9mJLNumAUtrWfRCioxrzwRiWXb2MsRFjTdtWJKzguz++Y+W0lTbsmRBCiI4qJAR++QXCwgzP\nAwPht98M2Z+2kJAAc+aAcUIwlFLIZ8OWmTVrFgMHDuT555+3dVdaxdzvgHF7i2vmJOMjRAfW4Do+\nMqubEEKIFtIasrIgIOD8trYud+uoZW7twS+//MKJEyfQWvPNN9/w1VdfMXXqVFt3q92SyQ2E6MAa\nKnWTMT6dQ3pROj+c/IFZl86ydVeEEHYkLw/c3AyLidaQwKf9SktLY/r06eTk5NC9e3feeecdBg8e\nbOtutVsS+AjRgTU0uYHM6tY57Di9g+U7l0vgI4SwqNrje2q0deCTlAQ33NB27duzG2+8kRtvvNHW\n3egwpNRNiA6qsrqSkooSPLp61NkuGZ/OIb0onfRiK6wqKIToVDIzDWN6amvrRUwl4yOsRQIfISxk\n3TrDj7UUlBXg5eyFg6r7v3HNGB8ZGGrf0ovTySjOkH9nIYRFWTvjU10NR46AcYZmIdqUBD5CWMiO\nHbBzp/Wu19D4HgAXRxe6qC6UVJRYrzPC6tKL0imvKievNM/WXRFC2BFrBz6nT4OPD9h4eRfRSUjg\nI4SFFBRAfr71rtfQ+J4aMrOb/aspc5NyNyGEJVk78JEyN2FNEvgIYSFWD3zMZHxAxvnU0Foz8O2B\nFJYV2rorFpdenI6TgxPpRRL4CCEsRwIfYc8k8BHCQvLzrRv4NLSGTw2Z2c0gpTCFxKxEUgpTbN0V\ni0svSmdQt0GS8RFCWFRDgU9QUNsFPklJEvgI65HARwgLaU+lbpLxMUjMSgTgbOFZG/fE8tKK0rg8\n6HLSitpwjlkhhE1VVRkCEWtqKPDx9YXiYigttfz1OmLGp0ePHri5ueHl5UVoaCjz58+npMQwrnbX\nrl14eXnVmXhmwYIF9bbdc8893H///Y1eJzo6Gj8/PyoqKtrkdWRmZhITE0NYWBi+vr5ERUWxZ88e\n0/64uDguv/xyfH196datG7fccgupqalm29u/fz/jxo3Dx8eHiIgI/vrXv5r2/fjjj3Tp0gUvLy88\nPT3x8vJi1apVbfK6GiOBjxAW0p5K3WSMj0FSVhKA3QUHxeXFVFZX0tevr5S6CWHHvvsO5syx7jUb\nCnwcHAxZn7aY0joxsePN6KaUYuPGjRQUFJCQkMC+fftYunQpAFdeeSVaa3799VfT8du3byc8PLzO\ntvj4eMaNG2f2GqdOnWL79u04ODjw1VdftcnrKCoqYsSIEezbt4+cnBxuv/12brjhBlMQd8kll7B5\n82Zyc3NJTU2lT58+LFy40Gx7MTExREdHk5eXR1xcHP/85z/5+uuvTfvDwsIoKCigsLCQgoIC5s6d\n2yavqzES+AhhIdYudcsva2SMj4tkfMCQ8XHu4szZIvvK+KQXpxPkEUSQR5CUuglhxzIzITvbutfM\nyKgf+EDbrOVT877Zvbtl27WGmuxNYGAg1157LQkJCQA4Ojpy1VVXER8fDxiyKuXl5cycOdO0LSMj\ngyNHjjQa+KxcuZJRo0Zxxx138J///Me0fc+ePYSEhNTJHq1fv57BgwcDUFpayrx58/Dz8+OSSy7h\nlVdeITw8vMFr9OzZk0WLFhEYGIhSigULFlBeXk5SkuFLw27duhEWFgZAdXU1Dg4OHD9+3GyfT506\nRUxMDAC9evVi7NixHDp0yPxNtAEJfIRVlVSUsOvMLlt3o01YO+PT6BgfVxnjA5CYncio8FF2V+qW\nXpROkHsQQe4S+Ahhz/LyrPu+onXDGR9omwkOkpIM2R6HDvxp9MyZM2zevJm+ffuato0bN84U5MTH\nxxMVFcXYsWP58ccfTdt69epFaGio2XZXrlzJnDlziImJ4dtvvyXTWPM4YsQIPDw8+P77703Hrlmz\nhjnG1GBsbCzJycmcPHmSrVu38uGHH6KUatJrSUhIoKKigj59+pi2nT59Gl9fX9zc3Hjttdd46qmn\nzJ6/aNEiVqxYQWVlJUlJSezatYtrrrnGtD8jI4OQkBB69+7No48+asosWVMH/lUTHdEj3zzC7etv\nt3U3LK66GoqKoKwM2qgUt578UhnjczGJWYlM6DHBLjM+wR7BhoyPlLoJYbdyc60b+BQWgpMTuLrW\n39cWgU9HHN9TY+rUqXh5eREREUFQUBCxsbGmfePHj2f79u0AbNu2jaioKEaOHMnu3bsBQ+nb+PHj\nzba9fft2kpOTmTlzJkOHDqVPnz6sXr3atH/WrFmm54WFhWzatInZs2cDsHbtWp599lnT+KOHHnqo\nSa+noKCA22+/ndjYWDxrLaoUHh5Obm4u2dnZ/PWvf6Vfv35m27jhhhv47LPPcHV1ZdCgQdx1110M\nHToUgIEDB5KQkMDZs2f5/vvv2bt3L4899liT+mZJjla/oui0NiRt4IukL6jW1bbuisUVFRneKLp2\nNWR+/P3b/pqNlbr5usgYn8KyQrJLsrkq7CriT8XbujsWVZPxCfYIloyPEHYsL8/wnmIt5rI90HYZ\nn5YGPnEqrtXXj9bRLT73yy+/ZMKECWzbto2YmBiysrLw8vICYOTIkRQVFXHw4EHi4+O5//77cXd3\nJzw83LTt4YcfNtv2ypUrmTRpEr6+vgDMnj2bFStWmM6JiYlhzJgxvPPOO6xbt45hw4bR3VgvmJqa\nanoMmC1zq620tJSbbrqJ0aNH8+STTzZ4jI+PD7fffjuDBw8mNTUVhwvSdLm5uVx33XX885//ZPbs\n2aSlpXHLLbcQFBTEfffdR2BgIIGBgQBERkby8ssvM2XKFP71r39dtH+WJIGPsIr0onTu+foePr7l\nY65ZdQ3VuhoHZT8Jx4IC8PY2fFOWn2/FwEcyPmYdyT5CX/++hHqG2mXGJ8jDWOpWlI7WusmlDEKI\njiMvz1BJUFYGzs5tf73MTDB+Nq0nOBgsPVwjMRH+9KeWnduaoMUSasbYREVFMW/ePB577DHWr18P\ngLOzM8OHD2fDhg2kpaWZsiRRUVFs2LCBAwcOmB3fU1payqeffkp1dTUhISEAlJeXk5eXx4EDB7js\nsssYOHAgkZGRbNq0iTVr1pjG1QCEhoZy5swZBhgjyuTk5EZfR3l5OVOnTiUiIoJ33nmn0WMrKirI\nzMykoKAAH5+6pfYnTpzA0dGR2267zdSPWbNmsWnTJu67774G26uutv4X4fbzyVO0W1pr7t5wN3cM\nvoMJPSfg5uRmdwtKFhSAl5ch+LFWWcJFx/h08oxPUnYSAwIGEOIZYrdjfFydXOnapSsFZVb8SlgI\nYTV5eYb/Wut9xdoZn444o1tDFi1axNatWzlw4IBpW1RUFG+88QajR482bRszZgxvvPEGISEh9OzZ\ns8G21q9fj6OjI7///jv79+9n//79/P7770RFRbFixQrTcTExMbzxxhts27aNGTNmmLbPmDGDpUuX\nkpeXR0pKCm+//bbZfldWVnLLLbfg5uZWZwKF2n05cuQIWmsyMzN59NFHGTp0aL2gB6Bfv35orfn4\n44/RWpOWlsYnn3ximnQhLi7OFISdPn2axYsXM3XqVLN9aysS+Ig29/6v75NSkMILE14A7DMbkZ9v\n/cCn0emsXXzt7h43V2JWIgP8B+Dn6kdReRGllW2wAIWNpBWnEeQRBECQR5DdTdcthDDINX5/ZY+B\nT2UlHD8OteYE6DAuzLAHBAQwb948XnzxRdO28ePHk5mZSVRUlGnb2LFjyczMvOhsbnfeeSdhYWGm\n8rDAwEAeeOABVq9ebcqSzJo1i/j4eCZOnIifn5/p/CVLlhAWFkbPnj2ZNGkSM2bMwNlMunDnzp1s\n2rSJLVu24O3tbVpfZ8eOHQCkpKRw3XXX4eXlxeDBg3F0dGTdunWm8xcuXGhai8jT05N169bx2muv\n4efnx9ChQ7n88st59tlnAdi3bx+jR4/Gw8ODsWPHMmTIEN54440m3W9LklI30aaOZh/l2e+fJf6O\neLp26QoYAp/c0lx60vC3HbZyOPMwy3cu54ObP2j2uTWlbs7O1qvHvlipW2ef1S0xK5FpA6bhoBxM\nkwBE+kTaulsWUZPxAUwzu/UPsIOvTUWntXTbUoaFDmNS70m27kq7kpcHXbq0j8AnKMiygc8ff0BI\nCLi5Wa5Nazlx4kS9bRdmViZNmkRVVVWdbYGBgfW2XWjz5s0Nbp8xY0adzE54eDiVlZX1jnNzc2Pl\nypWm5++8806dMT+1jRs3rtH+PPjggzz44INm9184Pic6OrrOAqi1PfLIIzzyyCNm27IWyfiINlNR\nVcGc9XNYMm4JA7sNNG1vjxmf8qpyblt3G6t+W0Vldf0/JBdjq1I3cxkfHxcf8svy7XIiiaZKzEpk\nQIChxjnEI8SuxvnUjPEBZGY30eElZiXy3A/PEXcyztZdaXfy8iAsrH0EPjUZn1rLx7RKR57RrT1L\nS0tj586daK1JSkri1VdfZfr06bbuVrshgY9oM/9v2//Dx8WHB0Y8UGd7ewx8lvywhAjvCII9gjlT\ncKbZ51u71K28qpzK6krcnBr+qqyLQxc8u3qSX2rFeVDbkarqKo7mHKWfv2FAqb2N82ko4yNER/Xk\n1icZHDyYU/mnbN2Vdic3FyIj20fg4+FhWG+nqMgy12rNjG7CvPLycu699168vLy4+uqrmTZtGgsX\nLrR1t9oNKXUTbWL3md3865d/se/effVmb2tvgU/8qXhW7l9Jwn0JzFw7kxO5J+jh06NZbdSUurm6\nWucNqmZ8T2Mzefm6Gsb5+Lr6tn2H2plT+afo5tYN967ugCHjYy/jYM5VnKOsqsw0sUXNzG5CdETf\n//E9BzMO8u6N7xL7Y6ytu9OuVFcb3lsiItpH4APnsz61lnlpsaNHwTjuXVhQREREnUkWRF2S8REW\nV1RexJz1c3h78tuEetZflbg9BT75pfnM+2Ie7095n0D3QHr59uJEbv3a3YuxdqlbY+N7atSMpeqM\nkrKSTGVuAMEewXZT6pZebMj21AS9spaP6Kiqqqt4bMtjLLt6Gf38+3EqTzI+tRUVGca/+PtbL/DJ\nyGha4GMJjU2dLURbkcBHWNyj3z7KmPAx3Dro1gb3t6cZxx765iGu7X0tN/S7AaDFgY+1S90aG99T\noz3dZ2urPb4HjGN87KTULb3o/PgeMI7xkcBHWMHxnOMWbW/Vb6twc3Lj1kG3EuYVRkZxBuVV5Ra9\nRkeWmws+PtYdO9rUjE9TFJUXkVmcaXZ/VhYEBDSzg0K0kgQ+wqKyS7L5+ODHvHn9m2aPaS8Zn7WH\n1vLT6Z94ddKrpm29fHtxPLf5b+41pW5Wy/iU5ptdw6dGZ57ZrV7g42k/kxvUZHxqBLnLdNai7aUW\nptL/rf4WW4OtuLyYv3z/F16d9CpKKRwdHAnxDGnRGEt7lZfXcQOfgrICJqyYwGNbHjN7jAQ+whYk\n8BEWtSdlD1eGXomXs5fZY9pD4JNSkMKDmx/kw+kfmsaBAPT27W03pW6dOuOTnViv1M1egoO0orS6\ngY/M6iasYEfyDqp0FQcyLDN2YPnO5URFRjGy+0jTtkjvSCl3qyUvD3x9rfe+UlxsmLHN3d38MU0J\nfM5VnOPmj2/GxdGFpOwks8dJ4CNsQSY3EBa1O2U3V4Vd1egxtg58qnU187+czwPDH2BE2Ig6+1pT\n6pbn9Ds5Opf8/NEXP6GVGlu8tEZnHuOTmJVIf//z69rY03TW9UrdjLO6aa0bnexCiNbYnrwdJwcn\nEtISGB3eur9xqYWpvLnnTfbes7fO9h4+PWRmt1qsnfGpGXPT2J+R4GD46Sfz+yuqKvjTZ38iyD2I\nN69/k/5v9W/wb1N1taGUz9+/4XYiIyPl71knFxnZNuvuScZHWNTulN1c1b19Bz5v7XmLwvJCnol6\npt6+ALcAyqvKySvNa1abBQXwbe5bPLh3HEcinm7zOvUmjfFx7ZwZn9xzuZRUlNSZWCPII4jM4kyq\nqhtfOK4juLDUzb2rO11UFwrLLVOCJERDtp/ezq2DbmV/2v5Wt/WX7//CgqEL6s2eGekdycm8k61u\n315Ye4zPxcrcoPFFTKt1NXd+dSeV1ZWsnLaSbm7dUCiySrLqHZufb8gsOTk13NbJkyfRWpt+evXS\nHDum62xr6c+nBz9l2LJbuWruBq7/8HqLtCk/lv85efJk836Bm6jVgY9SylkptVsptU8pdUAp9bxx\nu69SaotSKkkp9a1SqvFPaaLD01qzJ2VPu874HMo4xEvxL7Fq2iocHeonPJVS9PLtxR+5fzSr3YIC\nSClNZHnU/1Lk/htjPhjD0eyjlup2PfllMsbHnKRsw4xutb8t7NqlK94u3mSfy7ZhzywjvTidYI/g\nOtuk3E20pcKyQpKykrjrirtISE9oVVsJaQlsOrqJp8c+XW9fpE+kZHxqsUXG52KBj7lSN601D21+\niFN5p/hs5md07dIVpRT9/PtxJPtIveObW+bm6Wl4n7WEjOIMupR1oyJPJobpjFod+Gity4AJWusr\ngCHA9UqpEcBi4P+01v2B74H6f+WEXTmWcwx3J3dCPEMaPa4m8NHaQss/N5HWmnlfzGPpxKX08etj\n9riWlLvl58Op4iQm9hmH46dfc8fgOxj9wWg+2PdBm7zO/NKmjfHpjKVuF05sUCPYI9guZna7sNQN\nZBFT0bZ2p+zmipArGB42nIMZB6msrmxRO1prHtvyGM+Pf77Bv18yxqeumjE+Xl6W+9DfmKYGPukN\n/KlZ8sMSdp7eyYbZG+osrN3Pvx9Hc+p/CdiSwKfQQkntjOIMVHEgpVnyhVFnZJFSN611ifGhM4Zx\nQxq4GVhh3L4CmGqJa4n2qyllbgCuTq4opThXec4KvTrvl9RfKCgr4K4r7mr0uF4+LQh8zhVSUJ7L\nwNBwzpUo7hv2AHHz4vj7rr8zY+0Mi2e48suaNsanM5a6JWYlMsC/fuBjL+N8Lix1A8n4iLa1PXk7\nY8LH4OXsRYhHSIuz2RuPbuRs4VkWDFvQ4H7J+NTVHkvdAgMNa/1UV5/f9tpPr7H28Fq+mfNNvYC2\nr19fi2R8vLwsG/hU5gdSnBFIRnEG1br64icJu2GRwEcp5aCU2gekAVu11j8DQVrrdACtdRogy1TZ\nud1ndjMybOTFD8Q2ZVirD6xm9qWzLzpgsrlTWldVQYlbEv38++HYxQEPD8O3c5cEXsKeBXvo7tWd\nIe8MIe5kXCtfwXl5pXkXz/i4dt6MT/+A/vW2h3jax1o+DWV8gt1lEVPRdnac3sHYiLEADA4ezP70\n5o/zqaiq4ImtT/DKNa80WGYMEOEdwZmCM/JB1Kg9lro5OxuyLznG79Q+2PcBb+5+k61ztxLoXv9j\nniVL3SwV+GSWZFKWE0hBjgtuTm6dsiS8M7NUxqfaWOrWHRihlLoEQ9anzmGWuJZov3al7GpSxges\nn42oqq7ik0OfMPuy2Rc9trdf86a0LiwE57BEBnQzZBlqv0m5OLrw9+v+zntT3mPWZ7PYkbyjRf2/\nUFPH+HTajE9DpW7uHX9K69LKUkoqSvB18a2zPchD1vIRbaOyupLdZ3abZnIbEjSEhLTmj/P55NAn\nhHiEMLnvZLPHuDi64Ofq1+AXFAsWQJL5mZHtUk2pm5sbVFQYftpSUwIfOD/O53DmYZ7+7mm2zN1C\nuHd4g8dastTNkmN8ijO6kZ8vC0B3RhadzlprXaCUigOuA9KVUkFa63SlVDCQYe682NhY0+Po6Gii\no6Mt2S1hBaWVpRzKOMTQkKFNOt7aH8p/PPUjwR7BDX4gvlBzx/gUFIBTyPnpkxv6du66PtcxfeB0\nEtISGBMxpll9b0hTprP2dfHtdN9kVVRVcDLvZINjuEI8Q1o0VXl7kl6UTqB7YL2sZZB7UIs+jApx\nMfvT9hPhHYGfqx8AQ4KH8PbPbze7ne/++I6Zl8y8aMa9Zma3MK+wOtt/+AGuvRb610/m2q2ajI9S\nhlKv/Py2XfemuYFPcsUuru19Lf38+5k9tq9/X47lHKNaV+Ogzn/XbusxPnmpgVRXQzeXYNKL0hnU\nbZBlGhcWFxcXR1xcnMXaa3Xgo5QKACq01vlKKVfgGuC/ga+AO4BlwDzgS3Nt1A58RMe07+w+BgQM\nqDOosTHWDnxWH1hNzGUxTTo20juS0wWnqayuNFuSUVtBARCQyICAWwFD4NPQN1MR3hEk5yc3p9tm\nNWUBU4+uHpRVlVFeVU7XLl0tct327kTuCbp7dcfF0aXevhCPEHactkzGzVbSi+uXuYH9fWtZUVWB\no4Njh1zHo6yyzDSjlT2oGd9TY0hwyzI+25O389ioxy56XM04nzHU/YIoLQ3OnGn2ZU1KK0sb/Ltg\nDS29ds0YHzj/hVpbBj4ZGc0LfA5UHuCywMsaPdajqwfezt6kFKTUyQplZUEf83MM1WPpMT4FZwMJ\nCgIfJ/v622mPLkyIvPDCC61qzxKlbiHAD0qpBGA38K3WehOGgOcapVQSMBFDMCTsVFMWLq3N18V6\na8yUVZaxPnE9sy6d1aTjnR2dCXIP4kxB095l8/Oh0ifJlE0yV48d4R1BcoFlAp+mrOOjlOp0WR9z\nZW5gH2N80ovqT2wA9jer25Nbn+TJrU/auhstMu2TaaxPXG/rblhM7fE9AN29ulNRXdGs0sr0onSy\nS7Kb9K16QzO7FRVBcTGkpDS937X9nPIzV7x7RctObqXi8mLCXw9vUSlqTcYHrDPOp7kZnwMZB7gs\nqPHABxoe52OrUreKqgoKywvxdfHF3x+8usjEMJ2NJaazPqC1Hqq1HqK1vlxr/f+M23O01ldrrftr\nrSdprZu3IqToUJo6o1sNa2Z8vjn2DZcGXkp3r+5NPqc55W65+VWUuR8zpfsbDXwskPHRWjdpOmvo\nfON8Ggt8gj06/hifhtbwAfub1S0hPYE3dr/RpmthtYWatcy2HN9i665YhNaa7cnb6wQ+SikGBw1u\n1kKmO07vYFT4qDqlTuZEetef2a1m3ZiWZnw+/O1DjmQfoaKqjQfJNOCTQ5+QVZLVor/9NWN8oH0G\nPgczDnJp4KUXPb6hcT7Z2bYpdcsqycLbyZ+gQAd8fMBd29eXRuLiLDK5gRC7zzQv42PND+SrD64m\n5tKmlbnVaE7gczz7JC6VQaYyP3NvUOFe4RYJfEorS3FQDk0qnbDWzG4NzdpjC4nZ58daXahmOmtr\nrx9lSZ0l43Mk+wh3D72bp/7vKYu3XV5VzoH0AxZvFyClMIW80jy+/+P7Nmnf2v7I+wOlFD18etTZ\n3txyt+3J2xkbPvbiB9LwlNZpaeDg0LKMT1V1FZ8e/hQ3JzeLlRo3x7t738XHxYfUwtRmnVdZCSUl\n4OFheN7WgU9pKZSXG0rKLiYoCE5mZlJWVUaYZ9hFj29oSmtbTWedUZyBVxdDmZu3N7hUdbwvjbKy\nGl5EVjSNBD6i1TKLM8k5l9PgFMLmWCvwKSwr5Jtj33DroFubdV4v314cz2nalNbHchPxrTqfZTD3\nBhXqGWpYP6CFi//VaMr4nhrWuM8H0g/Q/63+7WLhwcYyPp7OnigUheUWKhS3AXNjfDy6eqC1pqi8\nyAa9sqyi8iJyzuWwfNJyfj37K/Gn4i3WdlJWEqP+ZxSj/mcU5yosv45YQloCE3pOIPtcNikFLazL\nakd2JO9gTPiYeuOVhgQPISG9mYFPRNMCnx4+PTiZd7LOtrQ0uOSSlmV8fjj5A929ujM8dLjVJzdJ\nSEsgtTCVGYNmNDvwyc83fNh3MH5Ka+vApybb05ShacHB8EexYXxPU8ayWarUzRKBT2ZJJm46kMBA\nQxmhU3nH+9Lorbfgb3+zdS86Lgl8RKvtTtnNiLARTSpjqOHn6kdOadsHPl8mfUlURBT+bv7NOq+3\nb29O5DXtTfJkcSKBDueDPnNvUE5dnAh0D2z2G+CFmjK+p4Y1xvi8t/c9XBxd+DLJ7PwlVqG1bjTw\ngTVehNIAACAASURBVI5f7tbQ4qVgKD8K9gjucN9cNuRYzjH6+PXBzcmNpROX8tiWx1q9rovWmvf3\nvs/Y/x3LgqELGBoylO/++M5CPT5vf9p+hgYPJbpHND+c/MHi7VubuYClORmf4vJiDmUe4srQK5t0\nfKR3JMn5yXUys+npMGwYpKZCcxO2qw8YMv69fZu3TIElvLf3Pe6+4m66e3Vv9vjC2mVuYL3ApymC\ngyG16uITG9S4MPCpqqr/+i7GUmN8Mooz6FrZjcBAwz3tUtLxlgJIT4dk6ycv7YYEPqLVdp3Z1awy\nN7BexmfNwTVNns2ttuaUuqWUJhLqfPGMD1hmnE9Tx/dA29/nkooSVh9czctXv9ziwOdYzjF++KP1\nHxIzSzJxUA4EuJn/GrGjT3DQ0OKlNexlLZ8j2UdM4+VmXToLB+XA6gOrW9xedkk2t3x6C2///Dbx\nd8Rz35X3MW3ANL5MtHygnpCewJDgIUzoMcEiv9O2tv10w4HPgIABnMo7RUlFyUXb2JOyh8FBg3F1\ncm3SNT2dPXHu4kxWSZZpW1oa9OwJ7u6GTEFTlVaW8kXiF/zp0j81e5mC1iouL+bjgx9z19C7CPUM\nbfYXXrVndIP2F/jkOjVtYgMwvJ8m5yebxljl5hpej2Mz5hW2VMYnozgDh3PGGd18gOKOl/GRwKd1\nJPARrdbciQ3AOoFPZnEm25O3c1P/m5p9bnPeJNOrkohws2Lg04TFS2v4urTtGJ+1h9YysvtI7hp6\nF7+k/tKif9NXdrzCi/EvtrovNdmexkovasb5dFRpRWkNZnzAfsb5HMk+Ql+/voAhk/XapNd45rtn\nmvQh+0LfnfiOIe8OoadPT3bfvZuB3QYCcPOAm/nqyFdUVVdZtO8JaQkMDh5sCHw6eMYn51wOp/NP\nc3nQ5fX2de3Slf4B/TmYcfCi7TSnzK3GheN80tIMH7a7d29eudvmo5sZEjyEUM9Qw9/0JmbxLeGT\nQ58wNmIs3b26t+jvTu0Z3aB9BT7+/lDmfZABfhef2AAMM6WGeoaaShibW+YGlh3jo4vOl7pV5gcZ\ntnWgsZ8ZGXD6tK170XFJ4CNapVpX83PKz+0y4/PZ4c+Y3HcyHl09mn1ugFsA5VXl5JVefDLCHIdE\n+vg0MfDxslDGp4mlbm19n9/d+y73DrsXNyc3/qvnf7HxyMZmnV9eVc7nv3/O3tS9rf4QmpiVyAD/\nxheoDfHo4BkfM2N8wBj42EGpW+2MD8CYiDFc1f0qXv/p9Sa3UV5VzpNbn+T2L27nf276H1699lWc\nHZ1N+3v59iLIPYhdZ3ZZrN+FZYWkFqbSz78fg7oNoriiuF2Me2upnad3clX3q8yuZdbUcjdzWaPG\nXDildVqaYUB9WFjzJjhYfXA1sy+dDTRv3KYlvLv3Xe4Zdg9AizI+7bnUTTlUowIPEUjTAh+oW+7W\nksDHYmN8ijOpyOtmmtygJN8VF0eXJr3XtxcZGYZ7WNL874IEEviIVkrKSsLP1Y9u7k38i2nk5+rX\n5mNP1hxc0+zZ3Goopejl24s/cv9o9LjskmwqKSPc5/wUw22d8WnWGJ82nNXtQPoBkvOTmdx3Mv+f\nvfMOj7JMu/hvJr1Peq+kFxIIvQjYECsq9vohIrI21r7qumtZ2+qqq2tvuMLawQqKghBKCMGEJJDe\n66RNyqRMpnx/vEwyk+nJREE915UL8s7bMpm8z3Oec+5zA1yQdIHNdrfvqr4jOSCZUK9QjrYfndT9\nlHaUWgzYOJlrfIaVw/Qr+vFz8zP6+m+liWlFV4VBJ/gnT3uSfx34l1W/O22AQWlHKQU3FXDmtDON\n7rcyeaVd69KOtB0hLTBttPHqROp8BgfhjjvsdkuTgqUktqxgfeLT0gKPPqq/j0qt4kDjARZELrDp\n2uMjrSei+PQO9/Jd1XdcnHoxcJz4dFf9Iiv72lCDFfErgIkTnxNV8amV1eKo9GWw2zrnAUyM+DyZ\n8+ToMXar8RmQMtgxpvjIZCefWt7WJrx/f6g+E8MfxOcPTAoTsbkBeDl7MagcnLK+CvU99ZS0l7A8\nfvmEz2GN3a2sswx3eTI+PmP2Km9v0wNUpM/kI61PlFS3Nw6/wQ0zbhhdET438Vy+r/7eprSsTUWb\nuDLjSuaGzyW3KXdS92Mp2ACO1/icpFY3qVxKkEeQyRCR36riAzDNbxrXZV7HwzsfNnmcRqPh9fzX\nWfj2QtbMWMPWy7eaXZC5IOkCPi/93G4T4cK2QrJCska/PzXmVJtjraur4YUXhMnYrw1LFrXxik9+\nPmwaV4pVJC0izCvMbN2dMYxPdtMSH1sUny2lW1gas3R0oUD77y8R768NNXAQOwAQ6BGIbEhm03g3\nvsbH3LhiD9hCfIraipAMZ9gUqZzglzDay8ca4qNUK3ls92Ns2L4BAFdXIRRhZJJTBqlcSl9b0Gi4\ngUx2cvVBGxoSFkgyMv6o85ko/iA+f2BSsLV/jxYikWhK608+LP6Qi1MuxtnBecLniJNYJj6lHaU4\n9Sbho8NDfolwA5tqfKZAWRsYGeCDog+4YeYNo9sC3AOYETLD6rQsuULONxXfcEnqJcwNnztp25FV\nxOckrvExleimxW9B8ekc6ESpVhLobjgDe/CUB/m89HOjdSXaAIP/5P2HPf+3h5tn32wxZndm6EyG\nlEMc6zhml3svaC3QIz7LYoU6H1uIlXYiU2y5dGZKMaQcoqC1wOyiVmZIJkXSotHEvfp6YbKuC1v6\n9+hCt8ZHrRZWuIODbVN8NhdvHrW5wZiKP9UBB7qhBlqIRWKCPIJsUpuNKT72UDxMQSq1gfhIiwgR\nW0F8hodH0yhsVXwKWwuJ8I6grKOM76u+RySyj92tXd5Od8OY4tPTc3IpPlIpBAVBdPQfxGei+IP4\n/IFJIbdpYsQHplaN2FS8aUJpbrqY5jeNqm7znvDSjlJEncl6Td8sEZ+G3snp0z3Dv36NjzbUIMon\nSm/7BUkXWJ2W9UXZF8yPnE+gRyBzIyan+Awph2juayZWEmt2P3tb3V7MfZH/5P3HbuczB3OJbvDL\nDN5KtZLtldu59vNrSXk5xe6KrVbtMUZafN18efCUB7nru7v0tpsKMLAEkUjEyqSVdkt3K2gtIDM4\nc/T7BL8E1Bq1xWeILrQTmaKp6a9qNfKb80kOSDZbHylxleDv5j9aN1Nfb6hU7W3Yy8KohTZfX7fG\np7tbSHNzdRWIjzWKj1QuZX/Dfs5LPE9v+y8Raa0baqALW9XmE7nGp1haTIx7Om2WHjd33QWhobB0\nKbM+3U9PlWBn7ugQAhLMIac+h6UxS3n6jKe587s7UalVdrG7SeVSHIcD8fAYZ3U7SRQfLfGJivpt\nEp+LP7qYg00Hp/QafxCfPzBhDIwMUNZZxozQGRM63tfNd0om5cfajyGVS1kctXhS57HW6qZs1Sc+\n3t7Q3y+sVI6Hr6svI6oReodte3prNBqU/ULjU9mQzGqr21TV+GhDDcbDlrQs3cLj6cHTqemuoW94\nYst5FZ0VxPnG4eTgZHY/e8dZ76jeYdcGm+ZgjeIzFfVLGo2GvKY87th2BxHPRfDQzofIDs1GoVJQ\n1llm12sZq+/RxbpZ66jurmZb5TaLAQbW4ILkC9hStmWyt41SraSkvUQvAU0kEtkca11fL0w+f23F\nx9okNl27W329sMA/NCS8ptFo2FO3x+ZgA9BXfLQ2NxCsbtYoPh+XfMw5iefg4eyht/2XUHx0Qw10\nYWudz4kcZ10kLSLZz4LiU1kJmzdDTQ3ceSe+JdVsf6YF1YL5ZO/+F9GYD/7QhmJcmHwhElcJ7xa8\nO2nFZ3BkkCHlMMG+woCtfU9PplYAUqmgfv4WiY9Go+HHmh95Oe/lKb3O75r49AxN4VPkd4D85nzS\nAtNwdXTV227tw3mq1IjNxZu5PO3yUX/1RGHNIFnaUcpQY7Ke1c3BAdzdjT+gRSKRoPr0GFd9RrpH\n6M3tpfX9VmoeqqHkshLysvLY47mHfYH7KFhWQMS2CCTD1lvduga77FrQW9RWRF1P3WiogS7ifOMI\n8QyxaFvrHOhkd91uViavBIR43MyQTA41H5rQPVljcwPBjtc73ItCpZjQdcajoLVg0qEM1qKt3zzx\nsXcD0zpZHY/89AjJLydzxadX4OPiw+7/283BGw9y+7zbmR022+omltZCN8raGJwdnHnmjGfYsH0D\nC95aQFlnmdkAA0tYEr2Eis6KSTcVruisIMwrDC8XL73ttsZa19fDihW/vuKT05DDwkjLSs144gNj\nqk99Tz1KtZJpvtNsvr6/mz8KlYLe4V7a2saIj7WKj6lgm6kmPuNDDXQR6hlq0+fsRA03GFYOU91d\nzfTQZPPE54EHYMMG4Zd23nmIN25k2RPJ1N9yDf6txax8LBvmzIFcQ6Vfo9GMkm+RSMSzZz7LX3f9\nFQ/f/kkRn/aBdnydgggOEhTlkzHcoK1NUHwiI3974QYdAx0o1Uq2lm6d0vCr3y3x6RvuI/Jfky80\n/z3DmM1NLheec/39lo+fCuKj0WjYVLSJKzKusLyzBUT7RNPQ24BSrTT6ukKloE5Wx3DrNDz0FxbN\n+rF163zUSjXdP3RTvr6cfeH7OBB1gPI/ldP1bRc4QMAFASS9mcSClgUs7F5I+K3hRORH4HO6D8Wr\nimn/vB31sOmu9i6OLjg7OCMfkU/oPTCG8aEG42FNutunxz5l+bTleLuMSWWTCTgo7Sglyd98ohtM\nzGtvCp0DnXQNdlHRVWHyM2JPtPa3mrW6eTl7odKokCvs87s+879n0tTbxHsr36Pi1gr+vuzvempM\nZnDmlBAfc4oPwPlJ5zMvYh5rZq5hy2VbbE6U1IWTgxMrElbwRdkXEz4HGNb3aGFrnU99PZx9tkB8\nfq22ImqNmn0N+6yyqGWFZFHQNkZ83NzG6nx0J662QiQSESOJoU5Wp6f4+PiAUmne7lQnq6Oss8wo\nGdYmu00Vxoca6CLMK8wmtXm81c3TU1DTlFPwqFEohLFbYsV6WmlHKXG+cUSGuZgmPnl5kJNjEFEY\nG5zE4axgHg5/i8NftcDq1bB2rZBaoIMaWQ1ikZhon2gAZofPZlnMMjqSnp4c8ZG34ykOJChI+N7N\nTXg//V1CThri81tWfMo7y0kLTOPshLN5/8j7U3ad3y3x2VK6hT5FH3vq9vzat3LSwliiW0mJQHpK\nSiwf7+dqX+Kj0WjI/TYX735vskOzJ30+F0cXgj2CaextRKPR0PJWC2XryhhuGQagqquKcM9IvN1d\nGD+2m1udi/aIpmNbB2U3lrE/dD/V91XjEuVC1s4sFvUuYtahWaRuSiX2b7EEXxmM9yxvHL0dcXB1\nIPCiQN648Q28D3rjd5YfjS80si9sH2Vry+ja0YVqwNBiZk+CqQ01WDNzjcl9ViavtJiWNb7wGCZJ\nfDqtU3zAfnU+hW2FzAidQahn6C/SH8SS1U0kEtlt5bJrsIuWvhZeOfcV5kXMMzp5tbaPiy2whviI\nRCLeueAd1s1aN6FJ9XisTFrJltLJ2d3G1/doESuJxcXBhdKOUqvOU18Ps2aBs7Nt/WqshbxETtf3\nXfTm9TJQPoCiTYFqSP+ZUdpRisRVQphXmMXzZQZnUthaiKJfRUxzBzf5NtBdKnjd9jbstUo1MoVo\nn2hqZbV6xEcksqz6/K/4f1yccrFR2+tUKj7GQg10MVmrm7a4fyoCDrRhA2IrZoRF0iLSg9IJCcE4\n8dFo4N574eGHGb8iqA046OgA/xAnuOkm4Yd6X3+Sa4w0/+O0f9AY+jI1nTZ0sB0HqVyKqyqI4OOP\nUZFIeI/dNSdPjc94xeck6rtqERVdFST4J7A2ey2v578+ZdHzv1vis6l4E/Mi5rG3Ye+vfSsnLYwl\numktGtZYNew1IR9uGabuyToOJh2k48YOnv7H0xSdW0TbpjZU8sk1xYzzjaO6upqSVSU0/rsRBw8H\nDk0/RNMrTZRKS4nx0re5aTGe+Gg0Grq+66L0/0pZdf0qvF71wj3ZnZl5M8nOyyb6vmjcE92tmsjJ\nhmRIgiWErQljxq4ZzPp5Fm7T3Kj9ay17A/eSPy+fqrur6NjawUjniF2T3UyFGuhiRsgMhpXDJtOy\nGnsbKWwtZEWCvh1kboSQ7DaRh11ZR5nVxMdedT4FrQVkBWeRGpj6i9jd2uRthHiGmN3HXrGsh1sO\nMyN0hsnobBgjPvYanDQaDZVdlWatblOBs+LPYl/DvklZnwvajCs+IpFoVPWxBJUKmpuFiX1Ghn3t\nbrIcGUfOPkLhmYXUP1lP+c3lHDn7CHnpeeR45/CT60/sDd5LbnIulVdXsqZkDf1F/WjUpn+3qgEV\nHj96cOPGG9kfvpcrHBqIEckZvvYQhWcV0r+ln4XBkyM+dT36ig9YjrQ2F2wT5RNFc1/zlLRRMBVq\noIWtiZLjrW4wdXY3W4MNMoIyTBOfbduEpk6rVxu8lOifSEVXxViqm0gEzzwDDz2k143TWBpglE8U\nyf3reL/pARt+Mn1I5VKcFEGjig8I76mL8uSxumnDDTw8hK/29l/7juyH8s5yEv0SWRK9hBH1CPsa\n9k3JdYx7VX7jaJe3s79hP1sv38qt3976a9/OSYmWvhbkI3Li/eL1thcXC4OUNcW5fm5+Ey6OVivV\ndH3TRcubLfTs6SHg4gCS30vm+vzr2bRiE8EHgmn7bxvl68sJOC+A4KuDkZwmQexoG9df2LAQ1X0q\nXK90JXVTKmIXMaGrQym7qQzHlx2ZftVspN6Gx2kHKNWQirb/ttH4r0ZEjiJCbwil7fI2tg9sZ+WF\nKyf0s/cM6ffxcY1yJereKKLujUI1oKL3YC89e3poermJY9cc40GvB+na00XDrAYww6s8p3vis8TH\n7Hv0+uHXuXfhvQbbNWoNilYFQ3VDDNcPc3fR3Ry56QgqpYqhuiFGpCOjE+SBkQE+UH9A/r/yR4/3\nWeRD8sZk1Bo1Db0NZomVwbU1Gso6yyw2L9XCXpHWhW2FnBJ1Cm5ObhxtP8qFKRdO+pzmYCnVDezn\nVT/UfMiiahriGYKD2IHmvmbCvcMnfc3mvmY8nD2sDu6wF7xcvFgUtYhtldu4LP2yCZ3DlNUNhDqf\nL8u/ZP3s9WbP0dYGfn7g4gLp6cIzdIVhqYjV0Gg0dG3vov4f9Qw3DRN1bxTpn6cjdhEb7KceUqPs\nUaLsVPLda98xvW46JReXMNIxgs9CH3wWC1/uye50f99N+yftdG3vwmu2F72ZvRRtULDptRlERUHQ\nchXRmjpmPTaLka0jVF5VScgNIXimm06IM4ZoiZDs1tYKqalj281FWpdIS+ga7DIZqODs4EyoZyj1\nPfVM87O99sgcXst/jYdOecjk67YqPicq8SmSFrF25lq8vYWeOnK5jrCjUglqzxNPgKPh9DLBL4F3\nfn6Xvj6dn23+fJg3D55/Hv7yF0AgPjfPutng+AXq+9g0mMjhlsPMDJ1p888plUsRDQQRrMNNJRJw\nVggLRhqNxi4q8lRCa3WDMbubLpE7mVHeWc4lqZcgEolYO3Mtrx9+fUKpkJbwuyQ+Hx8VEl8WRC6g\nRlZD92A3vm6+lg/8A6PIbcplTvgcg4dEURFcdtnUKT6DtYM0v9pM28Y2XONcCb0hlJRNKTh6OiIb\nktG4q5GM2AwcpzkSfFUwCqkC6YdSah6uYei6IYIuCyJgZQA+i3wQO5me4KsVamoequGUt0+h8M5C\nzrjvjNHXPNI8mLF7Bt/e8i1nPbkQX88qVPIYHDzGfN3BLgpUbzdzYHUTXtleJLyYgORUCSKRiPCa\ncOp/mpg5V6PR0DvcazLO2sHdAd+lvvguFT7PaqWam5++mfN7z2e4Ydj0edUapJulDNUMEbAygMBV\ngUhOlei9R8XSYmpltZydcDYatYb+wn66v++me0c3PXt7cPBywDXaFdcoV9J80vjS5UtOu/Y0XKNd\ncQ52HtWXz3r/LB445QFmRc86fnGouruK4nOKWXTNInIbc20iPk19TXg6e1rd28heVreC1gJunXMr\njmJHvqv+btLnswRLVjewXyxrfks+K5PME3ORSDSq+tiD+Fhjc5sqrExeyZayLRMiPq39rajUKoI1\nwbRtbqNjaweOEkckiyX4LPZhWcwy7vzuTtQatVEFTaPS0JffR/Xb3Twm7ybHv58zgz0pdZLQne2D\n9zxvHNytD2rRqDS0f95O/T/q0YxoiLo/isBLA00uaIhEIhzcHHBwc8AlxIV349/ly4e+JDUwleHW\nYXpyeujZ00PFnyqQH5Xju8yXwFWBJPwnAecAZ9759h32VxcQFXU6Egl0DzpQekYpWx7ewpqFa2h9\np5Ujy4/gEuFCyP+FEHRJEE7+5tMXQVB8DrccRmaD4qMNtjGnVGrtbvYkPuZCDbQI9bI+3GBoSEgF\ndXPT335CEJ+2IjKCMxCJhN9LWxvExR1/8YMPBOvaBRcYPTbRP5GyjnL8/MbZ6p54QiA/a9bQ6elA\nY28jGcEZBsf7e3mxlL9z53d38uO1P9pMUtoH2lH1BeoRBYkEhvrccXJwEsbWX3jhxVZorW4wRnxm\nzfp178le0B0Drsu6jvgX46dkfv67JD6bijZx36L7cHJwYk74HPY37jeaUPUHTMNU49KiIvjHP+Dc\ncy2fwxbio1aoaXi2gYZnGwi5PoTMHzPxSNb3D+c15TEzdKZe0b1zkDMRt0YQcWsEAxUDSP8npfre\nagYrBvE90xf/8/zxO8sP54CxRqcDZQMcveooLqEuyD6RkdeeZ3A/IrGIL2d+ifv0JQQ8Mkxeeh4J\nLyfgGuNK478aufLbdgbmBJK1MwuPFP37NJfqZgnyETkuji4WY5u1EDuKUaYpaYls4ZyZ51jcf7B2\nkI7POqj9ey0DVw7gf54/gasC8TvDj/e/eZ8HWx6k7MoyZD/IcPR3xO8MP8L/FE7ax2k4+oy974mq\nRFb9cxV3n3Y3gV5jI2pZRxml4lKWzV6mVwCcsjGF8pvLuf7Z69n3yD4uSbvE6vfE2kQ3LUI9Qydd\nmzKsHB4txNRoNDyf+/ykzmcJ2gh0f3fzzS/s1cQ0vzmfR5c9anE/bcDBOYmWP1uWUNFVQaLfr0N8\nzk86n3t33ItCpbCp6fGIbIQjbx7h8f89zoHHDuCz2IeACwNQ9ato/6ydyg2ViN3E3Bl4J/nkk3pO\nKu4p7gxWDY4uGsh2ynAJd6E32pdj0yO5+iNPDv+vn+6nZNQ8VEN/YT8e6R6jRMo9xR1Vn0pQaHqU\nKGVKVD3C94VH6hHtGyAoXEL83+LxP9cfkdj6yWFLXwuyIdno35NLiAtBq4IIWiXMtIytiGeFZPHv\nQz9yepSQaCmTwd76vSyKWoR7vDtxj8cR8/cYurd307qxlep7q5EskxB8VTD+5/rj4Gac1GkjrQfH\nEZ+ICH1HwVDDED17epAflfOd+Dteu/k1sz/jVPTyefPwmyZDDbQIdA9ENiRjRDVi8fmtVXtsqR2d\nDKwlPrIhGd1D3cRIYgBG7W5xcQhs7aGHYNMmwxs/jhDPEAaVg0SEygCdhar4eLjqKnjkEfbdupx5\nEfOMhud4eUFC52q2yV/ki7IvuCDZOMEyBalciqIrxcDqptvE9EQnPsYUn98C1Bo1lV2Voy6iAPeA\n0ZCD2+beZtdr/e6IT52sjtKO0tHEl4WRC9lbv/cP4mMjcptyuWfhPXrbpFIhHWb2bCEpRdtt2xSs\nJT49e3sou6kM1yhXsvOycYt1M7rfgcYDZpupuie4E/NQDDEPxTDcOkzXN110fNZBxZ8q8MjwEAZh\ndwfqHq0j5pEYwtaFMdA0QHW54SCp0Wgo7SjFIyKFH0/x5/LVXZTfXI5KriJ8fTi718zBK8qZlUZ6\nKUZ4R9DU14RKrbI5cls2JLO6eakWvm7W1/i4xbgR+edIIv8cyVDjEB2fd9DwTANHLz3KAvECwlaE\n4Xe2H9OemYZrpKvJ8zg5OHF2wtl8UfYF62atG92+uXgzl6ZeavBzi8QiEl9NpOnGJqZvmI5isQLn\nIOsmoNYmumkR6hXKt5XfWr2/MRxtP8o032m4ObmRHJBMWUfZhH6f1kIqlxLgHmB2JRuEwdtUbZW1\n6BrsomOgwyr1JSski89LP5/U9bQo7ywnwf+Xre/RIsQzhOSAZHbV7rIYjT3SNULH1g7aP2mnZ08P\n/Zn9DJw2wLwf5+EkGZvQRt4RiUajYaBsgN0v7aZxZyOq/6hQtClw9HXE93RfAi4MIOGlBFxCXfjn\nP0HTAi5hMP0mF0570J+HfwLRiIq+g33I9shoermJwcpBIezExwFHH0ccJY44+jiicFfwiey/dC51\n58D0jcyQzeDqwqu5KOUiqydz2kACU58zYyvsmSGZ1CmeIypKsD11dkJuQw4PLB6rxRA7ivE/xx//\nc/xR9ipp/6ydltdbKL+pnICVx63ISySIHMbOr011U+sQH41GQ5RmgOZ9PRy7pgfZHhlquRqfxT50\n+3bz1w//ikQsQXWvyiShmoqAg5/qfuK9le+Z3cdB7DCaKBnpE2l2X2M2N/j1iU+xtJjUwNTRz4dW\n8QHg5ZdhxgxYaNqaJBKJCHdNwD2iApit/+JDD0FKCqXZwyyKM25V9PaG2lpHnj3zWW7bdhtnJ5xt\n9SIgCM9RuTRIb14yGml9vJfPr6U6WwO1eiyIAgTi81uJtG7qbULiKtFrCbA2ey23fHMLt8651a4W\nxN8d8dEmvmhX9RZFLeLxPY//ynd1ckGlVnGo+RBzwufobS8qEopyRaKx4lxLxMdcc82R7hGq76um\n86tO4p+PJ3BVoNkPf25TLqtnGBZUGoNLiAuhq0MJXR2KeliNbJeMzq86GaweJOunLDxSBZXG1CDZ\nJm/DUeyIut8fb2/wO9OPOWVzQA1iZzFuj5seoFwcXfBz86NN3mZVcpIuxtf3WIOJhki4RriOqmUb\n92zkw7oP+frqr60+fmXySt48/OYo8dFoNGwu3szGlRuN7i8Sich+KZu7z76buCVxZO3IwiXcHdZH\noQAAIABJREFUckPKiSg+k63xKWwrHK3p8HLxItAjkFpZrd3rBrSwxuYGwgR+V92uSV3LmmADLbJC\nsvjbrr9ZfW5ljxLZLhld33fhFOBE9IPRoxas8s5yFkQumOhtoxpU0Zsr1Lf1H+5H7CHGOdh59Msp\nyEnv/+Otrtp0N2PERzWoovOrTto+aEO2U4bv6b4EXxNM6v9SuXr71ZyTcI4e6dFCJBLhkexB1E1R\nvFfyHlsu38JI9wiOEkeDZ1l9/ZhlyMMDwsKgogJSUhyQLJEgWWLayqnRaFj18Sp21cWySP5Pmu58\ngq8rvuaDog+4Y/sdLJ+2nKsyrmJFwgqzilZOvXX9e3SRGpiKTFxJcPgQXVJXyioV5DfnMy9intH9\nHb0dCb0+lNDrQxluHkb6PylVd1WhaFPgke6hV4d4V9VdaDp/pu06Ma0qDfJCOd7ODkgGffC5xYeo\nB6JwTxJCYW7/9nbClocR+3EseWl5xD8fj/95/gbvc5xvHB8f/dimn9EcFCoFlV2VpAQYWeUah1Av\n4dljDfHxNeLumSriI5VClvESNT1ogw20GA046O6Gp56Cn36yeI5Ah0QUweUYEJ+AALjrLua89DSq\nT4z/frQNTJfHLydWEsurh17l1rnW12m3D7TT22IYbiCTQUiUffugTQW6uoT3wPn4n3BkpNE2SCcl\njFmddUMO7Fnr87sjPpuKN/HiWS+Ofj8vYh75zfkMK4dt7vr9e8XR9qOEeIbg5+ant724WCA8IPxb\nXAynn276PKYm5BqNRhgM76wi4MIAZpfMNjqpGH9MblMur51r3uZgDGIXMX7L/fBb7mfwWoB7AAqV\nQkhS06kh0U62ezsYTXXT9dD7+AgJTaag7eVjM/EZ7rG6lkULX1ffSfereqPyDe6af5dNxyyftpzV\nW1fTO9yLt4s3h1sOo1QrDQizLnxcfdi7ai+rW1bz8+Kfyfwh06TCp0WRtIhzE63wVh6HPWp8xscX\npwamUtJeMmXEx1IPHy3skepmTbCBFon+iTT1NdE33GfQvBNAPaKm90Av3Tu66f6+G3mRHO/53vie\n7kv3D90cOesIaR+m4eTvZHONz0jXCD17hfqTnpwewRKWIVjCgq4IQj2kRtGmQNGmQF4sH/3/SNsI\nSpkS91R3vOd44zXbC6/ZXpyfcD5nfHAGL539EmKRGI1Kg2yXjLYP2ujY0oFXthdBVwWRsjEFR++x\nobOgtUBP3TCGpTFL+dM3f0KlVuHka/xZVl8P8fNKufTjv/Kfc/5DRkYAxcWQYnk+zXuF71EqraDr\n4020ZoObkxurUlexKnUVXYNdfHL0E5478Bw3fHEDcb5xJs9T3lnON1d9Y/mCOnB1dMW5LwGFpASJ\nIpva4cMk+Cfo9egyBZcwl1GFWV4qZ6h2SO/1nz7bw+DQGazeEIFIJMI9zR2ZoyuXTIe7bxzbT6VW\n8dHRj9h9/W4SLk2g6/suKm+rpPnVZuJfiMc9wX10X3srPuWd5UT5ROHmZP45BdYHHIyPstbi11Z8\nitqK9IhPcPBx4vPUU0JdjxUfVokykVbfcqOvDa1fS+yT9xNYq4JYw9d147z/eeY/OfW9U7lp1k1W\n21Ol/VLk7YH46Qz1Eom+1e1Ehq7NDX5bVjdjz/+pCjn4XRGfEmkJnQOdLI5ePLrN28WbRH8hJWR+\n5Pxf8e5OHhjr3wOCwpN9fL6Uni70MDMHiauEnqEe1Bo1KGCwepCBsgGaX21G0awg7bM0fOZZp27U\nyGpwcXCxS5G1LkQiEXG+cdR01zAjdMbo9lHiUy3I7+Ph7W1+gNISH1OroqbQM9QzIavbZGLDW/pa\nKJYWG8RPW4KXixeLoxfzbcW3XJZ+2WjvHkuS9dzwufw8+2fODz2fglMKmP79dIN6Li1qZbUUS4tZ\nEr3E6vsK8RRW9kwVm1uDgtYCzkkYq2tJDRAirc9POn9C57OEtn7rFB97DN7WBBto4Sh2JDUwlSJp\nEQsiFwjWrmMDY/Uru2W4xbvhe4YvMY/E4LPQZ9SCFHlnJNV/qSZ/dj7JnyYLipmvZeIo2y2j+v5q\n5EfkeM31QrJYQuxjsXjP9dYLGDEH1YCK/oJ++vL66P6xm/on61G0KHg4+GH2le0jxDME6f+kOIc4\nE3xVMLGPxeISZrgwJlfIqe+pt6g4hnqFEuwZTGFbock0qsr2Og40nMmMiDRWfLCCZek/UlTkxSUW\nyt1qumu4+/u7eSjqR16eZthU0s/Nj7XZa1mbvZbG3kazE28nsZPJdDpzUDdn0eFYQIpvNo3iHJZH\nGrcqmYNHsofB33lbVT+dtW34nzW2yBCkFlboh4eFBDwQbM5BHkGjVkm/M/yYVTiLxhcbOTz/MGE3\nhRH9l2gcPBxGm5jaK8FrPBkwh1BP6wIOzFndOjttvUPLsJr4SIu4OPXi0e9DQqB2TwNse8Pq/HWP\noUQGPYw7Bw51l/DdhTE88pe/wr7TDWqFtIoPQHqXI499p6Rm1RGSYi1X92s0GqRyKf5ugXrBChKJ\noKzaKxhmKqEbbAC/LeJT0VVhtJXBVIQc/K6Iz+bizVyebpj4sjByIXsb9v5BfKyEuWCD668X/p+R\nAW+/bXisRqVB9pOMgWMDDJQP8NS2pzjw7gFGmkdwjXbFPdEdv7OEgnlzqWtG78kIGbMHtMWwusRH\n2zemrkeoyxwPSytzkd6RE1JhZEOyCVndzFkKLeHL8i85K/4sm4q+tViZJKRlrUpdxebizXx/zfcW\nj5kbPpf9jfu5ef3NOHg4ULiskLhn4pAsleAaoV9X9Nbht7g642qTq60quYrBykHcU91HP08uji54\nuXjROdBJoIfp0X64eZjW91oZrBhEskyC72m+uIS5oNFoBMUnZGwylhaUxq7aXVa8IxODNT18wD6K\nj7XBBlrMd5xPzds1+Fb50r2jG5GzCL8z/Ai+Jpikd5L0gkN0IXIQMe2paXjO8OTIGUc475zzzK6a\nD1YNUnVPFX2H+oh7Io7ASwJtekbowsHdAZ8FPvgsGPtbGuke4cAbBzhWdoxwr3Ayd2QaBJOMR7G0\nmOSAZKvqDJbFLGNnzU6jxKetv41jc87g79l38sBpt7H+6/V8I7uAuP3fAKZr6VRqFdd8fg33LbyP\nxi0ZrFqFUCukMV5fHuEdYbLPzETR1wealiwq+wuZ7w9StxwWRV1h+UArICEaeUid3jaxGEJDBUU9\n9rgqsKV0Cxck6Re6i53FRN0VRfAVwVTdU8XBtIMkv52M3zJhub97qNvAtTARFEmtJz5hXmFW9RAz\nZ3WrnoL+q9YQH41GY/CzhoRA/J6HYd06wZtpBRx6EujzrjD6Wk59Dv2rzocHd8MnnzCe9Xt7g1tn\nI6z5G3zxBfMCxfj83zr4IVdI1jCDfkU/IhwI8dP/m9aO1WmewRxuOWzVz/BrYbziExIi2N90FwFO\nVpR3lhtdwJyKkIMTuoHpl1/Clcb7kNkMbX2BscZmi6IWkVOfY58L/cbxfdX3bCnbYvABVauhpERQ\nekD49+hRYbsWI90jFJ1XROUdlfQX9eMa7UrOkhz8/ufH4r7FzC2bS8aXGUTeEWnzhCa3yTgZswe0\nK4S6KO08rvj0YlUD0/HQKj62omd4AoqP6+QUn61lW61WAMbjvKTz2Fa5jR9qfiDAPYDUwFSLx8yN\nmEtuk2BcDrkuhKS3kuj4tIP8GfkciD3AsWuP0fxGMz0lPbx1+C1uzBY8L+oRNX2H+2h+rZnSNaXk\nTc9jb+BeSi4pYV/IPo5df4yOrzpQD6sJ8QwxWuejHlHT/nk7R849Ql5aHkM1Q3jO9KRjawd56Xkc\nTD/I4fWHWVi5ED/12KRpqpuYWqv4+Lj4oFApGBwZnNB1rA02GGoYovKuSg6mH+T8O85HtV2F9wJv\nsnZnMa9mHklvJBF0aZBJ0qOL4MuDGXhrgKu+uYqq+6rQqPSbZip7lFTdXUX+3Hy8sr2YUzqH4CuD\nJ0x6TMHJ14lFVyzihewXiP1brEXSA+b794zHshjjjUxlQzLO2LgcUfGVPHDa7YhEIl46+yWi/YP5\nwe8ys802n9r7FM4OzmyYv4GcHMFa7OYmTJx/KTQ0QIgok8LWAnx8NPRJ9trNluI+Eo2Df53Bdt1I\na41Gw5ayLaxMNv6Mcgl3IfWDVBJfSeTYtceovKOSJI8ku9ndiqRFRqOXjcFaq5spxceSk2CisIb4\nNPc14+zgrLdYFCcvYkbL13DPPWaO1Ie6PYEOTbnRxsc59TksjFksNDW9/34hLUmLri7iXr2HD0oy\nhZstL2fTP69jqK/bquu3D7Tj7RhoUHc8Gm5wEljdxis+Dg4C3zTV1+pkgjmr89rstbye/7rdmmWf\n0MSnrg42b4ZjkwspAuBg00EcxY7MCJlh8NrCqIXk1OfY7U39LWJYOcyd2+9k9Rer+XDVhwYP+poa\nofme9mHt4wP+/mOrU/ISOYfnHMYt0Y3s/GySXk0i8s+RNMxqoC+8b9KTmKkmPuMHyVGrW69xq5s1\nxKeh1/Y4lp4h22t8/Nz8rE51G4++4T721O2x2eamRYhnCCkBKdy+7XauTLduFSM9KJ2GngZkQ8Ls\nzf9sf9I/T2eBdAEZ32Tgs8iHnt095J+Zz+uPvI5mjYbD8w+TI8nh2DXH6D3Qi9dML5LeSmJR9yLm\nls9lVuEsvGZ60fB0A/tC9rHuv+to+6wN1aAKAHmpnKq7q9gfuZ+GZxsIXBXI/Mb5JL2eRMQtEaR/\nks7C9oUkv5NMu2s7l+6/lP1h+/n5lJ9perWJJK8kjnUcE2ybU4A2ueXmpSBYM4M8giY8gFsKNhjp\nGqHq7ioOZR1CJBKR/HYyrgWuvLz6ZcLXheMe7z4h+1BZcBk/vfATfXl9HDnnCCPdI6iVappeaSI3\nKZeR7hFmF88W7Eom0rrsgbkRc+kY6KCi0/iK9HjYQnyWxiwlpz4HpVo5um1gZIBzN51Llu8SYuse\nHlVpHMQOfHLVRoYUSq79bLXRz1V+cz7PH3ie91a+x/CQmMJCmDNHp+7iF0J9PUzzyKSwrZAOSkHh\nYTdVyak/BpWnIfHRbWJ6rOMYw8pho2O7LvxX+DP7yGxG2ke4/4n7J9xLbTxstbpZE6xirsZHW+Ni\nLyiVwjn9LIhfBsqWRkPyy7fytOvDxlf/TKBP6oezg4vBM0qtUY8VsZ92GiQmwiuvwMCAUEOUlITL\nUA/L/IuEvj8SCYkhaTx9x2xhlfzNN81eVyqX4kGQQbPP0ThrO6jlUw2p1LBZ6W/B7jaiGqG+p95k\n/aFuyIE9cEITn54eYVL59NOTP9emok1cmX6l0UE5wjsCLxcvyjrLJn+h3yCOtR9j3lvzqJHVUHBT\nAafGnmqwj26wgRbagIP2z9spWFpA9IPRJDyfoEdyJpo4poth5TBH2o6QHWZdQbatGE98BkYGaOlr\nIUYSM/oZHY8TSvGZRI3P9qrtLIhcYFWhsimsTF5JaUcpl6dfbtX+jmJHssOyyWvSLxITiUR4pHgQ\ntjaMlPdT+OfT/6Tn4x6Crw4m7sk4FrQuYE7JHJLfSSZ8fTjes71Hu9S7RrgScVsEM3bPYPax2fRm\n9KJ8V8m+kH3kZeVRsLQARJC1K4uZOTMJvT7UoF5E5CDCe7Y3uRfkUvZiGQtaFxB1fxSdX3RSOr2U\ni36+iLp2w0maObT0taCQKujN66X903Yanmug4o4Kii8s5lD2IfKm59H3c5/VqW4wuQHcVLCBakBF\n3ZN1HEw6iLJXyeyi2Ux7Zhrec7zJDMukWFqsN6G3FRVdFcTExTB9+3Q80jzIn53PocxDtH/STub2\nTJLfTMYlZOq9HGKRmAuSLmBr2Var9tdN97OEQI9AonyiRu00CpWCiz+6mHi/eK7y/xfRUfpjk5uL\nE+lHP6a0pY7bv71db2FuYGSAqz+/mufPep5In0gOHRJUdg8PnaStXwj19RAfFoC3izdf1v8XTd0i\n7LWGqO6OZtCl1mB7RMSY4qO1uVlDuJ38nEjdlErN6ho8/+RJ9YPVqBUTX6zoHe6lfaDdbGCELiar\n+ExFuEFnp2Crs+AUMyR4H32E04CMl5U30d9v/fU6OiDaM9FgceFY+zH83PzGLL1PPw2PPioQoLw8\nyMlB/cprVA6MWeqSA5I5PFwLX30FDzwAOw0VVS2kcikuyqCTWvEZb3WD30akda2sljCvMJMBY7oh\nB/bACU18envhpptg69bJ/WK1iS9XZJj2HWv7+fyBMWg0Gl7Je4VT3j2F9bPW8+mln5psoKiNstZF\nRrqG3udrqLy9koxvMgi5zrBGwR7Ep7CtkHi/eDydPSd1HlMYT3wqOiuY5jcNR7HjL251m0iNj4+L\nD/2KflRqlc3X21Jq2kJiLS5Pv5y7F9xNtCTa6mPmho/Z3YyhVlbLwaaDXHTqRQRdGoRkiQRHL+tK\nFl1CXOhZ1UPhs4XMrZxL/PPxzG+Yz7Snp5kMUdBFQZuwyu/g4YD/Cn+mfzOd1A9TWVa6jJqZNbS8\n1YJ6xPRkSiFV0PpeK0WrijgceJh9yfsoX1dO23/bGKofwjXaleCrg0l8LZGo+6I4svwIAQcCrFJ8\nQBjAJ5pal9+Sr0d81Eo1za83k5uYS39+PzNyZpD0WpJeob+XixehnqFWqyTGoO3hI3YUE/9sPPHP\nxRP3VByZOzLxzJyav2tTuDz9cl7Pf92sxQyEcaVIWqSX7mcJy2KW8WPNj6jUKq7+7GpcHV158/w3\naWwQE2kk4Xh6ijs3uH/J3oa9PLzr4dHt935/L1khWaPW7ZycsfYpvwbxiYoSos3fLngDx5aFNk2E\nzWGoLZpekXGrm1bx2Vq21eZnlPtKd7587kv6C/o5PPcw/UUTu+ESaQkpASlW9+/Sxllbwi8ZZ21L\nsMGo00Muh7vvRvTvfxMZ40CdifWeT9vbeWmcD6ujAxL8Eijv1E92y6nPYVGUTihGejo89xx8+qlQ\n75OUhIcHDA6C6vhQluSfRGlHKZqEBMEedPnlQlKBEUjlUhyHjCs+un18TmTnz3irGwiR1ie74mNN\noud1WdextXTrhN0rujihww16e2H6dKFg/l//Ev4GJoKdtTsJ9wo3+8YuilpETkMON8y8YWIX+Y2h\nXd7Omi/X0NjbSM7/5ZAUYL5BZFERnHfe2PfKHiWnfHeM7gYl2cXZOAcb9/rbg/jkNuYyL9y2dDRb\nEO0TTUNvA0q1EkexI2WdZaMpTqasbt7ewmumiowD3QPpV/QzMDKAu5O74Q4mMBHFx0HsgJeLF7Ih\nmUniagwjqhG+qfiGp8+YnOQa5RNl8znmhs/l3cJ3Tb7+1uG3uCrjKqsiZI0hxDOE+p56nAOdcV5q\nW2hDQWsBT53+lN42n3k+FD1TxHDNMH6b/aj7Rx0xf40h6KogRA4i5EVyOr/spPOrTuRH5fie7kvf\noj6uib2G9SvW89ipjxm9lvcsb1zjXLnszMtwineCP1u+P2tWLtXDappfa2awahCxqxixixixq5iA\ngwEkz0+m+WAzGoWGxhcacYlwIf3zdLxnm1b9skKyKGgtICXQiuxlIxg/8AWcHzCh89gDS2OWEiOJ\nsdgjpKq7igD3AJsWIpbFLuM/ef+hqquKzsFOvr7yaxzFjqPkYTwyMqCqxIdtj21j8TuL8XX1JSUw\nha1lWylcVzi6X04O3HB86NJrKvkLoL4eTj0VhoMz+ar8K4J6FyGTCQlck0VvYwQ9ia2MqEb0AiQi\nIuDAAaHupLKrklOiT7HpvHG+cXws/piMLzNofbuVwlMLCb81nIgNEVYvoIBt9T0gPPe7B7sNfp7x\n+CXjrG0hPrfOOf738MQTsHgxLF5MbKxgdU9L09//h+5u7s0tY1qphnmnujAzwx+xk5iODkgLSTQk\nPg05nBIl/B5Vg0IozaDXCjT1GsTSjtHnVJarmPYDYrwDxHh6e+Lk4CSEv5x6qqAQnXuu8OEYxxzb\n5e1o5IEEjQuO1MZZezp74iByoE/RNymHw1TClOJz+MTOZLAIa4iPbsjBZHFCKz5aG9Gf/wzvviuk\nV0wEm4uMhxrowt4BB5VdlVR2VdrtfL8UhpXDfFj8IVmvZZHsn8z+G/ZbJD2gr/jIS+Xkz81HkujC\nP/wzTZIesBPxMRGvbS+4OLoQ7BFMY6+wclXaUUqyv0B8TFndnJzA1VVYGDMGkUhEpHckDT22SZkT\nqfGBiSW77a7bTYJ/gs29huyBuRFzOdB4wOjq24hqhLd+Hgs1mAgm2sRUNiSjY6DDaOxyamAqB8MO\nkrUji+R3kml5p4WDSQc5EH2A4pXFKKQKYh6JYaF0IemfprNn9h7iE+PZUrrF7DXdZ7tz++rb6X2j\nl8o/VxoU/4+HNq7bGDQaDe2ftnMw9SDd33fjGuuKk58TIgcR/d39uHS44FnnSe/+Xnpze0n4dwKZ\nOzLNkh4QiE9hW6HZfUxhSDlEa38rMZKYCR1vDN99px+sYgtEIhHPnvksj+15bLTOzBhsqe/RYkn0\nEn6o+YEj0iNsuWwLro5CYps54lNUBEEeQXx/zfc8n/s8V356Je+ufHc02lWthv377aP4/PQTjJgX\nugygq/j4uPgQoEmzW7iCtMWJANdgmvqa9LZrww2+KPuCFfErrErV04VWxReJRITeEMrMvJkMlA6Q\nOy2X+qfqUfbr2zYrKqC21vA8ttT3gLAIFeQRZFGRtafVrVhaTLu83eTr1hAfpVpJWUeZEE5TVQWv\nvjpagxATY/jeHOnvZ11OCW9ucGDDx87Unn+UPV57OJCQy1/6i5j3wQycP3Km+8duOrd10vhiIzHP\nxZB2Zxr7o/eT45vD0cuO0vpOK9IPpTS/1kzD0w3UPFDD+pEKqtcco+i8IvLS83jhpRcofaKU4eZh\nWLsWzj4bVq0y+CBL5VJGZIZWN29vIZlQrT7x63yMKT7GanwKWwvtooz8UjAVZT0ea7PX8lq+7b0a\nx+OEV3y8vYXVnQsugJdfhocesu0cQ8ohPi/9nEeWPWJ2v9TAVDoHOoX0JCstJeZw34778HLx4p0L\n3pn0uaYaao2aPXV7+O+R//JZ6WdMD57O+xe+b7SWxxiGh4UVn+RkkH4kpeJPFcQ+EUvAtWFU+cDQ\nkEACjMHPzc/myf945Dblcv+i+yd1DkuY5idEWsdIYijtKGVFvFDsb8rqBmODlKcJp06kjxBpbQ2x\n1KJnuMdmqxtMLNltS+mWCae5TRYR3hE4OzhTI6sx8M9/XfE1sb6xpAelT/j8oV6hVsXKjseRtiNk\nBGUYtbakBqby5s9Cga3kFAkzds2g50APjt6OuKcYFv3vrN3JvQvv5ZZvb6Gyq5J4PyO56AhpRIoI\nBTP3zaTkohJKLikh5b8pOLgbt9cEewYbrKYC9OX3UbmhEmWPksTXEvE7Xb+aeUf1Dn5O+5mk663/\nPGqRFZLFSwdfsvk4gKquKqIl0TiK7TMcqVSwYoVgkT7X+r62esgIzuD8xPN5fPfjPHPmM0b3KWgt\nICvYNuLj6+bLK+e8wsUpF+s1fG1oME98QFBOd1yzg/2N+/WezUePCkEy2gldSAiUTbBc9ZprYONG\nWLrU+mO09z4j/DReOOsF3vhKTLed5lxtbZAsiaZOVqdHjLXhBltKt7Bm5hqbzxvlE0VzX/Oo8uIW\n40bqplTkR+XU/r2W3PhcIu+KJHx9OA7uDjzxhCAgPPus/nmKpEU29+7S2t0ifYx4G4/DlNXNy0tY\nTFOpLNfkaLFh+waUaiU/XPuDQWiJeliN8vNWFjcqaXhepKf+av91CnSiObaZMK8wPJw9YMMGuPtu\ngX3CqOKjRcPQEJflHOE/9zgQc00o0Q9Fs/zIEU53l3BJcwA5KwdZFjmA249u1P6tFrGLGHWMmjrf\nOhLXJeKR7IFLlIteQ3BdXJkCn30m9ErVqDRse2Ib7gfdyUvLwzPbk+Ar7iKw5BYcb71VCEc4/tyV\nDkgZ6so2IA4ODkJtXF/fmFqu7Qd1osGacAONRsNln1zGqtRVJp0EJxrKO8sN4uiNYUn0Es6OP5uj\nTC5B9YRWfHQnlffcAy+9JAR82IJvK74lMyTTYmNLsUjM/Mj57G2YfJ1Pz1APX1d8fcLXDBW1FXHf\njvuIeT6G27bdRoJ/AgU3FbDzup1Wkx6A0lJIilFRd0c51X+pZvq26YStCcPZGaZNE143BT83P7qG\nJq74dA50IpVLLTYQnCziJGN1PqUdpSQFJKFQCItKbibcVjbX+eTkCF5mU1979wo1PjZa3cD2ZDeN\nRsPWsq1ckGz5YTRVmBs+l9xGwzqf1/Nf56bsmyZ1blNx1pZQ0FpgsqYjJTCFo+1H9VQqn3k+eKR6\nGJCeEZWQULMsdhnnJZ7H1lLTxfRt/UIPHydfJ6Zvn46DpwMFSwsYbh02uv94q9tw0zDHrhNWSIOv\nDWbW4VkGpAdMBxtYg8zgTApaCyZ0rDU2B1sglQqrt08+ObnzPHrqo7xd8LbJ2GNbgg10sTZ7rYHl\n1JTiEx4uJPpKpcL3Cf4JXJt5rd4+OTmwSKc0wqziMzRkUoZWqaClRQiksRZqtUBAIiIEUndd1nWj\nxeIG6OiAI0esPvfgoPAVHxBDXY9+EUlYGLR097CvYR/Lpy23/oaPw9nBmVDPUIM6S49UD9I+TCPz\n+0x6D/SSOy2Xhn81UHhQZUAmNRoNxdJim6xuYF3AgSmrm1gsLKRpm3hag6PtR5HKpTy3X79WoPOb\nTvLS83DJ78BHPMJQzRD9Bf3IfpLR+UUnbe+30fTvJkpWlVCzvoYs3yz49lthQL/jjtHzxMSMER/Z\nyAgX7S3kn3dD3AVBRD8UjUgk4rXERJ5ua6DIW0RdRADpD6Xz6NmPMn3XdDK/z6Tijgp6ruwh4OwA\n3OLcTJIe0G9iKnIQ4XuqL7vW72J+83zC1oXR8WUX+3M3ULI5kfbV7zLSKSg/7fJ2+tsCDYgD6AQc\nnMCKj5bwjreQaomPdtgpaC2gua+ZzcWbT+h6JV1YOwaIRCKTC1G24IRWfHRtRCkpMH/dGgx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xl8ajDV8dWciD5B5Z+6vbz1Sd0MqviUtFKXXkf5nnIufn6Rc0+dI+mGJA77HOZYyDGyX8ymLr2O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\nPsTe3rY/JyU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8cf47b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Q Learning Agent done\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    print  \"running....\"\n",
    "\n",
    "    QLearnFeatures,QLdeadlines,QLrewards=run(agentType=LearningAgent,trials=100, deadline=True,gui=console, delay=.1)\n",
    "    #statsFromRun(QLearnFeatures,QLdeadlines,QLrewards)\n",
    "    scorePerRun(QLdeadlines,QLrewards)\n",
    "    print \"\\nQ Learning Agent done\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "We can see that the fully implemented agent has learned a set of rules governing road travel, including stop light behavior, and how to follow the route planner. Our agent quickly approaches an optimal policy state, with the caveat that it retains the ability(via epsilon settings) to learn changes. Since we have concentrated on 100 run trials,we will not see all possible states, and so can't reach a fully optimized policy, wherein the agent would follow all rules perfectly and always take the shortest path that those rules allow. We currently see behaviors, such as circling around a block, that maximize the total reward, but don't serve any other purpose. The possibility exists to save the policy and continue to learn, eventually having an entry for each possible state, and minimizing the mistakes. Until then, our agent is able to perform in a very acceptable manner for this environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#EOF - project complete and accepted 5/21/2016"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}