lab1.ipynb

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    "Parts of this assignment will be **automatically graded**. Please take note of the following:\n",
    "- Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\\rightarrow$Run All).\n",
    "- You can add additional cells, but it is not recommended to (re)move cells. Cells required for autograding cannot be moved and cells containing tests cannot be edited.\n",
    "- You are allowed to use a service such as [Google Colaboratory](https://colab.research.google.com/) to work together. However, you **cannot** hand in the notebook that was hosted on Google Colaboratory, but you need to copy your answers into the original notebook and verify that it runs succesfully offline. This is because Google Colaboratory destroys the metadata required for grading.\n",
    "- Name your notebook **exactly** `{TA_name}_{student1_id}_{student2_id}_lab{i}.ipynb`, for example `wouter_12345_67890_lab1.ipynb` (or elise or stephan, depending on your TA), **otherwise your submission will be skipped by our regex and you will get 0 points** (but no penalty as we cannot parse your student ids ;)).\n",
    "\n",
    "Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\", as well as your names below:"
   ]
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    "NAMES = [\"Ioanna Sanida\" , 'Nikos Kondylidis']"
   ]
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   "source": [
    "---"
   ]
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from tqdm import tqdm as _tqdm\n",
    "\n",
    "def tqdm(*args, **kwargs):\n",
    "    return _tqdm(*args, **kwargs, mininterval=1)  # Safety, do not overflow buffer\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "assert sys.version_info[:3] >= (3, 6, 0), \"Make sure you have Python 3.6 installed!\""
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   "source": [
    "---\n",
    "## 1. Policy Evaluation (1 point)"
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    "In this exercise we will evaluate a policy, e.g. find the value function for a policy. The problem we consider is the gridworld from Example 4.1 in the book. The environment is implemented as `GridworldEnv`, which is a subclass of the `Env` class from [OpenAI Gym](https://github.com/openai/gym). This means that we can interact with the environment. We can look at the documentation to see how we can interact with the environment."
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   "source": [
    "from gridworld import GridworldEnv\n",
    "env = GridworldEnv()\n",
    "# Lets see what this is\n",
    "?env"
   ]
  },
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   "source": [
    "# To have a quick look into the code\n",
    "??env"
   ]
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    "Now we want to evaluate a policy by using Dynamic Programming. For more information, see the [Intro to RL](https://drive.google.com/open?id=1opPSz5AZ_kVa1uWOdOiveNiBFiEOHjkG) book, section 4.1. This algorithm requires knowledge of the problem dynamics in the form of the transition probabilities $p(s',r|s,a)$. In general these are not available, but for our gridworld we know the dynamics and these can be accessed as `env.P`."
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       "  3: [(1.0, 0, 0.0, True)]},\n",
       " 1: {0: [(1.0, 1, -1.0, False)],\n",
       "  1: [(1.0, 2, -1.0, False)],\n",
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       "  3: [(1.0, 0, -1.0, True)]},\n",
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   "source": [
    "# Take a moment to figure out what P represents. \n",
    "# Note that this is a deterministic environment. \n",
    "# What would a stochastic environment look like?\n",
    "env.P\n",
    "# P[state] : { action: trans_prob, new_state, reward, is_terminal_state  }"
   ]
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   "source": [
    "def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\n",
    "    \"\"\"\n",
    "    Evaluate a policy given an environment and a full description of the environment's dynamics.\n",
    "    \n",
    "    Args:\n",
    "        policy: [S, A] shaped matrix representing the policy.\n",
    "        env: OpenAI env. env.P represents the transition probabilities of the environment.\n",
    "            env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\n",
    "            env.nS is a number of states in the environment. \n",
    "            env.nA is a number of actions in the environment.\n",
    "        theta: We stop evaluation once our value function change is less than theta for all states.\n",
    "        discount_factor: Gamma discount factor.\n",
    "    \n",
    "    Returns:\n",
    "        Vector of length env.nS representing the value function.\n",
    "    \"\"\"\n",
    "    # Start with a random (all 0) value function\n",
    "    V = np.zeros(env.nS)\n",
    "    while True:\n",
    "#         update state values until convergence\n",
    "        new_V = np.zeros(env.nS)\n",
    "\n",
    "#         for every state\n",
    "        for s in range (env.nS):\n",
    "        \n",
    "#             for every action \n",
    "            for a in range(env.nA):\n",
    "#                 retrieve environment action details\n",
    "                trans_prob, new_state, reward, is_terminal_state = env.P[s][a][0]\n",
    "                \n",
    "                new_V[s] += policy[s][a] * trans_prob * (reward + discount_factor * V[new_state])\n",
    "        \n",
    "#         if the values have converged then break\n",
    "        if np.sum (np.abs( V - new_V ) ) <= theta :\n",
    "            V = new_V\n",
    "            break\n",
    "        V = new_V\n",
    "\n",
    "    return np.array(V)"
   ]
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       "array([  0.        , -13.99999087, -19.99998647, -21.99998486,\n",
       "       -13.99999087, -17.99998808, -19.99998656, -19.99998647,\n",
       "       -19.99998647, -19.99998656, -17.99998808, -13.99999087,\n",
       "       -21.99998486, -19.99998647, -13.99999087,   0.        ])"
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   "source": [
    "# Let's run your code, does it make sense?\n",
    "random_policy = np.ones([env.nS, env.nA]) / env.nA\n",
    "V = policy_eval(random_policy, env)\n",
    "V"
   ]
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_gridworld_value(V):\n",
    "    plt.figure()\n",
    "    c = plt.pcolormesh(V, cmap='gray')\n",
    "    plt.colorbar(c)\n",
    "    plt.gca().invert_yaxis()  # In the array, first row = 0 is on top\n",
    "\n",
    "# Making a plot always helps\n",
    "plot_gridworld_value(V.reshape(env.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "5d879d65fc89af254883e1b68234e76e",
     "grade": true,
     "grade_id": "cell-b5c9d69b1731aff5",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# Test: When you hand in the nodebook we will check that the value function is (approximately) what we expected\n",
    "# but we need to make sure it is at least of the correct shape\n",
    "v = policy_eval(random_policy, env)\n",
    "assert v.shape == (env.nS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "863ed58baecbbb4923162f40084e870d",
     "grade": false,
     "grade_id": "cell-b680e98c9ff204b8",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "---\n",
    "## 2. Policy Iteration (2 points)\n",
    "Using the policy evaluation algorithm we can implement policy iteration to find a good policy for this problem. Note that we do not need to use a discount_factor for episodic tasks but make sure your implementation can handle this correctly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "cfa494b2b437f9007f6b29b1ed5e0f78",
     "grade": false,
     "grade_id": "cell-383c54749617512c",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def policy_improvement(env, discount_factor=1.0):\n",
    "    \"\"\"\n",
    "    Policy Improvement Algorithm. Iteratively evaluates and improves a policy\n",
    "    until an optimal policy is found.\n",
    "    \n",
    "    Args:\n",
    "        env: The OpenAI envrionment.\n",
    "        policy_eval_fn: Policy Evaluation function that takes 3 arguments:\n",
    "            policy, env, discount_factor.\n",
    "        discount_factor: gamma discount factor.\n",
    "        \n",
    "    Returns:\n",
    "        A tuple (policy, V). \n",
    "        policy is the optimal policy, a matrix of shape [S, A] where each state s\n",
    "        contains a valid probability distribution over actions.\n",
    "        V is the value function for the optimal policy.\n",
    "        \n",
    "    \"\"\"\n",
    "    # Start with a random policy\n",
    "    policy = np.ones([env.nS, env.nA]) / env.nA\n",
    "    \n",
    "    while True:\n",
    "        V = policy_eval(policy, env, discount_factor)\n",
    "        \n",
    "        policy_stable_flag = True\n",
    "#         for every state\n",
    "        for s in range(env.nS):\n",
    "            old_preference = np.argmax(policy[s])\n",
    "            \n",
    "#             for every action \n",
    "            for a in range(env.nA):\n",
    "        \n",
    "                trans_prob, new_state, reward, is_terminal_state = env.P[s][a][0]\n",
    "                policy[s][a] = trans_prob * (reward + discount_factor * V[new_state])\n",
    "        \n",
    "                    \n",
    "            new_preference = np.argmax(policy[s])\n",
    "#             greedy policy iteration (seting probability of 1 to the best action and 0 to the rest)\n",
    "            policy[s] = np.zeros(env.nA)\n",
    "            policy[s][new_preference] = 1\n",
    "            \n",
    "            \n",
    "            if new_preference != old_preference:\n",
    "                policy_stable_flag = False\n",
    "            \n",
    "        if policy_stable_flag :\n",
    "            break\n",
    "            \n",
    "            \n",
    "    \n",
    "    return policy, V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "c4ab9c8d01a5902c276a3fbfbcc89e01",
     "grade": true,
     "grade_id": "cell-8c62e92d1f34720b",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Policy Probability Distribution:\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 1. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [1. 0. 0. 0.]]\n",
      "\n",
      "Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\n",
      "[[0 3 3 2]\n",
      " [0 0 0 2]\n",
      " [0 0 1 2]\n",
      " [0 1 1 0]]\n",
      "^<<v\n",
      "^^^v\n",
      "^^>v\n",
      "^>>^\n",
      "\n",
      "Value Function:\n",
      "[ 0. -1. -2. -3. -1. -2. -3. -2. -2. -3. -2. -1. -3. -2. -1.  0.]\n",
      "\n",
      "Reshaped Grid Value Function:\n",
      "[[ 0. -1. -2. -3.]\n",
      " [-1. -2. -3. -2.]\n",
      " [-2. -3. -2. -1.]\n",
      " [-3. -2. -1.  0.]]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see what it does\n",
    "policy, v = policy_improvement(env)\n",
    "print(\"Policy Probability Distribution:\")\n",
    "print(policy)\n",
    "print(\"\")\n",
    "\n",
    "def print_grid_policy(policy, symbols=[\"^\", \">\", \"v\", \"<\"]):\n",
    "    symbols = np.array(symbols)\n",
    "    for row in policy:\n",
    "        print(\"\".join(symbols[row]))\n",
    "\n",
    "print(\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\")\n",
    "print(np.reshape(np.argmax(policy, axis=1), env.shape))\n",
    "print_grid_policy(np.reshape(np.argmax(policy, axis=1), env.shape))\n",
    "print(\"\")\n",
    "\n",
    "print(\"Value Function:\")\n",
    "print(v)\n",
    "print(\"\")\n",
    "\n",
    "print(\"Reshaped Grid Value Function:\")\n",
    "print(v.reshape(env.shape))\n",
    "print(\"\")\n",
    "\n",
    "plot_gridworld_value(v.reshape(env.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "eee712b931eb830cb89792ef30675558",
     "grade": true,
     "grade_id": "cell-695dc14dbc6a8f95",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# This is not an empty cell. It is needed for grading."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "c926edcbca292c79812c5b27eab63108",
     "grade": false,
     "grade_id": "cell-ceb755afcff43612",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "---\n",
    "## 3. Value Iteration (3 points)\n",
    "Now implement the value iteration algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "87f515e22f7ad0ea461271479dff3f5e",
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     "locked": false,
     "schema_version": 1,
     "solution": true
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   },
   "outputs": [],
   "source": [
    "def value_iteration(env, theta=0.0001, discount_factor=1.0):\n",
    "    \"\"\"\n",
    "    Value Iteration Algorithm.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI env. env.P represents the transition probabilities of the environment.\n",
    "            env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\n",
    "            env.nS is a number of states in the environment. \n",
    "            env.nA is a number of actions in the environment.\n",
    "        theta: We stop evaluation once our value function change is less than theta for all states.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        \n",
    "    Returns:\n",
    "        A tuple (policy, V) of the optimal policy and the optimal value function.        \n",
    "    \"\"\"\n",
    "    \n",
    "\n",
    "    V = np.zeros(env.nS)\n",
    "    policy = np.zeros([env.nS, env.nA])\n",
    "    \n",
    "    while True:\n",
    "        \n",
    "        diff = 0\n",
    "        \n",
    "        policy_stable_flag = True\n",
    "#         for every state\n",
    "        for s in range(env.nS):\n",
    "            v = V[s]\n",
    "        \n",
    "#             for every action \n",
    "            for a in range(env.nA):\n",
    "        \n",
    "                trans_prob, new_state, reward, is_terminal_state = env.P[s][a][0]\n",
    "                policy[s][a] = trans_prob * (reward + discount_factor * V[new_state])\n",
    "            \n",
    "            best_action = np.argmax(policy[s])\n",
    "            V[s] = policy[s][best_action]\n",
    "            \n",
    "            diff += np.abs(v - V[s])\n",
    "            \n",
    "        if diff <= theta :\n",
    "            break\n",
    "            \n",
    "#     set deterministic policy based on the converged state values\n",
    "    for s in range(env.nS):\n",
    "                    \n",
    "        best_action = np.argmax(policy[s])\n",
    "#         greedy policy iteration (seting probability of 1 to the best action and 0 to the rest)\n",
    "        policy[s] = np.zeros(env.nA)\n",
    "        policy[s][best_action] = 1\n",
    "        \n",
    "            \n",
    "    return policy, V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "d103427f5b98a8957ad486243f98e64c",
     "grade": true,
     "grade_id": "cell-b82ed3adfeecc757",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Policy Probability Distribution:\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 1. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [1. 0. 0. 0.]]\n",
      "\n",
      "Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\n",
      "[[0 3 3 2]\n",
      " [0 0 0 2]\n",
      " [0 0 1 2]\n",
      " [0 1 1 0]]\n",
      "^<<v\n",
      "^^^v\n",
      "^^>v\n",
      "^>>^\n",
      "\n",
      "Value Function:\n",
      "[ 0. -1. -2. -3. -1. -2. -3. -2. -2. -3. -2. -1. -3. -2. -1.  0.]\n",
      "\n",
      "Reshaped Grid Value Function:\n",
      "[[ 0. -1. -2. -3.]\n",
      " [-1. -2. -3. -2.]\n",
      " [-2. -3. -2. -1.]\n",
      " [-3. -2. -1.  0.]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Oh let's test again\n",
    "# Let's see what it does\n",
    "policy, v = value_iteration(env)\n",
    "print(\"Policy Probability Distribution:\")\n",
    "print(policy)\n",
    "print(\"\")\n",
    "\n",
    "print(\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\")\n",
    "print(np.reshape(np.argmax(policy, axis=1), env.shape))\n",
    "print_grid_policy(np.reshape(np.argmax(policy, axis=1), env.shape))\n",
    "print(\"\")\n",
    "\n",
    "print(\"Value Function:\")\n",
    "print(v)\n",
    "print(\"\")\n",
    "\n",
    "print(\"Reshaped Grid Value Function:\")\n",
    "print(v.reshape(env.shape))\n",
    "print(\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "3add7d8b6101d0e3b6250b6bb064566c",
     "grade": false,
     "grade_id": "cell-ded21ac846e244a3",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "What is the difference between value iteration and policy iteration? Which algorithm is most efficient (e.g. needs to perform the least *backup* operations)? Please answer *concisely* in the cell below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "078f713af4c6bf3af8fb31b8da772758",
     "grade": true,
     "grade_id": "cell-940a8d8e21f18f69",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    }
   },
   "source": [
    "The policy iteration algorith iteratively updates the policy and the state values until there is no difference on the prefered actions for every state.\n",
    "\n",
    "The value iteration, updates iteratively only the value functions, and as soon as they converge, a deterministic policy is being greedily formed. \n",
    "\n",
    "For every loop, the policy iteration algorithm needs to back up one value for every state, denoting the prefered action. The value iteration algorithm also needs to back up one value for every state (in order to calculate the total difference), which is it's state value. The policy iteration is more efficient, as the policy can converge, without needing the state values to have converged at that time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "98ec4e85c09c116f6fe1658fa0451e33",
     "grade": false,
     "grade_id": "cell-7ab207a9f93cf4d3",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## 4. Monte Carlo Prediction (7 points)\n",
    "What is the difference between Dynamic Programming and Monte Carlo? When would you use the one or the other algorithm?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "dba98a584a2d9c97735f96547ac7442a",
     "grade": true,
     "grade_id": "cell-74a904ed87b8e2cc",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    }
   },
   "source": [
    "Monte Carlo evaluates states based on predefined traverses (episodes). This way, the value of a state depends on the specific previous states that the agent \"visited\" on the same episode, and not on all states. Dynamic Programming on the other hand, takes into account the values of all \"adjacent\" states, when it evaluates one state. Dynamic Programming assumes that we have complete knowledge of the environment ( transition probabilities, and rewards for all combinations of state-action pairs). So, when the details of the system are unknown we have to use Monte Carlo, otherwise Dynamic Programming is expected to return more accurate evaluations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "0d826feda7dc9cab51ad9db8ccbdfadf",
     "grade": false,
     "grade_id": "cell-5f0c1d608436b67b",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "For the Monte Carlo Prediction we will look at the Blackjack game (Example 5.1 from the book), for which the `BlackjackEnv` is implemented in `blackjack.py`. Note that compared to the gridworld, the state is no longer a single integer, which is why we use a dictionary to represent the value function instead of a numpy array. By using `defaultdict`, each state gets a default value of 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "fb7a884505c5973aff2fe9998cc104e2",
     "grade": false,
     "grade_id": "cell-a342b69fcfdea5b2",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "from blackjack import BlackjackEnv\n",
    "env = BlackjackEnv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "26ae90f402b5de7d07f23e776a525c4b",
     "grade": false,
     "grade_id": "cell-7366692dee80c32c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "For the Monte Carlo algorithm, we need to *interact* with the environment. This means that we start an episode by using `env.reset` and send the environment actions via `env.step` to observe the reward and next observation (state)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "ef5fc8c121d400f2192646c9201e7769",
     "grade": false,
     "grade_id": "cell-85356add2643980e",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# So let's have a look at what we can do in general with an environment...\n",
    "import gym\n",
    "?gym.Env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "0433c9161cddc2d9171c8e87b1e9b444",
     "grade": false,
     "grade_id": "cell-251b7b17c5d08a24",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# We can also look at the documentation/implementation of a method\n",
    "?env.step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "42aceb4ea182a1aacfb40c5f201d4b01",
     "grade": false,
     "grade_id": "cell-6decb2ab83c5bcec",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "??BlackjackEnv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8fa896a9ea64567134c861511cfa6011",
     "grade": false,
     "grade_id": "cell-ae161126d3cb1b7b",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "A very simple policy for Blackjack is to *stick* if we have 20 or 21 points and *hit* otherwise. We want to know how good this policy is. This policy is *deterministic* and therefore a function that maps an observation to a single action. Technically, we can implement this as a dictionary or as a function, where we use the latter. To get started, let's implement this simple policy for BlackJack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "0a51bbe0d24d711482d0d0770e955a16",
     "grade": false,
     "grade_id": "cell-9fdcb503df9cdb08",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def simple_policy(observation):\n",
    "    \"\"\"\n",
    "    A policy that sticks if the player score is > 20 and hits otherwise.\n",
    "    \"\"\"\n",
    "    \n",
    "    if observation[0] < 20:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "bf04382f012b102f2c5c360bb8da3241",
     "grade": true,
     "grade_id": "cell-99f02e2d9b338a5b",
     "locked": true,
     "points": 1,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13, 2, False)\n",
      "((17, 2, False), 0, False, {})\n"
     ]
    }
   ],
   "source": [
    "s = env.reset()\n",
    "print(s)\n",
    "a = simple_policy(s)\n",
    "print(env.step(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "b5e33f6499dd4040071dc62a3e8fccb2",
     "grade": false,
     "grade_id": "cell-0184f4c719afb98c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Now implement either the MC prediction algorithm (either first visit or every visit). Hint: you can use `for i in tqdm(range(num_episodes))` to show a progress bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "a90a42914d0f7ade801bee9a8bd04e19",
     "grade": true,
     "grade_id": "cell-b822e9d13cf1f65e",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def mc_prediction(policy, env, num_episodes, discount_factor=1.0):\n",
    "    \"\"\"\n",
    "    Monte Carlo prediction algorithm. Calculates the value function\n",
    "    for a given policy using sampling.\n",
    "    \n",
    "    Args:\n",
    "        policy: A function that maps an observation to action probabilities.\n",
    "        env: OpenAI gym environment.\n",
    "        num_episodes: Number of episodes to sample.\n",
    "        discount_factor: Gamma discount factor.\n",
    "    \n",
    "    Returns:\n",
    "        A dictionary that maps from state -> value.\n",
    "        The state is a tuple and the value is a float.\n",
    "    \"\"\"\n",
    "\n",
    "    # Keeps track of sum and count of returns for each state\n",
    "    # to calculate an average. We could use an array to save all\n",
    "    # returns (like in the book) but that's memory inefficient.\n",
    "    returns_sum = defaultdict(float)\n",
    "    returns_count = defaultdict(float)\n",
    "    \n",
    "    # The final value function\n",
    "    V = defaultdict(float)\n",
    "    \n",
    "    \n",
    "    \n",
    "    for i in range(num_episodes):\n",
    "        states_visited = []\n",
    "        \n",
    "#         reseting the environment\n",
    "        s = env.reset()\n",
    "        \n",
    "        states_visited.append((s, 0))\n",
    "        \n",
    "        done = False\n",
    "        \n",
    "        while not done:\n",
    "#         decide an action\n",
    "            a = simple_policy(s)\n",
    "#         apply an action\n",
    "            s , r, done, x = env.step(a)\n",
    "#         save state and reward\n",
    "            states_visited.append((s, r))\n",
    "    \n",
    "#         record states and rewards in reverse time being affected by the discount factor\n",
    "        G = 0\n",
    "#       (for Exploring Starts implementation)\n",
    "        states_evaluated = []\n",
    "        for i in reversed(range(len(states_visited))):\n",
    "            state = states_visited[i][0]\n",
    "            r = states_visited[i][1]\n",
    "#             (for Exploring Starts implementation)\n",
    "            if state in states_evaluated:\n",
    "                continue\n",
    "            states_evaluated.append(state)\n",
    "            G = discount_factor * G + r\n",
    "            returns_sum[state]   += G\n",
    "            returns_count[state] += 1\n",
    "            \n",
    "\n",
    "    for state in returns_sum:\n",
    "        V[state] = returns_sum[state] / float(returns_count[state])\n",
    "    \n",
    "    return V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'float'>, {(20, 10, False): 0.48214285714285715, (25, 4, False): -1.0, (19, 4, False): -0.45454545454545453, (17, 4, False): -0.8, (19, 4, True): -1.0, (20, 4, False): 0.375, (21, 10, True): 1.0, (20, 3, False): 0.75, (16, 3, False): -0.42857142857142855, (14, 3, False): -0.9166666666666666, (12, 3, False): -0.46153846153846156, (28, 1, False): -1.0, (18, 1, False): -0.8, (25, 2, False): -1.0, (15, 2, False): -0.7, (18, 2, True): -0.5, (13, 2, True): 0.0, (26, 2, False): -1.0, (18, 2, False): -0.75, (13, 2, False): -0.7, (26, 10, False): -1.0, (19, 10, False): -0.7457627118644068, (22, 1, False): -1.0, (12, 1, False): -1.0, (14, 4, False): -0.3333333333333333, (17, 2, False): -0.5, (12, 2, False): -0.25, (26, 1, False): -1.0, (16, 1, False): -1.0, (29, 10, False): -1.0, (16, 10, False): -0.5384615384615384, (20, 8, False): 0.9090909090909091, (22, 5, False): -1.0, (12, 5, False): -0.6, (20, 6, False): 0.5714285714285714, (22, 8, False): -1.0, (12, 8, False): -0.8461538461538461, (22, 10, False): -1.0, (12, 10, False): -0.7111111111111111, (18, 10, True): -0.8333333333333334, (20, 2, False): 0.7647058823529411, (26, 4, False): -1.0, (16, 4, False): -1.0, (16, 4, True): -0.75, (17, 8, False): -0.7142857142857143, (16, 10, True): 0.0, (22, 7, False): -1.0, (19, 7, False): -0.6363636363636364, (19, 7, True): 0.0, (27, 5, False): -1.0, (19, 5, False): -1.0, (18, 5, False): -0.3333333333333333, (14, 5, False): -0.7777777777777778, (21, 9, True): 0.8, (15, 8, False): -0.8, (21, 10, False): 0.8421052631578947, (13, 10, False): -0.5740740740740741, (14, 7, False): -0.8181818181818182, (13, 7, False): -0.2727272727272727, (22, 6, False): -1.0, (12, 6, False): -0.5384615384615384, (24, 9, False): -1.0, (16, 9, False): -0.7333333333333333, (27, 2, False): -1.0, (17, 2, True): -1.0, (28, 4, False): -1.0, (24, 3, False): -1.0, (23, 8, False): -1.0, (13, 8, False): -1.0, (18, 10, False): -0.7857142857142857, (23, 2, False): -1.0, (15, 2, True): -1.0, (18, 4, False): -0.9090909090909091, (15, 4, False): -0.46153846153846156, (14, 10, False): -0.7391304347826086, (25, 1, False): -1.0, (23, 9, False): -1.0, (14, 9, False): -1.0, (12, 9, False): -0.7272727272727273, (27, 9, False): -1.0, (17, 9, False): -0.8, (22, 9, False): -1.0, (25, 10, False): -1.0, (22, 2, False): -1.0, (16, 2, False): -1.0, (14, 2, False): -1.0, (15, 10, False): -0.5833333333333334, (28, 2, False): -1.0, (12, 9, True): -1.0, (15, 1, False): -1.0, (14, 1, False): -0.5, (19, 10, True): -0.6666666666666666, (17, 10, False): -0.625, (25, 6, False): -1.0, (19, 6, False): -1.0, (15, 3, False): -0.5, (21, 4, False): 0.8181818181818182, (29, 5, False): -1.0, (16, 5, False): -0.75, (12, 7, False): -0.3333333333333333, (19, 2, True): -0.25, (19, 2, False): -0.45454545454545453, (19, 9, False): -0.5, (12, 4, False): -0.1, (23, 10, False): -1.0, (23, 3, False): -1.0, (27, 10, False): -1.0, (24, 10, False): -1.0, (20, 9, False): 0.7647058823529411, (29, 9, False): -1.0, (16, 8, False): -0.8571428571428571, (23, 7, False): -1.0, (17, 1, False): -0.8571428571428571, (27, 3, False): -1.0, (18, 3, False): -1.0, (19, 8, False): -0.7894736842105263, (20, 5, True): 1.0, (15, 6, False): -0.3333333333333333, (22, 3, False): -1.0, (19, 3, False): -0.6363636363636364, (19, 3, True): -1.0, (13, 3, True): -0.3333333333333333, (21, 2, True): 1.0, (23, 6, False): -1.0, (13, 6, False): -0.5, (25, 7, False): -1.0, (15, 7, False): -0.6666666666666666, (17, 6, False): -0.7142857142857143, (20, 9, True): 0.5, (20, 7, False): 0.7272727272727273, (16, 7, False): -0.45454545454545453, (28, 5, False): -1.0, (17, 10, True): -0.7142857142857143, (20, 5, False): 1.0, (13, 10, True): -0.25, (21, 9, False): 0.8, (28, 10, False): -1.0, (24, 6, False): -1.0, (16, 6, False): -0.7777777777777778, (24, 8, False): -1.0, (14, 8, False): -0.6363636363636364, (13, 1, False): -0.5555555555555556, (22, 4, False): -1.0, (12, 4, True): 0.0, (25, 8, False): -1.0, (24, 7, False): -1.0, (21, 7, True): 1.0, (26, 6, False): -1.0, (18, 6, False): -1.0, (26, 3, False): -1.0, (21, 5, True): 1.0, (13, 9, False): -0.8181818181818182, (20, 1, False): 0.375, (21, 2, False): 0.7142857142857143, (15, 10, True): 0.0, (27, 8, False): -1.0, (13, 3, False): -0.6666666666666666, (18, 8, True): -1.0, (23, 1, False): -1.0, (26, 7, False): -1.0, (21, 1, True): 0.5, (26, 8, False): -1.0, (28, 9, False): -1.0, (18, 9, False): -0.8333333333333334, (21, 4, True): 0.6666666666666666, (17, 4, True): 1.0, (27, 7, False): -1.0, (17, 7, False): -1.0, (21, 7, False): 1.0, (20, 7, True): 1.0, (18, 7, True): -0.2, (14, 10, True): -0.6666666666666666, (12, 7, True): 1.0, (21, 6, False): 1.0, (13, 5, False): -1.0, (19, 1, False): -0.8888888888888888, (20, 2, True): 1.0, (24, 5, False): -1.0, (25, 3, False): -1.0, (15, 3, True): -0.6666666666666666, (26, 9, False): -1.0, (28, 8, False): -1.0, (26, 5, False): -1.0, (16, 8, True): -1.0, (15, 8, True): -1.0, (19, 1, True): -1.0, (21, 1, False): 0.7142857142857143, (18, 4, True): 0.0, (29, 8, False): -1.0, (21, 3, False): 0.7142857142857143, (21, 8, True): 1.0, (20, 4, True): 1.0, (15, 9, False): -0.6666666666666666, (27, 4, False): -1.0, (13, 4, False): -1.0, (16, 9, True): -1.0, (28, 6, False): -1.0, (20, 6, True): 0.5, (17, 8, True): -1.0, (21, 3, True): 0.8, (16, 3, True): -0.25, (20, 3, True): 1.0, (14, 3, True): 0.0, (14, 7, True): 0.0, (29, 1, False): -1.0, (29, 6, False): -1.0, (25, 5, False): -1.0, (15, 5, False): -1.0, (19, 5, True): -1.0, (13, 5, True): -1.0, (18, 8, False): -1.0, (18, 3, True): -1.0, (23, 4, False): -1.0, (17, 3, False): -0.5555555555555556, (18, 7, False): -0.5, (14, 6, False): -0.45454545454545453, (20, 8, True): 1.0, (13, 8, True): 1.0, (21, 8, False): 1.0, (18, 1, True): 0.0, (16, 6, True): 0.0, (25, 9, False): -1.0, (21, 6, True): 1.0, (17, 3, True): -1.0, (29, 7, False): -1.0, (28, 3, False): -1.0, (20, 1, True): 0.0, (17, 5, False): -1.0, (23, 5, False): -1.0, (28, 7, False): -1.0, (14, 4, True): 1.0, (15, 7, True): -1.0, (20, 10, True): 0.0, (17, 6, True): -1.0, (14, 9, True): -1.0, (14, 2, True): -1.0, (21, 5, False): 1.0, (27, 1, False): -1.0, (24, 1, False): -1.0, (12, 10, True): 0.3333333333333333, (27, 6, False): -1.0, (14, 8, True): 0.0, (24, 2, False): -1.0, (13, 1, True): -1.0, (13, 9, True): -1.0, (29, 2, False): -1.0, (24, 4, False): -1.0, (18, 9, True): -1.0, (19, 9, True): -1.0, (13, 7, True): -1.0, (29, 3, False): -1.0})\n"
     ]
    }
   ],
   "source": [
    "V = mc_prediction(simple_policy, env, num_episodes=1000)\n",
    "print(V)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "65fb9d8060a4c843e72474169396eb3e",
     "grade": false,
     "grade_id": "cell-9d32f907f180c088",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Now make *4 plots* like Figure 5.1 in the book. You can either make 3D plots or heatmaps. Make sure that your results look similar to the results in the book. Give your plots appropriate titles, axis labels, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8a4eee824f088a6c13ee8c9296af4561",
     "grade": false,
     "grade_id": "cell-cbaf4d6a0e4c00fa",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.6 s, sys: 0 ns, total: 19.6 s\n",
      "Wall time: 19.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Let's run your code one time\n",
    "V_10k = mc_prediction(simple_policy, env, num_episodes=10000)\n",
    "V_500k = mc_prediction(simple_policy, env, num_episodes=500000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "84102d69cdad07cf0100d846346b65e6",
     "grade": true,
     "grade_id": "cell-ba046443478aa517",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Reward')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the axis\n",
    "x = np.linspace(1, 10, num=10)\n",
    "y = np.linspace(12, 21, num=10)\n",
    "\n",
    "rewards_usable_10k = np.zeros([10, 10])\n",
    "rewards_unusable_10k = np.zeros([10, 10])\n",
    "\n",
    "rewards_usable_500k = np.zeros([10, 10])\n",
    "rewards_unusable_500k = np.zeros([10, 10])\n",
    "\n",
    "for s in V_10k:\n",
    "    player, dealer , usable = s\n",
    "    if player > 21:\n",
    "        continue\n",
    "    if usable:\n",
    "        rewards_usable_10k[player - 12][dealer-1] = V_10k[s]\n",
    "    else:\n",
    "        rewards_unusable_10k[player - 12][dealer-1] = V_10k[s]\n",
    "        \n",
    "for s in V_500k:\n",
    "    \n",
    "    player, dealer , usable = s\n",
    "    if player > 21:\n",
    "        continue\n",
    "    if usable:\n",
    "        rewards_usable_500k[player - 12][dealer-1] = V_500k[s]\n",
    "    else:\n",
    "        rewards_unusable_500k[player - 12][dealer-1] = V_500k[s]\n",
    "        \n",
    "            \n",
    "\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.contour3D(x, y, rewards_usable_500k, 50, cmap='binary')\n",
    "ax.set_title('Usable 500k')\n",
    "ax.set_xlabel('Dealer showing')\n",
    "ax.set_ylabel('Player sum')\n",
    "ax.set_zlabel('Reward')\n",
    "\n",
    "fig = plt.figure(2)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.contour3D(x, y, rewards_unusable_500k, 50, cmap='binary')\n",
    "ax.set_title('No usable 500k')\n",
    "ax.set_xlabel('Dealer showing')\n",
    "ax.set_ylabel('Player sum')\n",
    "ax.set_zlabel('Reward')\n",
    "\n",
    "fig = plt.figure(3)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.contour3D(x, y, rewards_usable_10k, 50, cmap='binary')\n",
    "ax.set_title('Usable 10k')\n",
    "ax.set_xlabel('Dealer showing')\n",
    "ax.set_ylabel('Player sum')\n",
    "ax.set_zlabel('Reward')\n",
    "\n",
    "fig = plt.figure(4)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.contour3D(x, y, rewards_unusable_10k, 50, cmap='binary')\n",
    "ax.set_title('No usable 10k')\n",
    "ax.set_xlabel('Dealer showing')\n",
    "ax.set_ylabel('Player sum')\n",
    "ax.set_zlabel('Reward')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "a65fdcbe1c3c2a4f23b847a8dae03570",
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     "grade_id": "cell-a5cc039e3d648855",
     "locked": true,
     "schema_version": 1,
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   },
   "source": [
    "## 5. Monte Carlo control with $\\epsilon$-greedy policy (5 points)\n",
    "Now we have a method to evaluate state-values given a policy. Take a moment to think whether we can use the value function to find a better policy? Assuming we do not know the dynamics of the environment, why is this not possible?\n",
    "\n",
    "We want a policy that selects _actions_ with maximum value, e.g. is _greedy_ with respect to the _action-value_ (or Q-value) function $Q(s,a)$. We need to keep exploring, so with probability $\\epsilon$ we will take a random action. First, lets implement a function `make_epsilon_greedy_policy` that takes the Q-value function and returns an $\\epsilon$-greedy policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "ecd6cfa62fdf8463d6a97a865de84140",
     "grade": true,
     "grade_id": "cell-78eff3f4ca0f0e09",
     "locked": false,
     "points": 1,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "def make_epsilon_greedy_policy(Q, epsilon, nA):\n",
    "    \"\"\"\n",
    "    Creates an epsilon-greedy policy based on a given Q-function and epsilon.\n",
    "    \n",
    "    Args:\n",
    "        Q: A dictionary that maps from state -> action-values.\n",
    "            Each value is a numpy array of length nA (see below)\n",
    "        epsilon: The probability to select a random action . float between 0 and 1.\n",
    "        nA: Number of actions in the environment.\n",
    "    \n",
    "    Returns:\n",
    "        A function that takes the observation as an argument and returns\n",
    "        the probabilities for each action in the form of a numpy array of length nA.\n",
    "    \n",
    "    \"\"\"\n",
    "    def policy_fn(observation):\n",
    "        \n",
    "        action_values = Q[observation]\n",
    "        \n",
    "        best_action = np.argmax(action_values)\n",
    "        \n",
    "        action_values = (epsilon / float(nA)) * np.ones(nA) \n",
    "        action_values[best_action] += 1 - epsilon\n",
    "        \n",
    "        return action_values\n",
    "    \n",
    "    return policy_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "12a7f4ab3c649a1d768ce80d4573cf28",
     "grade": true,
     "grade_id": "cell-2fc0baa87f31ab98",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def mc_control_epsilon_greedy(env, num_episodes, discount_factor=1.0, epsilon=0.1):\n",
    "    \"\"\"\n",
    "    Monte Carlo Control using Epsilon-Greedy policies.\n",
    "    Finds an optimal epsilon-greedy policy.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI gym environment.\n",
    "        num_episodes: Number of episodes to sample.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        epsilon: Chance the sample a random action. Float betwen 0 and 1.\n",
    "    \n",
    "    Returns:\n",
    "        A tuple (Q, policy).\n",
    "        Q is a dictionary mapping state -> action values.\n",
    "        policy is a function that takes an observation as an argument and returns\n",
    "        action probabilities\n",
    "    \"\"\"\n",
    "    \n",
    "    # Again, keep track of counts for efficiency\n",
    "    # returns_sum, returns_count and Q are \n",
    "    # nested dictionaries that map state -> (action -> action-value).\n",
    "    # We could also use tuples (s, a) as keys in a 1d dictionary, but this\n",
    "    # way Q is in the format that works with make_epsilon_greedy_policy\n",
    "    \n",
    "    returns_sum = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    returns_count = defaultdict(lambda: np.zeros(env.action_space.n, dtype=int))\n",
    "    \n",
    "    # The final action-value function.\n",
    "    Q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    \n",
    "    # The policy we're following\n",
    "    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n",
    "    \n",
    "    for i in range(num_episodes):\n",
    "        states_visited = []\n",
    "        \n",
    "#         reseting the environment\n",
    "        s = env.reset()\n",
    "        \n",
    "        done = False\n",
    "        \n",
    "        while not done:\n",
    "#         get the policy\n",
    "            p = policy(s)\n",
    "#         we sample an action from the policy \n",
    "            action = p[0] < np.random.uniform()\n",
    "#         apply an action\n",
    "            new_s , r, done, x = env.step(action)\n",
    "#         save state and reward\n",
    "            states_visited.append((s, int(action), r))\n",
    "#         update state\n",
    "            s = new_s\n",
    "    \n",
    "#         record states and rewards in reverse time being affected by the discount factor\n",
    "        G = 0\n",
    "#       (for Exploring Starts implementation)\n",
    "        states_evaluated = []\n",
    "        for i in reversed(range(len(states_visited))):\n",
    "#             print(states_visited[i])\n",
    "            s, a, r = states_visited[i]\n",
    "            G = discount_factor * G + r\n",
    "#             (for Exploring Starts implementation)\n",
    "            if s in states_evaluated:\n",
    "                continue\n",
    "            states_evaluated.append(s)\n",
    "            returns_sum[s][a]   += G\n",
    "            returns_count[s][a]  += 1\n",
    "#         update Q values\n",
    "            Q[s][a] = returns_sum[s][a] / float(returns_count[s][a])\n",
    "            \n",
    "    return Q, policy\n",
    "\n",
    "# Test it quickly\n",
    "Q, policy = mc_control_epsilon_greedy(env, num_episodes=10, epsilon=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "9ea50e12589ce430405fa2e92ee0c108",
     "grade": false,
     "grade_id": "cell-e6170d8979ca2a9c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29.6 s, sys: 603 ms, total: 30.2 s\n",
      "Wall time: 29.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Q, policy = mc_control_epsilon_greedy(env, num_episodes=500000, epsilon=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "94d3f0aecbe1b5ebfed94e7b1379f617",
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     "grade_id": "cell-449e36eb98369942",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "How can you obtain the (V-)value function from the Q-value function? Plot the (V-)value function that is the result of 500K iterations. Additionally, visualize the greedy policy similar to Figure 5.2 in the book. Use a white square for hitting, black for sticking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "ef932ff88d976ffefc0338822d7d2af9",
     "grade": true,
     "grade_id": "cell-7d797248a3b132f5",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No usable 500k π*, (White = Hit)')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ipo5kHf+lk1kiDqxZIg6sWSIOrFkiDqxZIg6sWSIOrFkiDqxZIg6sWSIOrFkiDqxZIg6sWSIOrFkiDqxZIg6sWSIOrFki6/UhbF14HnhifTciqbFpYtl2BplqhVz1ZqoVYK+RrjCqgY2I7fuxXUnzR/qf+4OSqVbIVW+mWqGqd6TreEpslogDa5bIhhLYWYMuYAQy1Qq56s1UK3RR76h+aqKZrZ8NZYQ12yg4sGaJpA2spF0l/UTSYkkPSjpp0DW1I2lTSfdJum7QtbQjaTtJV5cLdy+WdMigaxqOpFPK82CRpCslvWHQNQ1pcVH0N0q6UdKS8n1CJ9tKG1iqq+adGhF7AwcDn5C0z4BrauckYPGgi+jQRcCPIuJtwAGM4bolTQJOBKZGxH7ApsD7B1vVWmaz7kXRTwduiog9gZvK/bbSBjYino6Ie8vt1VRPqEmDrao1SbsA7wEuGXQt7UjaBjgMuBQgIl6OiBcGW1Vb44AtJI0DtgSWD7ie32l2UXTgWODycvty4LhOtpU2sHWSJgMHAncPtpJhfRk4DXh90IV0YA/gOeC/yhT+EklbDbqoViLiKeB8YCnwNPBiRNww2Kra2jEinoZq8AF26GSl9IGVNB64Bjg5IlYNup5mJE0HVkTEgkHX0qFxwJ8AX42IA4Ff0+GUbRDK+d+xwO7AzsBWkv5+sFX1R+rAStqMKqxzImLuoOsZxjuBYyQ9DlwFHCnpisGWNKxlwLKIGJqxXE0V4LHq3cBjEfFcRLwCzAXeMeCa2nlW0k4A5fuKTlZKG1hV/7JzKbA4Ii4cdD3DiYjPRMQuETGZ6sWQmyNizI4AEfEM8KSkof8mmQY8NMCS2lkKHCxpy/K8mMYYfpGs+B4wo9yeAVzbyUqj/e91vfRO4IPAA5IWlrYzIuKHA6xpQ/IpYI6kzYFHgY8MuJ6WIuJuSVcD91K9e3AfY+jPFMtF0Y8AJkpaBpwFnAt8q1wgfSnwvo625T9NNMsj7ZTYbGPkwJol4sCaJeLAmiXiwJol4sCaJeLAmiXy/5u6FNyCAx0ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rewards_usable = np.zeros([10, 10])\n",
    "rewards_unusable = np.zeros([10, 10])\n",
    "\n",
    "policy_prev_usable = np.zeros([22, 11])\n",
    "policy_prev_unusable = np.zeros([22, 11])\n",
    "\n",
    "for s in Q:\n",
    "    for a in range(env.action_space.n):\n",
    "        \n",
    "        player, dealer , usable = s\n",
    "        if player > 21:\n",
    "            continue\n",
    "            \n",
    "        policy_response =  policy(s)\n",
    "#             setting the state value equal to the weighted average of the action rewards\n",
    "        value = np.average( Q[s], weights = policy_response)\n",
    "        \n",
    "        if usable:\n",
    "            rewards_usable[player - 12][dealer-1] = value\n",
    "#             print(police_response)\n",
    "            policy_prev_usable[player][dealer] = int(policy_response[0] < policy_response[1])\n",
    "            \n",
    "        else:\n",
    "#             print('NOt usable :', police_response)\n",
    "            rewards_unusable[player - 12][dealer-1] = value\n",
    "            policy_prev_unusable[player][dealer] = int(policy_response[0] < policy_response[1])\n",
    "                   \n",
    "\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set the axis\n",
    "x = np.linspace(1, 10, num=10)\n",
    "y = np.linspace(12, 21, num=10)\n",
    "\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.contour3D(x, y, rewards_usable, 50, cmap='binary')\n",
    "ax.set_title('Usable 500k V*')\n",
    "ax.set_xlabel('Dealer showing')\n",
    "ax.set_ylabel('Player sum')\n",
    "ax.set_zlabel('Reward')\n",
    "\n",
    "fig = plt.figure(2)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.contour3D(x, y, rewards_unusable, 50, cmap='binary')\n",
    "ax.set_title('No usable 500k V*')\n",
    "ax.set_xlabel('Dealer showing')\n",
    "ax.set_ylabel('Player sum')\n",
    "ax.set_zlabel('Reward')\n",
    "\n",
    "\n",
    "plt.figure(3)\n",
    "\n",
    "policy_usable_plot = policy_prev_usable[1:][11:]\n",
    "policy_usable_plot =  np.flip(policy_usable_plot, axis=0)\n",
    "\n",
    "plt.imshow(policy_usable_plot, cmap=plt.cm.gray, extent=[1,10,11,21])\n",
    "plt.title('Usable 500k π*, (White = Hit)')\n",
    "\n",
    "\n",
    "plt.figure(4)\n",
    "\n",
    "policy_unusable_plot = policy_prev_unusable[1:][11:]\n",
    "policy_unusable_plot =  np.flip(policy_unusable_plot, axis=0)\n",
    "\n",
    "plt.imshow(policy_unusable_plot, cmap=plt.cm.gray, extent=[1,10,11,21])\n",
    "plt.title('No usable 500k π*, (White = Hit)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "1fd18efb629e87209d1db4236553de73",
     "grade": false,
     "grade_id": "cell-eecfd6fb626abfae",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## 6. Temporal Difference (TD) learning (8 points)\n",
    "Mention one advantage and one disadvantage of Monte Carlo methods. Mention an example where you would prefer to use TD learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "4b81bcd51404511164971c110ffa838f",
     "grade": true,
     "grade_id": "cell-cac4639044ba9074",
     "locked": false,
     "points": 1,
     "schema_version": 1,
     "solution": true
    }
   },
   "source": [
    "MC is more short-shighted compared to TD learning. MC evaluates a state only by averaging rewards of times when that state was visited (also affected by the future rewards, but with the discount factor applied). Advantage of MC is the fact that it does not need any knowledge of the environments dynamics. Dissadvantage of MC is that it has no opinion about a state if it has never visited (even though the agent could have visited all neighbouring states). MC method, needs the final reward in order to update values. Taking into account the discount factor, this could take considerably long to converge for cases with long episodes. For this reason, if a model had to be trained on playing some arcade game, where every step of the episode is one frame, then it would be prefered to use TD learning due to the huge length of the episodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "e61bd7837d3b364741b4c3aa43597a10",
     "grade": false,
     "grade_id": "cell-21ca38ffcbe1c3ca",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "For the TD algorithms, we will skip the prediction algorithm and go straight for the control setting where we optimize the policy that we are using. In other words: implement SARSA. To keep it dynamic, we will use the windy gridworld environment (Example 6.5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "609d0f1e1ef6ad89c8dcd96dd43aa798",
     "grade": false,
     "grade_id": "cell-c046fd0377cee46d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "from windy_gridworld import WindyGridworldEnv\n",
    "env = WindyGridworldEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "42b89f13768d1cd3b41fb52cddef0d97",
     "grade": true,
     "grade_id": "cell-6b662771f3762bb1",
     "locked": false,
     "points": 2,
     "schema_version": 1,
     "solution": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:00<00:00, 1384.80it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1, Q=None):\n",
    "    \"\"\"\n",
    "    SARSA algorithm: On-policy TD control. Finds the optimal epsilon-greedy policy.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI environment.\n",
    "        num_episodes: Number of episodes to run for.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        alpha: TD learning rate.\n",
    "        epsilon: Probability to sample a random action. Float between 0 and 1.\n",
    "        Q: hot-start the algorithm with a Q value function (optional)\n",
    "    \n",
    "    Returns:\n",
    "        A tuple (Q, stats).\n",
    "        Q is the optimal action-value function, a dictionary mapping state -> action values.\n",
    "        stats is a list of tuples giving the episode lengths and rewards.\n",
    "    \"\"\"\n",
    "    \n",
    "    # The final action-value function.\n",
    "    # A nested dictionary that maps state -> (action -> action-value).\n",
    "    if Q is None:\n",
    "        Q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    \n",
    "    # Keeps track of useful statistics\n",
    "    stats = []\n",
    "    \n",
    "    # The policy we're following\n",
    "    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n",
    "    \n",
    "\n",
    "    for i_episode in tqdm(range(num_episodes)):\n",
    "        i = 0\n",
    "        R = 0\n",
    "        \n",
    "#         retrieving initial state\n",
    "        initial_state = np.argmax(env.isd)\n",
    "#         retrieve action probabilities\n",
    "        action_probs = policy(initial_state)\n",
    "#         sample an action\n",
    "        action = np.random.choice([0,1,2,3], p=action_probs)\n",
    "    \n",
    "        trans_prob, new_state, r, done = env.P[initial_state][action][0]\n",
    "        \n",
    "#         for every step of the episode\n",
    "        while not done:\n",
    "            i += 1\n",
    "            R += r\n",
    "        \n",
    "    #         retrieve action probabilities\n",
    "            action_probs = policy(new_state)\n",
    "    #         sample an action\n",
    "            next_action = np.random.choice([0,1,2,3], p=action_probs)\n",
    "#             update Q value\n",
    "            Q[initial_state][action] += alpha* ( r + discount_factor* Q[new_state][next_action] - Q[initial_state][action])\n",
    "#             update state and action according to the step\n",
    "            initial_state = new_state\n",
    "            action = next_action\n",
    "            \n",
    "            trans_prob, new_state, r, done = env.P[initial_state][action][0]\n",
    "            \n",
    "        stats.append((i, R))\n",
    "    episode_lengths, episode_returns = zip(*stats)\n",
    "    return Q, (episode_lengths, episode_returns)\n",
    "\n",
    "Q_sarsa, (episode_lengths_sarsa, episode_returns_sarsa) = sarsa(env, 1000)\n",
    "\n",
    "# We will help you with plotting this time\n",
    "plt.plot(episode_lengths_sarsa)\n",
    "plt.title('Episode lengths SARSA')\n",
    "plt.show()\n",
    "plt.plot(episode_returns_sarsa)\n",
    "plt.title('Episode returns SARSA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "6d2e2b63d4428913ca84b62fb4384ef9",
     "grade": false,
     "grade_id": "cell-0eaf4b925ab3ea34",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Since we might not be interested in falling off the cliff all the time, we can find another person to do this 'exploration' for us (in the name of science). Still, we would like to learn ourselfs from this persons policy, which is where we arrive at _off-policy_ learning. In the simplest variant, we learn our own value by bootstrapping based on the action value corresponding to the best action we could take, while the exploration policy actual follows the $\\epsilon$-greedy strategy. This is known as Q-learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "954556134388a34f8d4b9a07834180c5",
     "grade": true,
     "grade_id": "cell-a87637d2e582fec0",
     "locked": false,
     "points": 1,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:00<00:00, 1077.28it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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JqtsB4O6bgPVmNiUUzQSWAg8Ds0LZLGBuePwwcGW4GmgGUBfvKupt6gASEUktq4fL/1/g92aWA6wGriIWKg+Y2WxgHXBZqPs4cAFQAewOdfuUBoFFRJLrUQC4+1tAWQezZnZQ14Ev9eT5ukpjwCIiqUXyTuA4nQCIiCQXyQDQ9wGIiKQWyQCI0xiAiEhykQwAjQGIiKQWyQAQEZHUIh0AuhFMRCS5SAeAiIgkF+kA0CCwiEhykQwADQKLiKQWyQAQEZHUIhkAuhFMRCS1SAaAiIikFukAcI0Ci4gkFckA0CCwiEhqkQyAOJ0AiIgkF8kA0AmAiEhqkQyAOJ0AiIgkF8kAMA0CiIikFMkAiNMYgIhIcpEMAB3/i4ikFskAEBGR1CIdAPo+ABGR5CIZABoDFhFJLZIBEKdBYBGR5CIZALoMVEQktUgGQJxOAEREkot0AIiISHIKABGRNNXjADCzTDN708weDdNHmNkCM1tpZn80s5xQPiBMV4T5pT197pQ0CiwiklRvnAF8BViWMP0T4GZ3nwzUArND+Wyg1t0nATeHen1G48AiIp3rUQCY2VjgQuDOMG3AR4EHQ5V7gEvC44vDNGH+TOvjy3V0/C8iklxPzwBuAb4JtIXp4cB2d28J05VASXhcAqwHCPPrQv19mNkcMys3s/Lq6upuN0wnACIinet2AJjZRcAWd1+YWNxBVe/CvPcL3O9w9zJ3LysqKupu88K6erS4iEikZfVg2Q8DHzezC4BcIJ/YGcEwM8sKR/ljgY2hfiUwDqg0syxgKFDTg+fvlG4GExHpXLfPANz9Oncf6+6lwOXAs+7+GeA54NJQbRYwNzx+OEwT5j/r3rfH6PowOBGR5PriPoBvAV8zswpiffx3hfK7gOGh/GvAtX3w3Hvp+F9EpHM96QLay92fB54Pj1cDJ3VQpxG4rDeeT0REei7SdwJrEFhEJLnIBoDGgEVEOhfZAADdCCYi0pnIBoBpGFhEpFORDQDQGICISGeiGwA6ARAR6VR0AwDdCCYi0pnIBoBOAEREOhfZABARkc5FOwDUAyQiklRkA0A3gomIdC6yAQA6ARAR6UxkA0A3gomIdC6yAQDQx183ICJyWItsAGgMQESkc5ENABER6VykA0A9QCIiyUU2ANQDJCLSucgGQH1TK3e+tKa/myEicsiKbACIiEjnFAAiImlKASAikqYUACIiaUoBICKSpiIfAPo4CBGRjkU+AFrbFAAiIh2JfAC0KABERDrU7QAws3Fm9pyZLTOzJWb2lVBeaGZPm9nK8LsglJuZ3WZmFWb2tpmd2Fsb0RmdAYiIdKwnZwAtwNfdfRowA/iSmU0HrgXmuftkYF6YBjgfmBx+5gC39+C5u97IVgWAiEhHuh0A7l7l7m+ExzuBZUAJcDFwT6h2D3BJeHwxcK/HzAeGmVlxt1veRS1tbX39FCIih6VeGQMws1LgBGABMMrdqyAWEsDIUK0EWJ+wWGUoa7+uOWZWbmbl1dXVPW6buoBERDrW4wAws8HAn4GvuvuOzqp2ULbfu7O73+HuZe5eVlRU1NPmaRBYRCSJHgWAmWUTe/P/vbs/FIo3x7t2wu8tobwSGJew+FhgY0+evys0BiAi0rGeXAVkwF3AMne/KWHWw8Cs8HgWMDeh/MpwNdAMoC7eVdSXNAYgItKxrB4s+2Hgc8A7ZvZWKPsP4MfAA2Y2G1gHXBbmPQ5cAFQAu4GrevDcXaYxABGRjnU7ANz9JZJ/8dbMDuo78KXuPl93aQxARKRj0b8TWGMAIiIdin4AhDGAtyu3c/yNT1FT39TPLRIROTREPgDiYwC/fG4V23c3M3/1tn5ukYjIoSHyARAfA2gLHwudkWzUQkQkzUQ/AFrjARCbjl29GrO+ZjctrbpMVETSU/QDoK2Na//8Ns8s2wxARgiAuoZmTv/pc3x37pL+bJ6ISL+JfAC0tjl/eP39jyDKDFu8p6UVgMff6fN70UREDkmRD4D29wFYuHUhPjhc19B80NskInIoiGwA/O8XTwb2vw8gPgSg+wNEJN1FNgBGDskF9v8soPgYQLMGf0UkzUU2ALIz9+3qiYtPJXYNzX1rw8FqlojIISOyAZAZLvj/2gOL9imfv3obSzbW7XMG8JU/vMW7m3dy7A1PcuntrxzUdoqI9JfIBkBWRsebdvvzq7jwtpf2GwM45+YX2NHYQvl7tQejeSIi/S6yAZCZ4pZffUqoiKS7yAZAfAwgGd0BLCLpLrIBoDMAEZHORTYAko0BxOkyUBFJd5ENgFRnAIvW1yWd566zAxGJvsgGQFaKALj5mXeTzrvnlbV7H9/01ApeXFndW80SETlk9ORL4Q9pGT344P9lVTv3Pr7t2QoA1v74wh63SUTkUBLZM4CeiA8ftLUbKN5U17jP9OINdVTv3HOwmiUi0qsiHQDjC/O6uWTs7KExfGQ0wIpNO5nxo3n8/NmVzH1rA+7ORT97ifNvfbEXWioicvBFtgsI4I//PINP/vIVqtoduady/2vrGF+Yxz8cNWJv2bm3vADAfz8VGzsYkht76bbu2sOLK6tpaGrlnKNH09rm1NQ3UTRkQC9thYhI34j0GUDx0IF887wpndY5ffKIDst/8sRy5ty7MOlyb7y3fe/jz931GnPuW0hjcys/fXI5H/qvZ6jbre8ZEJFDW6QDACAzxf0AG7Y3dGvez5+r2K+sdncTj7y1EYAlVckvM73ijvnc8cKqvdM7G5u588XV+405tLdi086932C2o7GZXzxXQW19EwtWb+t0uWR+N/89HnqjEoA/la/n/tfWdWs9h7K63c28uS79Pt+poalVX3YkKUW6CwhgQFYsAC48tpjH3t736x/PP2Y0s04t5ZmlmykrLeRffpf8iL8rTvnRs3sff/rXC/Y+/trZRzFz2kimF+fjDq+u3sarq7fxuRml7Gxs5qQfzgNgYtEgPjp1VNL1x7uhEv3q76vY0djCC984k/HDY2Mee1paGZCVmXQ9v3l5DUePGcp3/roYgAs+UMw3HnwbgCtOGs+ellaaWtq46jev86WPTuLUI4eTk5nB7qZWnlq6iYuPK+nwKqtnl29mdP5A1tXs5rxjRu8tb25tI8Os03sz3J3mVicnK4M1W+spHppLbva+29DY3LpfWeJzZJrt164595WzYE0Ny79/3t5lv/fIEuavruGhfz2VnKyMDtvV3NpGduaBHx8tWr+dptY2nly8iWvOmkx+bvZ+7d+2aw93vLiar589hc07Ghmal71PPYClG3fwzLLN/OsZR3a5He5OU2sbA7IyuehnL7Kqun6/q9fir1OrO2u21nPUqCEHvI2HiudXbKF2dxOfOGHs3rLEv6Oeam1zWttSr2tZ1Q6mjBrS4f9E/H/xicWbKCstYEhuFtkZGWRk2H5/z/H/kx0Nzby+toZzjh693/p6mx3KNz2VlZV5eXl5j9bR0trGTU+/yxdPn8hb67ezsa6Bb/9lMadPHsF9s0/er/6Njyxlfe1unl66uUfP21WZGbb3Owu+fOYkhuVls6iyjkuOH8OxY4exvnY3G2obuOOF1byzIflZxc2fOo4nF2/miSWb9pbd+4WTeHjRRkqH57GtvolHFlVRNqFgnzrtTR45mJVbdlE8NHefsZNjxw7l7crY83/ihBJeqtjKB8cX8NPLjmXuWxt5cvEmXqrYurf+oJxMrj59IgV52dzwyFLOmT6Ki44bw4pNOzhtUhEj8weQk5nB5h2NvLluO08v3cxra2uYOXUk85Zv4dyjR/GDSz5AXUMTKzbtomZ3E9/962Juvfx4jh4zlD0trcx9ayN7mlv5h6OKuPrectzh11eWsaOhmbycTM6cOpKp330CgM+fWso/lY1j/upt3Pjo0r3tnDhiEL+eVcasu19jWF42uxpbOGPKSH43/z2+c+E0ykoL2VbfxB9eW0dVXSM3f+p4nl2+hbY25/HFVRw/bhi/eXktAB+cUMDChE+T/chRRfz4Hz/A0IHZfOxnL7Gqup47ryzj6nv3/5v+7IzxXPPRyfz2lbVMLc7nG39axJ6W9+9W/1TZOC4rG8u3/7KYFZt3csunjuf4ccO4/I75nHfMaP7zoun86oXV/OSJ5fzokx/guofeAeB3s0/m1dVb+cVzq/jkCSU89OYGphXnM2XUYP761kZmn3YEd720hnu/cBLjCvNYsrGOI4sGk5Vh5GZn8nZlHU8u2cTU4iFML86nYssudjQ0U7m9gYuOLeb4cQV89s4FnD19FIWDcphWnM/QgdnsaGxmYHYmL1ds5cypIxmSm8WD5ZVkZ2XwodJCXqnYyqmTRvDzZ1eSPzCbS04o4diSoQwfPID1Nbu5/e+rqNnVxCUnlDAwJ5P6PS00t7ZRVdfIhR8oZvjgHKb/55MAvPStMxmWl8PmHY388rlV/PmNSh76P6fyyV++wg0fm87z71YzZdQQpo/JZ2zBQCaNHMIjizby/IotFA0ZwA8/8QFa25x7Xn2PppY2zpo2EjO48q7X2JjwPzBl1BAuKxvLuMI8lm7cwbqa3QzJzeLeV9/b+/82emgua7bW09LqVNU18IPHlu1dvmTYQCA2fpg/MJvX1tQwY2IhY4YOZPqYfG56+l2G5GaxeUfsysInvno6U0fnJ/1f7YyZLXT3spT1DnYAmNl5wK1AJnCnu/84Wd3eCID2mlra+PWLq7n8Q+MYPjj5QK270+axbpLrH17Cx48bwxUnjeeKX8/nq2dN5pZnVgLwwjfOZGlVHd+du0SXhB5iBmZn0tDcmrpiBBQOyqGmvqm/m9FjIwYPYOsu/R8BjBicQ/l3zu7WsodkAJhZJvAucDZQCbwOXOHuSzuq3xcBcKDcneWbdjKtOJbEnXVD1NY3sbu5lartDeQPzCYrwygeOpCtu/awcstOXl21jTfWbSfDYl9ZWVIwkCcWb+JDpYUcO3Yoyzft5PW1NZQOz+PIkYNZuLaWqrpGpoweQnNrG+ML81iztZ5lVTsoKy1kQmEeD5SvZ9LIwUwtzqd8bQ1nTh3J3Dc38sEJBdQ3tXDapBG8smoba7fVs6e5jdFDc/ncjAm8u3kn97+2jouPL2FR5XZOn1zE3S+t4fxjRlNRvYvhgwaQl5PJmGEDWbR+O+MKB1JT30zhoGyG5eXw6KKN7Glp4+LjS1i4rpbJIwczdfQQ/v5uNcePG8bq6nq27trDeceM5tG3q6itbyIjwzjjqCLmLd/C+prdjB+ex5RRQxgxeABNLW00NLdiBm+sq+W0SSPIH5hNc4uzp6WVtdvqOXrMUBasqWHKqMEYRm52BtmZGazdVs+SjTuoqmvk6tOOYGLRYN5cV8v2hmZq6psYPTSX+au2UTpiECMG5zB4QDYzp43k2395h8yMDPa0tJKdmcFpk0bwiRNLeHbZFj5+/Bh+/LflbN7RyAcnFOAOQwdmU7FlFwvfq+XIkYPJzIDR+QO58pQJrK7exd8Wb2JcYR51Dc00t7YxrTif7bubqKlv4r1tu5k5bSQbtzfy2DtVNLW0MXxQDuMK82hsbiV/YDaj83MZlpdNc2sbx5QMZVdjC48v3sSi9ds5qbSQGRML+cSJY3lxZTVLNuzg9bU1VG5v4Oxpo3CcV1dto6RgIKceOYJde1oYPiiHt9Zvp6qukRGDc2hsbmNIbhbTivOp39NCQ1Mr85Zvoa6hmTOnFLGzsYXJowaHv2ZjZ2Mz72yo45MnjGXN1l1MGD6IDDOG5WWTk5XBa2tq2LprDwvW1HDMmHzq97SyYvNOzKAgL4eTSguZWDSIdzfHbqysrG1g+aadHFk0iDOmjCQ3O4OXK7YxaEAmNfXNbNzewHHjhpGfm8XgAVlU79zD9DH5vLOhjnOmj2bLzkaaWtqYt2wLI/MH0NzaxtTR+by3rZ6xBXkMy8vmjXW1FOTlUDRkAIMHZLGprhEHauqbWLllJ2OGDqR4aC5rt+2mpGAg+bnZbNjewKghA3h3805mHDmc+8KZwD+eOJajS/JjZ6drapg+Jp9/+ciRvLRyK4+9U8WOhmbOOXoU62p2k5uVycCcTGp3N5GdmUHZhAI27djD+nCWMLYgj/GFebyxrpa63c20ulM0eAAzp43klVXbGDlkAGu21mMGQ3KzebliK7+/+mRFVQv9AAAE9UlEQVQmFg3u8L0mlUM1AE4BbnD3c8P0dQDu/qOO6h8KASAicrjpagAc7KuASoD1CdOVoUxERA6ygx0AHV0Gss8piJnNMbNyMyuvrtaHsImI9JWDHQCVwLiE6bHAxsQK7n6Hu5e5e1lRUdFBbZyISDo52AHwOjDZzI4wsxzgcuDhg9wGERHhIN8I5u4tZvZl4Elil4He7e5LDmYbREQk5qDfCezujwOPH+znFRGRfUX+s4BERKRjCgARkTR1SH8WkJlVA+/1YBUjgK0pa0VHum0vaJvThbb5wExw95SXUR7SAdBTZlbelbvhoiLdthe0zelC29w31AUkIpKmFAAiImkq6gFwR3834CBLt+0FbXO60Db3gUiPAYiISHJRPwMQEZEkFAAiImkqkgFgZueZ2QozqzCza/u7Pb3FzMaZ2XNmtszMlpjZV0J5oZk9bWYrw++CUG5mdlt4Hd42sxP7dwu6x8wyzexNM3s0TB9hZgvC9v4xfLAgZjYgTFeE+aX92e6eMLNhZvagmS0P+/uUNNjP/xb+rheb2f1mlhu1fW1md5vZFjNbnFB2wPvVzGaF+ivNbFZ32xO5AAhfO/kL4HxgOnCFmU3v31b1mhbg6+4+DZgBfCls27XAPHefDMwL0xB7DSaHnznA7Qe/yb3iK8CyhOmfADeH7a0FZofy2UCtu08Cbg71Dle3Ak+4+1TgOGLbH9n9bGYlwDVAmbsfQ+zDIi8nevv6t8B57coOaL+aWSFwPXAycBJwfTw0Dpi7R+oHOAV4MmH6OuC6/m5XH23rXGLfr7wCKA5lxcCK8PhXxL5zOV5/b73D5YfYd0bMAz4KPErsS4W2Alnt9zexT5k9JTzOCvWsv7ehG9ucD6xp3/aI7+f4twUWhn33KHBuFPc1UAos7u5+Ba4AfpVQvk+9A/mJ3BkAafK1k+GU9wRgATDK3asAwu+RoVoUXotbgG8CbWF6OLDd3VvCdOI27d3eML8u1D/cTASqgd+Erq87zWwQEd7P7r4B+G9gHVBFbN8tJPr7Gg58v/ba/o5iAKT82snDnZkNBv4MfNXdd3RWtYOyw+a1MLOLgC3uvjCxuIOq3oV5h5Ms4ETgdnc/Aajn/W6Bjhz22x26MC4GjgDGAIOIdYG0F7V93Zlk29hr2x7FAEj5tZOHMzPLJvbm/3t3fygUbzaz4jC/GNgSyg/31+LDwMfNbC3wB2LdQLcAw8ws/l0Widu0d3vD/KFAzcFscC+pBCrdfUGYfpBYIER1PwOcBaxx92p3bwYeAk4l+vsaDny/9tr+jmIARPZrJ83MgLuAZe5+U8Ksh4H4lQCziI0NxMuvDFcTzADq4qeahwN3v87dx7p7KbH9+Ky7fwZ4Drg0VGu/vfHX4dJQ/7A7KnT3TcB6M5sSimYCS4nofg7WATPMLC/8nce3OdL7OjjQ/fokcI6ZFYQzp3NC2YHr7wGRPhpkuQB4F1gFfLu/29OL23UasVO9t4G3ws8FxPo+5wErw+/CUN+IXRG1CniH2BUW/b4d3dz2M4BHw+OJwGtABfAnYEAozw3TFWH+xP5udw+293igPOzrvwIFUd/PwPeA5cBi4D5gQNT2NXA/sTGOZmJH8rO7s1+BL4RtrwCu6m579FEQIiJpKopdQCIi0gUKABGRNKUAEBFJUwoAEZE0pQAQEUlTCgARkTSlABARSVP/H627+yFaMyfEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def q_learning(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1, Q=None):\n",
    "    \"\"\"\n",
    "    Q-Learning algorithm: Off-policy TD control. Finds the optimal greedy policy\n",
    "    while following an epsilon-greedy policy\n",
    "    \n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI environment.\n",
    "        num_episodes: Number of episodes to run for.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        alpha: TD learning rate.\n",
    "        epsilon: Probability to sample a random action. Float between 0 and 1.\n",
    "        Q: hot-start the algorithm with a Q value function (optional)\n",
    "    \n",
    "    Returns:\n",
    "        A tuple (Q, stats).\n",
    "        Q is the optimal action-value function, a dictionary mapping state -> action values.\n",
    "        stats is a list of tuples giving the episode lengths and rewards.\n",
    "    \"\"\"\n",
    "    \n",
    "    # The final action-value function.\n",
    "    # A nested dictionary that maps state -> (action -> action-value).\n",
    "    if Q is None:\n",
    "        Q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    \n",
    "    # Keeps track of useful statistics\n",
    "    stats = []\n",
    "    \n",
    "    # The policy we're following\n",
    "    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n",
    "    \n",
    "\n",
    "    for i_episode in tqdm(range(num_episodes)):\n",
    "        i = 0\n",
    "        R = 0\n",
    "        \n",
    "#         retrieving initial state\n",
    "        initial_state = np.argmax(env.isd)\n",
    "    \n",
    "        done = False\n",
    "    \n",
    "#         for every step of the episode\n",
    "        while not done:\n",
    "    \n",
    "    #         retrieve action probabilities\n",
    "            action_probs = policy(initial_state)\n",
    "    #         sample an action\n",
    "            action = np.random.choice([0,1,2,3], p=action_probs)\n",
    "\n",
    "            trans_prob, new_state, r, done = env.P[initial_state][action][0]\n",
    "        \n",
    "            i += 1\n",
    "            R += r\n",
    "            \n",
    "#             get values of next state for every action\n",
    "            next_action_values = []\n",
    "            for a in range(env.action_space.n):\n",
    "                next_action_values.append(Q[new_state][a])\n",
    "#             get the maximum value of the possible future states\n",
    "            max_next_action = np.max(next_action_values)\n",
    "            \n",
    "#             update Q value\n",
    "            Q[initial_state][action] += alpha* ( r + discount_factor * max_next_action - Q[initial_state][action])\n",
    "#             update state and action according to the step\n",
    "            initial_state = new_state\n",
    "        \n",
    "        stats.append((i, R))\n",
    "    episode_lengths, episode_returns = zip(*stats)\n",
    "    return Q, (episode_lengths, episode_returns)\n",
    "\n",
    "Q_q_learning, (episode_lengths_q_learning, episode_returns_q_learning) = q_learning(env, 1000)\n",
    "\n",
    "# We will help you with plotting this time\n",
    "plt.plot(episode_lengths_q_learning)\n",
    "plt.title('Episode lengths Q-learning')\n",
    "plt.show()\n",
    "plt.plot(episode_returns_q_learning)\n",
    "plt.title('Episode returns Q-learning')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "829728d6b5eea5b47885e984a7faa416",
     "grade": false,
     "grade_id": "cell-9f1fcee44ba712c2",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Now compare the episode returns while learning for Q-learning and Sarsa (maybe run some more iterations?), by plotting the returns for both algorithms in a single plot, like in the book, Example 6.6. In order to be able to compare them, you may want to zoom in on the y-axis and smooth the returns (e.g. plotting the $n$ episode average instead). Which algorithm achieves higher return during learning? How does this compare to Example 6.6 from the book? Try to explain your observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "3c1a110fe85c38220afed145a8cf09bc",
     "grade": true,
     "grade_id": "cell-69ed62a52a44dd78",
     "locked": false,
     "points": 1,
     "schema_version": 1,
     "solution": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:06<00:00, 1566.43it/s]\n",
      "100%|██████████| 10000/10000 [00:07<00:00, 1267.39it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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eX0dPUIAQkX7FOccVV1zBZZddxooVK1ixYgWNjY184Qtf6LF1plKpg85/9tlne2zdPUlvcxWRfuXJJ5+krKyM6667DoBoNMp3vvMdxo8fz+23305VVVXe5V566SVuueUW6uvrqamp4e6772b06NH85Cc/Yd68eSQSCY4//nh+8YtfUFFRwZw5cxg2bBivvPIK06dPp7q6mvXr17N69WrWr1/PZz/7WW6++WbAv/Svvr6ep59+mrlz51JTU8OSJUs4/fTT+eUvf4mZ8eijj3LLLbdQU1PD9OnTWb16NY888kiv7bd8FCBEpGf88Uuw5fXuLXPUyXDxNw+aZenSpZx++ult0gYNGsSECRNYuXIl06ZNO2CZZDLJTTfdxO9//3tGjBjBgw8+yG233cZdd93FFVdcwSc/+UkAvvKVr/DTn/6Um266CYC33nqLBQsWEI1GmTt3Lm+++SZPPfUUdXV1TJ48mc985jMtL/PLeuWVV1i6dCljxozhHe94B3/729+YMWMGn/rUp/jLX/7Csccey+zZs7uyl7qNAoSI9CvOOcwsb3pHli9fzpIlS7jgggsASKfTjB49GoAlS5bwla98hT179lBfX8+FF17YstwHPvABotFoy/R73vMeSktLKS0tZeTIkWzdupVx48a1WdcZZ5zRkjZt2jTWrl1LVVUVEydO5NhjjwVg9uzZzJs37wj3QPdRgBCRntHJN/2eMnXqVH7961+3Sdu3bx9bt27le9/7Hq+88gpjxozh0UcfbZnvnGPq1Kk899xzB5Q3Z84cfve733Hqqady99138/TTT7fMq6ysbJM3+8tz4E9t5bs2kS9PMb0TL5cuUotIv3LeeefR0NDAPffcA/jewOc//3luvPFGfvazn/Hqq6+2CQ4AkydPZvv27S0BIplMsnTpUgDq6uoYPXo0yWSSe++9t0fqfOKJJ7J69WrWrl0LwIMPPtgj6zlcChAi0q+YGb/97W+ZP38+kyZNYvjw4UQiEW677bYOlykpKWH+/Pl88Ytf5NRTT2XatGktdx59/etf58wzz+SCCy7gxBNP7JE6l5eX85//+Z9cdNFFvPOd7+Soo45i8ODBPbKuw9Gl132b2QeAucBJwBnOuUU5824FPg6kgZudc493Vp5e9y3StxXj676fffZZZs+ezW9+85sDLl4Xk/r6eqqqqnDOccMNNzBp0iQ+97nPHZCvN1/33dVrEEuAK4A7cxPNbApwNTAVGAMsMLMTnHPpLq5PROSwnHXWWaxbt67Q1ejUT37yE37+85+TSCQ47bTT+NSnPlXoKnUtQDjn3gDy3TFwKfCAc64ZWGNmK4EzgAOvAImICJ/73Ofy9hgKqaeuQYwFNuRM14Y0EennivWOnP6gt/dtpz0IM1sAjMoz6zbn3O87WixPWt4tM7PrgesBjjnmmM6qIyJFrKysjJ07dzJ8+PC8zyLIkXPOsXPnTsrKynptnZ0GCOfc+UdQbi1wdM70OGBTB+XPA+aBv0h9BOsSkSIxbtw4amtr2b59e6Gr0i+VlZUd8OBdT+qpB+UeBu4zs2/jL1JPAl7ooXWJSJGIx+MtTwNL39elaxBmdrmZ1QKzgP82s8cBnHNLgYeAZcBjwA26g0lEpG/p6l1MvwV+28G824Hbu1K+iIgUjp6kFhGRvBQgREQkLwUIERHJSwFCRETyUoAQEZG8FCBERCQvBQgREclLAUJERPJSgBARkbwUIEREJC8FCBERyUsBQkRE8lKAEBGRvBQgREQkLwUIERHJSwFCRETyUoAQEZG8FCBERCQvBQgREclLAUJERPJSgBARkbxiha6AiBQh5yCTBpdu95npYnomjGfy5M1Jd5l2gztwHHeQfDnzDsjXnWXlyUfOvmuTTttxnJ8+rM98y/UcBQgR8A1TJgnpJKQTOUOYzqTyzEsd2MhlUu2GnPSWPO3zpVvXnUn6ctOJ1vFMsjVfS2OU04hm2jWomTTgwCKH0Ii3a6hzl+9PLOIHrHW8ZbAwRDrJx2HmibZdB2E9kWhOvXLSD/szZ3me75HdpgAhhZfJQKrJD8kGSDRAcj8kG/10qrl1PNnop9PNkErkfCY6SEu0y99Bmkv3/nZH4hCJhSEC0RKfFo21HW/JF/WNTiQKFg+f7RqjSE7jlQ0SLctEw/xou7Jy0i3Sbl776SNJj7Sd32l9IjnldKVRt9bP/u7qX/ZIsQoQ0rlMJjTc9ZDYD811/jNR33Y8O6+5LjTCTZBsglRjzmcYcuelE0det2ipb0xjJX48+5mbVlIB0aFhul2+lrQS3whH42H5eCijtLURj5a0pkdLfOOdbVSjOY14Nr9Fc9JyGsBI3DeMUhScc6QzjrRz/rtKJkMmA+mQnnGOVMaRybTmc86RcfjpjJ+fzmRIZ3yac6E8B5ls/lBmdtl8ZaZDHpwjEjGiZgypiFMaC72OEPdTGUcqnfGfmcxBt68rFCD6u0wGmvdB094w7IHmev/ZtNc35o27oWEXNOxsbeizjX+y0U8f6imHWBmUVPnPWCnEy/14vBwqaiBeBrHy/J/RUiip9EO8HOIVfjxaEsqt8Gmx0tZGvA9/O3Sh4UmlHclMhlTa/7On0m3TktmGIJ0hmfYNSkv+MG9IRZyR1WUMrYxTHo8SjZg/G4VvUyJhPyVSGZpTaZpTmTD48UQqQybjcISzWOHv7VoaOJ8WjUSImoXGs7U+bRqstJ+XHc82nvnypjOt2+y3K9vQurBu/LSjTWOabZgz7cZbGt1sgxzWl82Xabd8Omyb5KcA0dc45xv3fZth30bYsw7qtvrGvTE08o27oTEEhOZ9dNq4l1RDxVAoHwal1VA92n+WVoVGusqPl1R1MF7ZOh3t2UMqmc7Q0Jxmf2OK+uZmmpMZ4jHfGGYb1UzGURqLUl4SaW38nCMZGttEKkNTMsP+5hSNyTQuzEukfcMEkEpnaEplSIa0ZE5jnEy3NuYtDXa7Rjzb6LU2/GH5dmkDTTRiRCNGLDtEI0QjRjxiRKNGLOKno2bhLJLP579N++UjZpTFI0TMWvJGIrn5rCVfNIIPahFa8kXNr6ttvpzB2q4vu3xuvohlB58nFrWW4BnJyZvNk81vIb8Zvr65+VrWDYa1BLzd+xOkQq8kKxaJEIsa8bDeid/qmb+XAkQxSSd9o1+3xQ/126B+iw8Au9fCvlo/nmpst6BB+VCoGA4Vw2DQWBg5FcqHQNngA4fS6jA+xI9H4z22Sc45GpNp9jYm2deYYl9Tkr0NSf8Z0vY2JqlrStKQTNOcTNOYTNOYSJN20JhIsb85TUMixf5EmkSq57rT7ZXFI8QjkZaGKx71DUE8/HNm06KhoSuLR4iVxny+ln/gSEtDGAsNSW5aPPsZtXb5Iu3KaVumb1QjoWEy9jQk2VbXxO79CZqSGdLO+euloYOVDrutNBahNB6hNBb147EIJbEIJdEIkYiFZULjHPZDJDRgvhwfELONfDTSWp/s9mUb+ey+yc7PNrxyeMYOKS/YursUIMzs34H3AQlgFXCdc25PmHcr8HEgDdzsnHu8i3XtHzJp39jvXAnb3/Sfe9bD7nWwd4O/WyWXRaHqKBg6HsbOgOpR/hv+oDE+EAw5BqpGtr0zogc452hIpNnTmGRPQ4K9Dckw7hv6PY0hrSHJ7oYE2+ub2bU/wf7mFMn0wb8pV5XGqC6LUR6PUl4SpSx8RswYPaiMitIolSUxKkqjVJXEqCiNUVESpbosRiwSIRMaw1g0NJhmNKcyNCbTlER9IxgJ31JLQoNYGotSWRqlPB7FzDdmJTH/DRCy3/LUmMnA1tUexBPArc65lJl9C7gV+KKZTQGuBqYCY4AFZnaCc4W4VaSA6rfDlsWwbRlsXeY/ty9v2wOoHOEb+THT4G1X+kBQPQaqj4KqUb5X0E0XNJtTabbubWZ7fTP7Gv23+KZkmt0NSbbua2JnvW/QS2IR9jYm2VmfYF9TsuVUzMEa+pJohCEVcYZUxBlcHuekUYMYVllCVVmMweU+bVBZ+CyPtUxXl8WIRXXBVqQYdSlAOOf+lDO5EPj7MH4p8IBzrhlYY2YrgTOA57qyvqLlnO8VbH7NB4CtS2DjS/50UVbVKBh5Erz94zDiRKg5AWom+VNCR6ApmWbX/gS79ifYuT/Brv3N7KxP0JRMs72uma37mtnVkGBPQyJ8k/enbjpSVRpjWGUJlaUxkukMg8pijB9ewaDyOJUlUSpKYwwpzwaAkjbBYEh5CWXxiL5xi/Qz3XkN4mPAg2F8LD5gZNWGtAOY2fXA9QDHHHNMN1anh+1aA6ufgjV/gbXPwP7tYYb5XsAxs2DMaTDqZD90EAh27U/w+sa9vLl5Hyu31bNqez0ZB2ccO4y6phR1TUnqm1PUNaXY35yivjnFrv0JGhIdN/aDymKMHFTGsMoSJtZUcfr4EipLogwujzNqcBk11aUMKY9TXRanPKRXlepylIi01WmrYGYLgFF5Zt3mnPt9yHMbkALuzS6WJ3/e8xPOuXnAPIAZM2YU920dezfCGw/D4odg08s+rXoMHPcuHxBGn+p7CfGOLyo1JdM8s2IHTy7fxjMrdrB+V0PLvBHVpUysqaR2dyP/9dfVDKssZVBZjKqyGFWlMYZWVFBd5r/p5w7DWz5LKY1HKIv37PUIERkYOg0QzrnzDzbfzK4F3guc51rvw6oFjs7JNg7YdKSVLKiGXbDk17D4Qah90aeNOgUu+DpMvgSGH3fQe/F370+waN1u3tpax7OrdvDyuj00JtNUlcaYOXE4H5l5DG8bO5ipowczuMLfTZS9Vzuuc/MiUkBdvYvpIuCLwNnOuYacWQ8D95nZt/EXqScBL3RlXb1uw4uw6C5YMt8/6XvU2+C8f4aTLoWa4ztcrK4pyQtrdvHcqp08u2onb2zZ1/IgzkmjB/HBGeM498SRnHVcDSWx/AEgEjEieTthIiK9p6snnn8AlAJPhAuUC51zn3bOLTWzh4Bl+FNPN/SJO5gyGXjrMXhhnr++EK+A6dfC9I/600d5NCXTvLx+N39dsYPnVu3k9Y17SWccJbEIpx8zlM+dfwKzjhvOcSOqGFZZ0ssbJCJy5Lp6F1OHX6Wdc7cDt3el/F615i/w2Jdh6+v+jqMLvg7Tr/EPm7WTSmd4ZuUO/vDaZh5fuoX65hSxiDHt6CH8wznHMeu44Uw/ZqiuBYhIn6ZbV3augse/7HsOg4+Gy+f55xHyvDJiw64GfrlwHb9+uZYd9QkGlcW4cOooLpx6FO84voZK3QkkIv3IwG3RMmlY+CN48uv+aeXz58KZn/EvjcvhnGPh6l3c/ewanli2FTPj/JNGcvlpYzn3xJGtb1kUEelnBmaA2LMBfvNJWP8cnHAxvPc7MGh0myzJdIbfvrKRu55Zw5tb6hhaEefTZx/HR2aOZ0wB340iItJbBl6AWPNXeOga/2K8y34Mp17d5jbVRCrDHU8s584/rwbgxFHVfOvKk7l02lhdUxCRAWVgBYhlD8OvPw5Dj4Wr7/Wvugicc/xp2Vb+7bE3WbV9P8ePrOK2S07inMkj9AoJERmQBk6AePU++P0N/o2oH37Ivx4b2FHfzA+eXMndz64FYNLIKn567QzOO+moAlZWRKTwBkaAeOWXPjhMPAeuutf/yA3w1PJt/O9fvcbuhiTnTB7BRVNHceXp4/QEs4gIAyFArF8If/gsTDwXZj8A8TKS6Qw33/8Kf1yyhRNHVXPvJ2YyeVR1oWsqIlJU+neA2FsLD37U/97CB34G8TJ21jfziXsW8cr6PVw49Si+d/VpuvgsIpJH/w0QyUZ44MP+c84jUD6U9TsbuPZnL7BpTyP/b/ZpvO/UMYWupYhI0eqfAcI5ePhm/wM+s++HEZPZ25jko3c9z97GJPd98kxOH39kP9QjIjJQ9M+rsa/eC68/BO+6DSZfTCbj+PxDr7JxdyM/vXaGgoOIyCHofwGicQ888c/+B3ze+XkAfvTnVSx4Yxtfec9JCg4iIoeo/wWIv/y7/5Gfi78FkQjPrNjBHX9azvtPHcO1Z00odO1ERPqM/hUgdqyE5++E0z4Mo09l7Y793PzAKxw/sopvXnmynogWETkM/StA/Ok2/3vQ532VZDrDTfe/Qiqd4ccfOZ2Kkv55PV7DOlPeAAARHElEQVREpKf0nwCx8WX/mw7v/CxUjeTHT6/i9Y17+daVpzBxRFWhayci0uf0nwDx529B2RB4+yd4bMlm7njiLd5zymguPnl058uKiMgB+keAqF3kew+zbiBdMohP//JlAP7l/VMLXDERkb6rfwSIv94BFcNh5mf479c3A/Ddq6ZRU1Va4IqJiPRdfT9A7Nvsew+nfRRKq/npM2uYWFPJ+/UaDRGRLun7AeL1X4HLwGkf5dUNe3htwx6umTWeSES3tIqIdEXfDxCvPQDj3g41x3PvwnVUlES58vRxha6ViEif17cDxJbXYdtSOOUq9jYk+cPiTVw6bSzVZfFC10xEpM/r2wFi2cNgEZh6BfNfrqUpmeEjM48pdK1ERPqFvh0g3noMjj4TKocz/6VaTh03mKljBhe6ViIi/ULfDRD7NsGWxXDChTy1fBtvbN6nHwASEelGXQoQZvZ1M1tsZq+a2Z/MbExINzP7vpmtDPOnd091c6xc4D8nvZt7F64D4PLTxnb7akREBqqu9iD+3Tl3inNuGvAI8M8h/WJgUhiuB37UxfUcaNVTUDWKTM1JLK7dy8VvG8VwPRgnItJtuhQgnHP7ciYrARfGLwXucd5CYIiZdd9LkTIZWP0UHHcur27cy7a6Zi6cOqrbihcRkW74TWozux24BtgLnBuSxwIbcrLVhrTNeZa/Ht/L4JhjDvEOpF2roXE3HDOLx5duIRYxzp088oi3QUREDtRpD8LMFpjZkjzDpQDOuducc0cD9wI3ZhfLU5TLk4Zzbp5zboZzbsaIESMOrdab/Mv4GDudJ5ZuZdZxwxlcoWcfRES6U6c9COfc+YdY1n3AfwNfxfcYjs6ZNw7YdNi168jGlyFWTm18PKt3rOcjM8d3W9EiIuJ19S6mSTmT7wfeDOMPA9eEu5lmAnudcwecXjpim16B0afw/Fp/CWTWccO7rWgREfG6eg3im2Y2GcgA64BPh/RHgUuAlUADcF0X19MqnYLNr8Hpc1i4eidDKuJMPqq624oXERGvSwHCOXdlB+kOuKErZXdo+5uQaiQz5jT+/Mh2Zk0crje3ioj0gL73JHW4QL0qfgLb6pq5YMpRBa6QiEj/1PcCxMaXoXQwC/f4dy6dceywAldIRKR/6nsBYtPLMGYaP1/oH7MYO6S8wBUSEemf+laASKdg2xtkRp3Kym31TBhegZmuP4iI9IS+FSD2rIN0gk0l/rmHOWdNKGx9RET6sb4VIHa8BcCbSf/epbP1eg0RkR7TJwPEi3XDqCyJMn5YRYErJCLSf/W9AFE5gpe2wUmjB+n5BxGRHtS3AsSuNbhhx/PG5n1MGTOo0LUREenX+lyAqK88mv2JNFMVIEREelTfCRCJBqjbxKaI/92hKaMHF7hCIiL9W98JELvXArA8UUMsYkw6qqqw9RER6ef6ToDYtRqAV+qHcvzIKsri0QJXSESkf+s7AWJvLQDP7KjQBWoRkV7QdwJE3WZcJM7K+hKmjFaAEBHpaX0qQDSXj8QRYeoYXaAWEelpfSdA7NvEnlgNACeO0i/IiYj0tL4TIOo2s80No6aqhKGVJYWujYhIv9c3AoRzsG8zL+4qY2R1WaFrIyIyIPSNANG8D5L72eKGMmaIAoSISG/oGwFi32YAtrqhnKNXfIuI9Iq+ESDqsgFiGB+YMa7AlRERGRj6VICIDhlDaUxPUIuI9Ia+ESD2bQKgsuboAldERGTgiBW6Aodiz7b14Cp5YsW+QldFRGTA6BMBIr13MzvcUG654IRCV0VEZMDoE6eYXMMu9lDFJSePLnRVREQGjG4JEGb2T2bmzKwmTJuZfd/MVprZYjOb3qXym/awx1XpGQgRkV7U5QBhZkcDFwDrc5IvBiaF4XrgR11ZRzyxl/2RKipK+sQZMRGRfqE7ehDfAb4AuJy0S4F7nLcQGGJmR3x+qDRVRyKuV3yLiPSmLgUIM3s/sNE591q7WWOBDTnTtSEtXxnXm9kiM1u0ffv2AzOkmil1TSRL9IpvEZHe1Ok5GzNbAIzKM+s24MvAu/MtlifN5UnDOTcPmAcwY8aMA/M07vH5yoZ0VlUREelGnQYI59z5+dLN7GTgWOA1MwMYB7xsZmfgewy5T7WNAzYdUQ2bfIAwBQgRkV51xKeYnHOvO+dGOucmOOcm4IPCdOfcFuBh4JpwN9NMYK9zbvMRrahxt69o5bAjraqIiByBnrot6FHgEmAl0ABcd6QFNdXtpAworVaAEBHpTd0WIEIvIjvugBu6o9z6PTsoAyoG1XRHcSIicoiK/knqxr07AKgeNqLANRERGViKPkA01+0EYOhQBQgRkd5U9AEiuX83+1w5IwZXFLoqIiIDStEHCBp3s9dVMayypNA1EREZUIo+QESb91IfqSIeLfqqioj0K0Xf6sYTe2mIVBW6GiIiA07RB4jS1D6aYnpRn4hIbyv6AFGeqadZb3IVEel1xR0gnKMqU0cirje5ioj0tuIOEMkG4qRI6VXfIiK9rrgDRMurvhUgRER6W1EHCNe4y3+WDS1wTUREBp6iDhCJOv+qbyvXb0GIiPS2og4QjXX+RX2RCvUgRER6W1EHiES970HE9WNBIiK9rqgDRKphHwCllbpILSLS24o6QCSb6gAoq9KDciIiva2oA0S6aT9pZ1SW61XfIiK9ragDRCZRz37KqC6PF7oqIiIDTlEHCNfcQCOlVJZ2209ni4jIISruAJHYT4MrpUoBQkSk1xV1gIikGmikjNJYUVdTRKRfKuqWN5JqoClShpkVuioiIgNOUQeIWKqRhJUVuhoiIgNScQeIdAOJSHmhqyEiMiAVdYCIZ5pIRRUgREQKoagDREmmUQFCRKRAuhQgzGyumW00s1fDcEnOvFvNbKWZLTezC4+k/NJME6mYnqIWESmE7njA4DvOuf/ITTCzKcDVwFRgDLDAzE5wzqUPudRMhjKacQoQIiIF0VOnmC4FHnDONTvn1gArgTMOq4RUIwAurlNMIiKF0B0B4kYzW2xmd5lZ9pd9xgIbcvLUhrRDl2zynwoQIiIF0WmAMLMFZrYkz3Ap8CPgOGAasBm4I7tYnqJcB+Vfb2aLzGzR9u3bW2ckG/x8BQgRkYLo9BqEc+78QynIzH4CPBIma4Gjc2aPAzZ1UP48YB7AjBkzWoJIOtFIFIiU6BqEiEghdPUuptE5k5cDS8L4w8DVZlZqZscCk4AXDqfs5qb9AERL1YMQESmErt7F9G9mNg1/+mgt8CkA59xSM3sIWAakgBsO6w4moKmhngogqh6EiEhBdClAOOc+epB5twO3H2nZiUbfg4iVVR5pESIi0gVF+yR1oslfpI6XqgchIlIIRRsgks2+BxFXD0JEpCCKNkCkmn0PorRcAUJEpBCKN0A0ZQOETjGJiBRC0QaIdHhQrqy8qsA1EREZmIo2QGSaswFCp5hERAqhaAOESzSRdkZFuR6UExEphOINEMlGmiihojRe6KqIiAxIxRsgUk0kKCEayffePxER6WlFHCASJK07fs9IRESORNEGCNIJUqbTSyIihVK0AcLSzQoQIiIFVLQBgnSSjAKEiEjBFG2AsHSCdEQBQkSkUIo2QERcgowChIhIwRRtgIhlkqStpNDVEBEZsIo2QERcEhdVD0JEpFCKNkDEXJJMRD0IEZFCKdoAEXVJXLS00NUQERmwijZAxF0Kp4vUIiIFU5QBwjlHjCTEdIpJRKRQijJAJNOOElIQVYAQESmUogwQzak0JaSwmK5BiIgUSlEGiKZkhhKSmHoQIiIFU5QBojmVJq4ehIhIQRVngEgkiJrDdJFaRKRgijNANDcBEImrByEiUihFGSCSIUBE1YMQESmYLgcIM7vJzJab2VIz+7ec9FvNbGWYd+HhlJlo6UGUdbV6IiJyhLr0o89mdi5wKXCKc67ZzEaG9CnA1cBUYAywwMxOcM6lD6XcZCL0IEp0iklEpFC62oP4DPBN51wzgHNuW0i/FHjAOdfsnFsDrATOONRCU4lmAGLqQYiIFEyXehDACcDfmdntQBPwT865F4GxwMKcfLUh7QBmdj1wfZhsNrMlLTP/5Trgui5Wsc+qAXYUuhJFQvuilfZFK+2LVpN7otBOA4SZLQBG5Zl1W1h+KDATeDvwkJlNBCxPfpevfOfcPGBeWNci59yMQ6t6/6Z90Ur7opX2RSvti1Zmtqgnyu00QDjnzu9onpl9BviNc84BL5hZBh/Va4Gjc7KOAzZ1sa4iItKLunoN4nfAuwDM7ASgBN/lexi42sxKzexYYBLwQhfXJSIivair1yDuAu4K1w0SwLWhN7HUzB4ClgEp4IZDvINpXhfr059oX7TSvmilfdFK+6JVj+wL8+25iIhIW0X5JLWIiBSeAoSIiORVNAHCzC4Kr+VYaWZfKnR9upuZHW1mT5nZG+G1JP8Y0oeZ2RNmtiJ8Dg3pZmbfD/tjsZlNzynr2pB/hZldW6ht6iozi5rZK2b2SJg+1syeD9v1oJmVhPTSML0yzJ+QU8YRv9KlWJjZEDObb2ZvhuNj1kA9Lszsc+H/Y4mZ3W9mZQPluDCzu8xsW+6zYN15HJjZ6Wb2eljm+2aW73GEtpxzBR+AKLAKmIi/E+o1YEqh69XN2zgamB7Gq4G3gCnAvwFfCulfAr4Vxi8B/oh/pmQm8HxIHwasDp9Dw/jQQm/fEe6TW4D7gEfC9EPA1WH8x8Bnwvg/AD8O41cDD4bxKeFYKQWODcdQtNDbdQT74efAJ8J4CTBkIB4X+Idp1wDlOcfDnIFyXAD/C5gOLMlJ67bjAH8n6aywzB+BizutU6F3Sqj4LODxnOlbgVsLXa8e3ubfAxcAy4HRIW00sDyM3wnMzsm/PMyfDdyZk94mX18Z8M/G/A/+NulHwkG7A4i1PyaAx4FZYTwW8ln74yQ3X18ZgEGhUbR26QPuuAgBYkNo3GLhuLhwIB0XwIR2AaJbjoMw782c9Db5OhqK5RRT9sDI6vDVHP1B6AqfBjwPHOWc2wwQPkeGbB3tk/6yr74LfAHIhOnhwB7nXCpM525XyzaH+XtD/v6wLyYC24GfhdNt/2VmlQzA48I5txH4D2A9sBn/d36JgXlcZHXXcTA2jLdPP6hiCRCH/GqOvs7MqoBfA591zu07WNY8ae4g6X2Gmb0X2Oaceyk3OU9W18m8Pr8v8N98pwM/cs6dBuzHn0roSL/dF+H8+qX400JjgErg4jxZB8Jx0ZnD3fYj2ifFEiAGxKs5zCyODw73Oud+E5K3mtnoMH80kH0jbkf7pD/sq3cA7zeztcAD+NNM3wWGmFn24c3c7WrZ5jB/MLCL/rEvaoFa59zzYXo+PmAMxOPifGCNc267cy4J/AY4i4F5XGR113FQG8bbpx9UsQSIF4FJ4W6FEvwFp4cLXKduFe4Y+CnwhnPu2zmzHgaydxpci782kU2/JtytMBPYG7qYjwPvNrOh4RvXu0Nan+Gcu9U5N845NwH/t37SOfdh4Cng70O29vsiu4/+PuR39INXujjntgAbzCz7Ns7z8G8gGHDHBf7U0kwzqwj/L9l9MeCOixzdchyEeXVmNjPs22tyyupYoS/K5Fw0uQR/Z88q4LZC16cHtu+d+C7dYuDVMFyCP2f6P8CK8Dks5Dfgh2F/vA7MyCnrY/jf2FgJXFfobevifjmH1ruYJuL/kVcCvwJKQ3pZmF4Z5k/MWf62sI+Wcwh3ZRTjAEwDFoVj43f4u08G5HEB/AvwJrAE+AX+TqQBcVwA9+OvvSTx3/g/3p3HATAj7NdVwA9od2NEvkGv2hARkbyK5RSTiIgUGQUIERHJSwFCRETyUoAQEZG8FCBERCQvBQgREclLAUJERPL6/3g6QOMi6CfFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q_sarsa_10k, (episode_lengths_sarsa_10k, episode_returns_sarsa_10k) = sarsa(env, 10000)\n",
    "Q_q_learning_10k, (episode_lengths_q_learning_10k, episode_returns_q_learning_10k) = q_learning(env, 10000)\n",
    "\n",
    "sarsa_sum = 0\n",
    "sarsa_averages = []\n",
    "\n",
    "\n",
    "indexes = []\n",
    "\n",
    "for i in range( len(episode_returns_sarsa_10k ) ):\n",
    "    sarsa_sum += episode_returns_sarsa_10k[i]\n",
    "    sarsa_averages.append(sarsa_sum / (i+1))\n",
    "    indexes.append(i)\n",
    "    \n",
    "q_learn_sum = 0\n",
    "q_learn_averages = []\n",
    "    \n",
    "for i in range( len(episode_returns_q_learning_10k ) ):\n",
    "    q_learn_sum += episode_returns_q_learning_10k[i]\n",
    "    q_learn_averages.append(q_learn_sum / (i+1))\n",
    "    \n",
    "\n",
    "\n",
    "plt.plot(indexes, sarsa_averages, indexes, q_learn_averages)\n",
    "plt.title('Average returns over number of episodes')\n",
    "plt.legend(['SARSA', 'Q-learning'])\n",
    "plt.axis([0, len(sarsa_averages), -60, 0])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "a3357293c326223f2a02cae0f38ca24a",
     "grade": true,
     "grade_id": "cell-7acf9de8c94a171f",
     "locked": false,
     "points": 1,
     "schema_version": 1,
     "solution": true
    }
   },
   "source": [
    "The converge rate of the two algorithms seems approximately the same, nevertheless, the Q-learning algorithm has consistantly better average reward values. Additionally, Sarsa flaxuates slightly more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "f2f954f745662334010f6fb0fcfd9896",
     "grade": false,
     "grade_id": "cell-316d3cfd35d55387",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "After we have learned the policy, we do not care about exploration any more and we may switch to a deterministic (greedy) policy instead. If we evaluate this for both Sarsa and Q-learning (actually, for Q-learning the learned policy is already deterministic), which policy would you expect to perform better? Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "011f8038ac100bfdc5e40b78c1bdc2f8",
     "grade": true,
     "grade_id": "cell-ea5058e6f352d717",
     "locked": false,
     "points": 1,
     "schema_version": 1,
     "solution": true
    }
   },
   "source": [
    "An algorithm like Q-learning would be preferable in situations where we do not care about the agent's performance during the training process, but we just want it to learn an optimal greedy policy that we'll switch to eventually. On the other hand, with Sarsa typically performs better in cases where we care about the agent's performance during the process of learning/generating experience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "57ab54058d433e24421d1e1224a9bc87",
     "grade": false,
     "grade_id": "cell-8bcc6f5839a36860",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Please run the experiments to test your hypothesis (print or plot your results). How many runs do you need to evaluate the policy? Note: without learning, the order of the episodes is not relevant so a normal `plt.plot` may not be the most appropriate choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "149c39efef43f1807d2b06e6bc50bf95",
     "grade": true,
     "grade_id": "cell-55f9d1767bb7c011",
     "locked": false,
     "points": 1,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:06<00:00, 1609.23it/s]\n",
      "100%|██████████| 10000/10000 [00:07<00:00, 1370.56it/s]\n",
      "100%|██████████| 5000/5000 [00:02<00:00, 2104.12it/s]\n",
      "100%|██████████| 5000/5000 [00:03<00:00, 1586.32it/s]\n"
     ]
    }
   ],
   "source": [
    "# converge to Q values\n",
    "Q_sarsa_10k, (episode_lengths_sarsa_10k, episode_returns_sarsa_10k) = sarsa(env, 10000)\n",
    "Q_q_learning_10k, (episode_lengths_q_learning_10k, episode_returns_q_learning_10k) = q_learning(env, 10000)\n",
    "\n",
    "Q_sarsa_5k, (episode_lengths_sarsa_5k, episode_returns_sarsa_5k) = sarsa(env, 5000, epsilon = 0, Q = Q_sarsa_10k)\n",
    "Q_q_learning_5k, (episode_lengths_q_learning_5k, episode_returns_q_learning_5k) = q_learning(env, 5000, epsilon = 0, Q = Q_q_learning_10k)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(indexes[:5000], episode_returns_sarsa_5k, indexes[:5000], episode_returns_q_learning_5k)\n",
    "plt.title('Return of every episode')\n",
    "plt.legend(['SARSA', 'Q-learning'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given converged Q values (Q*) SARSA method performs steadily better than the Q-learning method. Since the Q values have converged, and the policy we follow is deterministic same with the environment, every episode evolves in the same way. That is, the agent always follows the same path, and it is clear from the plot that SARSA just makes one less move. So only one iteration is enough to compare the two policies."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}