ArtStudies/M2/Reinforcement Learning/Lab 2 - Second maze.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44b75d44",
   "metadata": {},
   "source": [
    "# Lab 2 - Second maze\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "100d1e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "np.set_printoptions(\n",
    "    precision=3,\n",
    "    suppress=True,\n",
    ")  # (not mandatory) This line is for limiting floats to 3 decimal places, avoiding scientific notation (like 1.23e-04) for small numbers.\n",
    "\n",
    "# For reproducibility\n",
    "rng = np.random.default_rng(seed=42)  # This line creates a random number generator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1018deab",
   "metadata": {},
   "source": [
    "## 2. Maze definition and MDP formulation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4fa301-c14f-44ec-b04f-b01ca42d979a",
   "metadata": {},
   "source": [
    "### 2.1 Define the maze "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f91cda05",
   "metadata": {},
   "outputs": [],
   "source": [
    "maze_str = [\n",
    "    \"############\",\n",
    "    \"#S#.......X#\",\n",
    "    \"#.#.###.#.##\",\n",
    "    \"#.....#X#..#\",\n",
    "    \"#.###.####.#\",\n",
    "    \"#...#X#X...#\",\n",
    "    \"###.######X#\",\n",
    "    \"#.....X...##\",\n",
    "    \"#.###.#.#..#\",\n",
    "    \"#...#...X#.#\",\n",
    "    \"#X#.X#X##G.#\",\n",
    "    \"############\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "564cb757-eefe-4be6-9b6f-bb77ace42a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_rows = len(maze_str)\n",
    "n_cols = len(maze_str[0])\n",
    "\n",
    "figsize = (n_cols / 2 if n_cols > n_rows else 8, n_rows / 2 if n_rows > n_cols else 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc49d58-2730-41d8-96fb-ca7c9cb4fcdf",
   "metadata": {},
   "source": [
    "### 2.2 Map each walkable cell (not a wall '#') to a state index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7116044b-c134-43de-9f30-01ab62325300",
   "metadata": {},
   "outputs": [],
   "source": [
    "FREE = {\n",
    "    \".\",\n",
    "    \"S\",\n",
    "    \"G\",\n",
    "    \"X\",\n",
    "}  # The vector Free represents cells that the agent is allowed to move into."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9ad05e-9c6c-4e00-918c-44b858f45298",
   "metadata": {},
   "source": [
    "**Dictionaries to convert between grid and state index**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1258de4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states (non-wall cells): 62\n",
      "Start state: 0 at (1, 1)\n",
      "Goal states: [60] at (10, 9)\n",
      "Trap states: [8, 18, 27, 28, 33, 39, 54, 56, 58, 59] at (1, 10)\n"
     ]
    }
   ],
   "source": [
    "state_to_pos = {}  # s -> (i,j)\n",
    "pos_to_state = {}  # (i,j) -> s\n",
    "\n",
    "start_state = None  # will store the state index of start state\n",
    "goal_states = []  # will store the state index of goal state # We use a list in case there are multiple goals\n",
    "trap_states = []  # will store the state index of trap state # We use a list in case there are multiple traps\n",
    "\n",
    "s = 0\n",
    "for i in range(n_rows):  # i = row index\n",
    "    for j in range(n_cols):  # j = column index\n",
    "        cell = maze_str[i][j]  # cell = the character at that position (S, ., #, etc.)\n",
    "\n",
    "        if (\n",
    "            cell in FREE\n",
    "        ):  # FREE contains: free cells \".\", start cell \"S\", goal cell \"G\" and trap cell \"X\"\n",
    "            # Walls # are ignored, they are not MDP states.\n",
    "            state_to_pos[s] = (i, j)\n",
    "            pos_to_state[(i, j)] = s\n",
    "\n",
    "            if cell == \"S\":\n",
    "                start_state = s\n",
    "            elif cell == \"G\":\n",
    "                goal_states.append(s)\n",
    "            elif cell == \"X\":\n",
    "                trap_states.append(s)\n",
    "\n",
    "            s += 1\n",
    "\n",
    "n_states = s\n",
    "\n",
    "print(\"Number of states (non-wall cells):\", n_states)\n",
    "print(\"Start state:\", start_state, \"at\", state_to_pos[start_state])\n",
    "print(\"Goal states:\", goal_states, \"at\", state_to_pos[goal_states[0]])\n",
    "print(\"Trap states:\", trap_states, \"at\", state_to_pos[trap_states[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc61ceef-217c-47f4-8eba-0353369210db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_maze_with_states() -> None:\n",
    "    \"\"\"Plot the maze with state indices.\"\"\"\n",
    "    grid = np.ones(\n",
    "        (n_rows, n_cols),\n",
    "    )  # Start with a matrix of ones. Here 1 means “free cell”\n",
    "    for i in range(n_rows):\n",
    "        for j in range(n_cols):\n",
    "            if maze_str[i][j] == \"#\":\n",
    "                grid[i, j] = 0  # We replace walls (#) with 0\n",
    "\n",
    "    _fig, ax = plt.subplots(figsize=figsize)\n",
    "    ax.imshow(grid, cmap=\"gray\", alpha=0.7)\n",
    "\n",
    "    # Plot state indices\n",
    "    for (\n",
    "        s,\n",
    "        (i, j),\n",
    "    ) in state_to_pos.items():  # Calling .items() returns a list-like sequence of (key, value) pairs in the dictionary.\n",
    "        cell = maze_str[i][j]\n",
    "\n",
    "        if cell == \"S\":\n",
    "            label = f\"S\\n{s}\"\n",
    "            color = \"green\"\n",
    "        elif cell == \"G\":\n",
    "            label = f\"G\\n{s}\"\n",
    "            color = \"blue\"\n",
    "        elif cell == \"X\":\n",
    "            label = f\"X\\n{s}\"\n",
    "            color = \"red\"\n",
    "        else:\n",
    "            label = str(s)\n",
    "            color = \"black\"\n",
    "\n",
    "        ax.text(\n",
    "            j,\n",
    "            i,\n",
    "            label,  # Attention : matplotlib, text(x, y, ...) expects (column, row)\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            fontsize=10,\n",
    "            fontweight=\"bold\",\n",
    "            color=color,\n",
    "        )\n",
    "\n",
    "    ax.set_xticks([])  # remove numeric axes, we don't need.\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(\"Maze with state indices\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_maze_with_states()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db078d86",
   "metadata": {},
   "source": [
    "### 2.4 Actions and deterministic movement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f7f0b8e4-1f48-4d03-9e5f-a47e59c3e827",
   "metadata": {},
   "outputs": [],
   "source": [
    "A_UP, A_RIGHT, A_DOWN, A_LEFT = 0, 1, 2, 3\n",
    "ACTIONS = [A_UP, A_RIGHT, A_DOWN, A_LEFT]\n",
    "action_names = {A_UP: \"\\u2191\", A_RIGHT: \"\\u2192\", A_DOWN: \"\\u2193\", A_LEFT: \"\\u2190\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4b06da5e-bc63-48e5-a336-37bce952443d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def move_deterministic(i: int, j: int, a: int) -> tuple[int, int]:\n",
    "    \"\"\"Deterministic movement on the grid. If the movement hits a wall or boundary, the agent stays in place.\n",
    "\n",
    "    Args:\n",
    "        i (int): current row index\n",
    "        j (int): current column index\n",
    "        a (int): action to take (A_UP, A_DOWN, A_LEFT, A_RIGHT)\n",
    "\n",
    "    Returns:\n",
    "        (tuple[int, int]): new (row, column) position after taking action a\n",
    "\n",
    "    \"\"\"\n",
    "    candidate_i, candidate_j = (\n",
    "        i,\n",
    "        j,\n",
    "    )  # It means “Unless the action succeeds, the robot stays in place.”\n",
    "\n",
    "    # Now each action changes the coordinates of the robot:\n",
    "    if a == A_UP:\n",
    "        candidate_i, candidate_j = (\n",
    "            i - 1,\n",
    "            j,\n",
    "        )  # if the action is UP, then row becomes row -1\n",
    "    elif a == A_DOWN:\n",
    "        candidate_i, candidate_j = (\n",
    "            i + 1,\n",
    "            j,\n",
    "        )  # if the action is DOWN, then row becomes row +1\n",
    "    elif a == A_LEFT:\n",
    "        candidate_i, candidate_j = (\n",
    "            i,\n",
    "            j - 1,\n",
    "        )  # if the action is LEFT, then column becomes column -1\n",
    "    elif a == A_RIGHT:\n",
    "        candidate_i, candidate_j = (\n",
    "            i,\n",
    "            j + 1,\n",
    "        )  # if the action is RIGHT, then column becomes column +1\n",
    "\n",
    "    # Check boundaries\n",
    "    if not (0 <= candidate_i < n_rows and 0 <= candidate_j < n_cols):\n",
    "        # If the robot tries to move outside the maze\n",
    "        # It will not move and it stays at (i, j).\n",
    "        return i, j\n",
    "\n",
    "    # Check wall\n",
    "    if maze_str[candidate_i][candidate_j] == \"#\":\n",
    "        # If the next cell is a wall, the robot stays in place.\n",
    "        return i, j\n",
    "\n",
    "    return candidate_i, candidate_j  # Otherwise, return the new position"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e620e6",
   "metadata": {},
   "source": [
    "### 2.5 Transition probabilities and reward function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "610253e7-f3f7-4a30-be3e-2ec5a1e2ed04",
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 0.95\n",
    "p_error = 0.1  # probability of the error to a random other direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7a51f242-fe4e-4e74-8a1f-a8df32b194b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize transition matrices and reward vector\n",
    "P = np.zeros((len(ACTIONS), n_states, n_states))\n",
    "R = np.zeros(n_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "49d54d1f-dc29-45b6-ad31-ad0e848f920d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set rewards for each state\n",
    "step_penalty = -0.01\n",
    "goal_reward = 1.0\n",
    "trap_reward = -1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b9b7495a-c233-425c-99c0-5bddaf6c3225",
   "metadata": {},
   "outputs": [],
   "source": [
    "for s in range(n_states):\n",
    "    if s in goal_states:\n",
    "        R[s] = goal_reward\n",
    "    elif s in trap_states:\n",
    "        R[s] = trap_reward\n",
    "    else:\n",
    "        R[s] = step_penalty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eca4c571-39c7-468b-af86-0bab9489415e",
   "metadata": {},
   "outputs": [],
   "source": [
    "terminal_states = set(goal_states + trap_states)\n",
    "\n",
    "\n",
    "def is_terminal(s: int) -> bool:\n",
    "    \"\"\"Check if a state is terminal (goal or trap).\"\"\"\n",
    "    return s in terminal_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2d03276b-e206-4d1f-9024-f6948ca61523",
   "metadata": {},
   "outputs": [],
   "source": [
    "for s in range(n_states):  # We loop over all states s.\n",
    "    i, j = state_to_pos[\n",
    "        s\n",
    "    ]  # We recover the states to their coordinates (i, j) in the maze.\n",
    "\n",
    "    # First, in a goal or trap state,\n",
    "    # No matter which action you “choose”, you stay in the same state with probability 1.\n",
    "    # This makes the terminal states as the absorbing states.\n",
    "    if is_terminal(s):\n",
    "        # Terminal states: stay forever\n",
    "        for a in ACTIONS:\n",
    "            P[a, s, s] = goal_reward\n",
    "        continue\n",
    "\n",
    "    # If the state is non-terminal, we define the stochastic movement.\n",
    "    # For a given state s and intended action a,\n",
    "    # With probability 1 - p_error, the robot will move in direction a;\n",
    "    # With probability p_error, the robot will move in one of the other 3 directions, each with probability p_error / 3.\n",
    "    for a in ACTIONS:\n",
    "        # main action (intended action)\n",
    "        main_i, main_j = move_deterministic(i, j, a)\n",
    "        s_main = pos_to_state[\n",
    "            (main_i, main_j)\n",
    "        ]  # s_main is the state index of that next cell.\n",
    "        P[a, s, s_main] += (\n",
    "            1 - p_error\n",
    "        )  # We add probability 1 - p_error to P[a, s, s_main].\n",
    "\n",
    "        # error actions\n",
    "        other_actions = [\n",
    "            a2 for a2 in ACTIONS if a2 != a\n",
    "        ]  # other_actions = the 3 actions different from a.\n",
    "        for a2 in other_actions:  # for each of the error action,\n",
    "            error_i, error_j = move_deterministic(i, j, a2)\n",
    "            s_error = pos_to_state[(error_i, error_j)]  # get its state index s_error\n",
    "            P[a, s, s_error] += p_error / len(\n",
    "                other_actions,\n",
    "            )  # add p_error / 3 to P[a, s, s_error]\n",
    "# So for each (s,a), probabilities over all s_next sum to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "341fe630-8f87-4773-84ad-92d3516e53e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Action ↑: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "Action →: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "Action ↓: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "Action ←: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "for a in ACTIONS:\n",
    "    # For each action a:\n",
    "    # P[a] is a matrix of shape (n_states, n_states).\n",
    "    # P[a].sum(axis=1) sums over next states s_next, giving for each state s:\n",
    "    # We print these row sums.\n",
    "    # If everything is correct, they should be very close to 1.\n",
    "\n",
    "    probs = P[a].sum(axis=1)\n",
    "    print(f\"Action {action_names[a]}:\", probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d23991",
   "metadata": {},
   "source": [
    "## 3. Policy evaluation\n",
    "\n",
    "### 3.1 Bellman expectation equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2fffe0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def policy_evaluation(  # noqa: PLR0913\n",
    "    policy: np.ndarray,\n",
    "    P: np.ndarray,\n",
    "    R: np.ndarray,\n",
    "    gamma: float,\n",
    "    theta: float = 1e-6,\n",
    "    max_iter: int = 10_000,\n",
    ") -> np.ndarray:\n",
    "    \"\"\"Evaluate a deterministic policy for the given MDP.\n",
    "\n",
    "    Args:\n",
    "        policy: array of shape (n_states,), with values in {0,1,2,3}\n",
    "        P: array of shape (n_actions, n_states, n_states)\n",
    "        R: array of shape (n_states,)\n",
    "        gamma: discount factor\n",
    "        theta: convergence threshold\n",
    "        max_iter: maximum number of iterations\n",
    "\n",
    "    \"\"\"\n",
    "    n_states = len(R)  # get the number of states\n",
    "    V = np.zeros(n_states)  # initialize the value function\n",
    "\n",
    "    for _it in range(max_iter):  # Main iterative loop\n",
    "        V_new = np.zeros_like(\n",
    "            V,\n",
    "        )  # Create a new value vector and we will compute an updated value for each state.\n",
    "\n",
    "        # Now we update each state using the Bellman expectation equation\n",
    "        for s in range(n_states):\n",
    "            a = policy[s]  # Extract the action chosen by the policy in state\n",
    "            V_new[s] = R[s] + gamma * np.sum(P[a, s, :] * V)\n",
    "\n",
    "        delta = np.max(\n",
    "            np.abs(V_new - V),\n",
    "        )  # This measures how much the value function changed in this iteration:\n",
    "        # If delta is small, the values start to converge; otherwise, we need to keep iterating.\n",
    "        V = V_new  # Update V, i.e. Set the new values for the next iteration.\n",
    "\n",
    "        if delta < theta:  # Check convergence: When changes are tiny, we stop.\n",
    "            break\n",
    "\n",
    "    return V  # Return the final value function, this is our estimate for V^{pi}(s), s in the state set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c428327",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_values(V: np.ndarray, title=\"Value function\") -> None:\n",
    "    \"\"\"Plot the value function V on the maze as a heatmap.\"\"\"\n",
    "    grid_values = np.full(\n",
    "        (n_rows, n_cols),\n",
    "        np.nan,\n",
    "    )  # Initializes a grid the same size as the maze. Every cell starts as NaN.\n",
    "    for (\n",
    "        s,\n",
    "        (i, j),\n",
    "    ) in (\n",
    "        state_to_pos.items()\n",
    "    ):  # recall that state_to_pos maps each state index to its maze coordinates (i,j).\n",
    "        grid_values[i, j] = V[\n",
    "            s\n",
    "        ]  # For each reachable cell, we write the value V[s] in the grid.\n",
    "        # Walls # never get values, and they stay as NaN.\n",
    "\n",
    "    _fig, ax = plt.subplots(figsize=figsize)\n",
    "    im = ax.imshow(grid_values, cmap=\"magma\")\n",
    "    plt.colorbar(im, ax=ax)\n",
    "\n",
    "    # For each state:\n",
    "    # Place the text label at (column j, row i).\n",
    "    # Display value to two decimals.\n",
    "    # Use white text so it’s visible on the heatmap.\n",
    "    # Center the text inside each cell.\n",
    "\n",
    "    for s, (i, j) in state_to_pos.items():\n",
    "        ax.text(\n",
    "            j,\n",
    "            i,\n",
    "            f\"{V[s]:.2f}\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            color=\"white\",\n",
    "            fontsize=9,\n",
    "        )\n",
    "\n",
    "    # Remove axis ticks and set title\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1ab67f0-bd5e-4ffe-b655-aec030401b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_policy(policy: np.ndarray, title=\"Policy\") -> None:\n",
    "    \"\"\"Plot the given policy on the maze.\"\"\"\n",
    "    _fig, ax = plt.subplots(figsize=figsize)\n",
    "    # draw walls as dark cells\n",
    "    wall_grid = np.zeros((n_rows, n_cols))\n",
    "    for i in range(n_rows):\n",
    "        for j in range(n_cols):\n",
    "            if maze_str[i][j] == \"#\":\n",
    "                wall_grid[i, j] = 1\n",
    "    ax.imshow(wall_grid, cmap=\"Greys\", alpha=0.5)\n",
    "\n",
    "    for s, (i, j) in state_to_pos.items():\n",
    "        cell = maze_str[i][j]\n",
    "        if cell == \"#\":\n",
    "            continue\n",
    "\n",
    "        if s in goal_states:\n",
    "            ax.text(\n",
    "                j,\n",
    "                i,\n",
    "                \"G\",\n",
    "                ha=\"center\",\n",
    "                va=\"center\",\n",
    "                fontsize=14,\n",
    "                fontweight=\"bold\",\n",
    "                color=\"blue\",\n",
    "            )\n",
    "        elif s in trap_states:\n",
    "            ax.text(\n",
    "                j,\n",
    "                i,\n",
    "                \"X\",\n",
    "                ha=\"center\",\n",
    "                va=\"center\",\n",
    "                fontsize=14,\n",
    "                fontweight=\"bold\",\n",
    "                color=\"red\",\n",
    "            )\n",
    "        elif s == start_state:\n",
    "            ax.text(\n",
    "                j,\n",
    "                i,\n",
    "                \"S\",\n",
    "                ha=\"center\",\n",
    "                va=\"center\",\n",
    "                fontsize=14,\n",
    "                fontweight=\"bold\",\n",
    "                color=\"green\",\n",
    "            )\n",
    "        else:\n",
    "            a = policy[s]\n",
    "            ax.text(\n",
    "                j,\n",
    "                i,\n",
    "                action_names[a],\n",
    "                ha=\"center\",\n",
    "                va=\"center\",\n",
    "                fontsize=14,\n",
    "                color=\"black\",\n",
    "            )\n",
    "\n",
    "    ax.set_xticks(np.arange(-0.5, n_cols, 1))\n",
    "    ax.set_yticks(np.arange(-0.5, n_rows, 1))\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.grid(visible=True)\n",
    "    ax.set_title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813121a9",
   "metadata": {},
   "source": [
    "### 3.3 Evaluating a policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ceb5dfe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 3 2 1 1 3 0 2 0 0 2 3 2 3 2 3 2 0 3 1 2 1 0 3 3 2 1 3 2 1 1 0 0 2 3 0 3\n",
      " 3 1 2 0 3 2 1 0 3 1 3 2 3 3 0 1 1 1 0 2 0 2 2 3 2]\n"
     ]
    }
   ],
   "source": [
    "# Random policy: for each state, pick a random action\n",
    "random_policy = rng.integers(low=0, high=len(ACTIONS), size=n_states)\n",
    "\n",
    "print(random_policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8f3e2ac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value function under random policy:\n",
      "[ -0.2    -0.295  -0.534  -1.301  -1.394  -1.467  -0.827  -1.176 -20.\n",
      "  -0.2    -0.207  -6.086  -0.548  -0.2    -0.201  -0.204  -0.28   -0.481\n",
      " -20.     -0.546  -0.566  -0.2    -1.126  -0.588  -0.201  -0.203  -0.209\n",
      " -20.    -20.     -1.769  -1.186  -1.222  -0.229 -20.     -1.944  -0.862\n",
      "  -0.824  -1.381 -18.279 -20.     -7.557  -6.924  -0.44   -5.78  -17.248\n",
      "  -7.364  -0.214  -0.207 -18.427 -17.386 -16.41  -16.347 -17.519 -18.483\n",
      " -20.     -0.013 -20.    -15.666 -20.    -20.     20.      5.496]\n"
     ]
    }
   ],
   "source": [
    "V_random = policy_evaluation(policy=random_policy, P=P, R=R, gamma=gamma)\n",
    "print(\"Value function under random policy:\")\n",
    "print(V_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cf45291e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I51IEzSIYEmPQxGQFQbxG586dK4+L17DobhTlxOtVrGMm1jMrCLHMjHi9irFk4r0jXkdi2RuRxRTvGzGBQENMIhHPp/hbi+VWuOAtkXFhBo3yRROUidl14gMoIzE27eOPP5bBh/hQFTM3xey0/HS9iYHuIgshsh5icVXxQSM+oEUmKivxASQ+rEUQJ84pPqD3798vP4xF12dhiOBIrKF148YNOWZOzFgVGTTRLVRYok7RzVenTh1Zt3hcYsFfEchoiEWAxXpl4gfcRaZDfHDnRASrYmKG6LYSAaoISkTAIs6RcQaioYmB7yJYEN21Yl048fcRM0h1LZKaXyKYFY9LdBGKdclEwCPGU+mqU6yDtmTJEtl9JwIdsV6fOL/IGhkqGBGvQ5EFE38XEXSJMXniby26pTVEACzWJRNZWrFemgiuxSxMMQO3oL9sIWajiteCCLjEWD8R4IugVPwtsr5mxUxrEUQL4jVGVGxEdrc4NiOnEmttFHcjiIiMifglATHuUmRvC5O5NRQRCIqMtq5fuyDSN9ELIiamRJxaBDsbK8OeOyYe5Zp/JL/8F2YYiBKwi5OIqAQS3cwiky1+SYOoWBXHyv5pxj9JgAEaEVEJImaxikk2v/76q+zqLezPmhFR8WKARkRUgogffxeTEsTSJytWrJA/5E5UrJhBKxSOQSMiIiL9jUE79k3xjEFrNcGox6BxFicRERGRwrCLk4iIiPQnTZ2+GfqcpSFAE7+p9/DhQ9ja2ub7J2qIiIioeIlRTOLXLipXriwX0qYSFqCJ4Ky4fu+PiIiIno/4+brnWYD7uXCSgP4CNJE50/yBjXWwHRERUWkcqC8SLJrPcSphAZqmW1MEZwzQiIiIjAuHJxkfThIgIiIiPU8SMHQXpxrGjiMGiYiIiBSGGTQiIiLSH7EevqHXxFczg0ZERERERYwZNCIiItIfLrNRKMygERERESkMAzQiIiIihWEXJxEREel3wL6hl71Qc5IAERERERUxBmhERESk/0kCht4K4NChQ+jTp4/8UXnxqwubNm3KdHz06NFyf8ate/fu0CcGaERERFSqxcbGwtPTE0uXLs2xjAjIgoKCtNvatWv12iaOQSMiIqJSrUePHnLLjYWFBZydnQ3WJmbQiIiIqFR3cebHgQMH4OjoiLp16+Kdd95BWFgY9IkZNCIiIiqRoqKismXBxFZQonvz5Zdfhru7O27fvo0pU6bIjNuxY8dgamoKfWCARkRERPqTVgzLbKSln8/NzS3T7hkzZuDzzz8vcHVDhgzRXm/UqBEaN26MmjVryqxa586doQ8M0IiIiKhECgwMhJ2dnfZ2YbJnutSoUQMVK1bErVu3GKARERGREVKnpW+GPicgg7OMAVpRuX//vhyD5uLiAn1hgEZERESlWkxMjMyGafj7++P8+fMoX7683L744gu88sorchanGIP26aefolatWujWrZve2sQAjYiIiEq106dPo2PHjtrb48ePl5ejRo3Cjz/+iIsXL2LFihWIiIiQi9l27doVM2fOLLIuU10YoBEREVGJnCSQXx06dIA6l9/v9PHxgaFxHTQiIiIihWEGjYiIiPRHTwvH5srQ59MDZtCIiIiIFIYBGhEREZHCsIuTiIiISvUkASViBo2IiIhIYZhBIyIiIj1n0Aw9SUANY8cMGhEREZHCMINGRERE+sMxaIXCDBoRERGRwjBAIyIiIlIYdnESERGRHqUBakOv7J8GY8cMGhEREZHCMINGRERE+sNJAoXCDBoRERGRwjBAIyIiIlIYdnESERGR/rCLs1CYQSMiIiJSmBKVQdvWYiqMQe+Ts6F0xvJcpqpVULr4VH4PKirG8J3Y3MQ4pvcbw3tn8NmvYQzU6pTiboKyid/hNPhvcabB2PGTg4iIiEhhSlQGjYiIiBSGY9AKhRk0IiIiIoVhgEZERESkMOziJCIiIv1hF2ehMINGREREpDDMoBEREZH+cJmNQmEGjYiIiEhhGKARERERKQy7OImIiEh/1Or0zdDnNHLMoBEREREpDDNoREREpD9cZqNQmEEjIiIiUhhm0IiIiEh/mEErlFIZoDk0ropGE/uirFsFxASEwW/uZkT4Beos69imLmqO9IZtTWeoU1IRfu4uLi/cjoRHUdoyTu3rw+OD7rB0tEPktYe4MPtfxN4LRWlQkOfSrm5lNJ7SH9aVHaAyUSHa/xGuLd0tn1N9PpflG1dF44l9ULZqBcQGhOHCnC14kkMbrVzKoeuWCUiJS9TuCz3jjxPjV8nr9nVd4DU142N4jCtLdiMsw2MoDMuKNmg+tS/Ke1SGVSU77BryAyJuBOdY3qV1bXh+2BXWTnZQq9V4cjUI5xbsROStR9oyNV95AR6vt4NFOWs8On0XJ2duQkJojGLaaGZlDq9x3eDavh5MLc1w3/cqzszZjtSE5EK3MWs7rSvZYWce7cyo8ftd0OD1djg0fg0eHLgm97X/fgQqNamqLWNiagKTMqbY2GUekiLiCt3OCp5V4TWxD2yqVkDMvTCcm7MF4Tm8LoXq/ZqhzkhvWFa0RcLjKFz9xReBPhflsXJ1XdB0Wn+UrewAiNflnce4tGQ3Qp/jdSmex2ZT+mn/3ruHLs31ebSr6Qivcd3hUL8yLMqVxb/tZyM5JkF7XGVqAs+PusHtpYYwtTJH5K0QnJ+/A0+uPcx3m3r27ImJEz9Bo0YNkZycjEOHDuOjj8bjwYMH2jL9+vXF/Plz4erqirNnz+HNN9/G9evXc6wzr/IFrY+oqJS6Ls4ydlZovmAk7q4/Dp/Os3Bvw3G0WDASZjaWOsub2Vjg9p+Hsa/PPOzv/w2SYxPR9Ksh2uNlq1ZEky8H4fKiHfDpMhuhp++g+TfD5X9GJV1Bn8v44Cc4M3ENdr80W5a/s+o/Wd7Ewkxvz6VoY8uFI+C/4QR2dpotL19cMCLHNmr49JqP7e1nyk0TnAlxwRE4+ela7OzyFXZ0mo1bq/7DiwtHaB9DYanT1Ag6ehOHx6/NV/knN4Jw4L0V2Njha2zqMg8P/7uBtt8O1R53bO4Oz7Ev4cjE9fi381wkhMeg1exXFdVGr/HdYOPqgB2vfo+tvRbKIKDphB54bgVsp0a52k5w9a6LuMfPvnwJBz9Yib/bztZut/89g+ATd54rOBOvy9YLR+D2+hPY2nE2bm84IW+XyeF1Kb4YNJnUB+e+2owt7Wfi3NytaDZ9AGzdK2lfl8c/WYutnb+S9d1Y9R9aL3q+16X4ewcfu4kjH6/JX/mUNATuuYSTn2/UebzWoJao3K4e9o3+GZs7foXgozfRZuGwArXJ3t4Oc+fOh5tbdbi710JUVBTWr1+nPV6nTh2sXr0S48ZNQPnylbB/vy82b94IU1NTnfXlVb6g9REVpZIfRWTh3MFDfvsM2Hwaacmp8jIxLFru1+Whz0U8OnIdqfFJ8pu9/7ojcGjgpg0aXHt4IezMHTz67zrSklJw8zdfmDvYoLxXNZR0BX0ukyPjER8ckX5DpZIfAGZlLWBRwVZvz6XL0zbe25TeRnGZEB6Nyh11tzEv2R5Dapp8DJZPH0NhJYbH4taGUwi//CwTkBuRCdNmw1SQ7SjrUg4qs/TXZY2+TXB3x0WEX7ovX7cXvt+DSk2ro6yrg2LaWKVjfVxZfhjJ0Qky03Llt0Oo1tMTps8Z7CYUsJ2yeSYqtJjeH6fnbpevk5yYmJuhWo/GuLP57HO10bWjB+IfR+Hu09eluBTvnZxelyIzFvswAo/P+Mvbj0/dQXxIJOxqOMrbSZHxMkh79t5KQ5nnfF2Kv/ftDSfz/TxG3wuF/+azmbK4GdlUcUDIyduIC46U7/27W87C2tEO5uWs892mtWvXYceOHYiNjUVcXBwWLfoOLVu20AZMw4cPg6/vAWzfvh2JiYmYOXMWHB0d4e3trbO+vMoXtD7KgfrpLwkYclMb/y8JlLouTrtazoi6EZRpn7htV9s5X/ev0NQd0Xcfyw8bXfWJ/TH+j+T+sKf/mZZUhX0uu+2bJrs4TMxMEbj9LOIfPtHbc2lX2wmRWdoYeSMYdrWccr1fp3UfyEDiyeX7uPKdD2KydLP23D9V+xgCtp1D3NPHYEjWzvbo/te7MLO2EJ/JMsARWQzBvrYzbq47nunDNiEsBuVqOSH2wRNFtFGlUsktY5BkZllGdvlF3gyBIdUd1hoRN4Px+GzuXYJuHevLDN39/Vef63z2tZwQeT3z61J0H9rX1v26DDl2E/Vebw/HljXx6OQdOLWsiTK2Vgg9fy9TuT6+U2XXsXhd3ium12VO7mw6i+Yz+qNsFQcZpLn3fwGhFwKeKxPZvn07XL16Famp6UF148aNcP78Be3xlJQUXLlyVe4/cOBAtvvnVb6g9REVpVIXoJlamyMl+tm4CEF8gxcfIHmxq+OCuv/rgjOTn3WdmFmby/tnrc+0bN71ldbnUnRviq4Xl44NYGJRRq/PpZmVRbY6U6Ljc2yj+LA4OOpH+eEpArC6b3ZA66VjsH/wd0iJfTYuTXRvisdQuVMDmVUpDuJDbmP7r+Xz5t7HC3HBz7rmyliZI0nX36asuWLaKLo864/xxpOn45rEeDnZdgO/d0RWsfbgFvB5bVmeZWsMaAb/HReQlpJzli0/TK0tMo3PEpJzeV2KLGjAzgto/e1w+cVBZKDOfPkvEsMyjykU3ZvidenaqQFMi+l1mZPYB+GIuB6MXpvHy+dPZDoPf/Bnoevz8vLCzJlfYODAZ0NObGxsEBHxNJP4lLhta6s7k5hX+YLWRzngJIFCUdY7WA9cu3mi0eR+8rromgo9eVuO/8hIjEdKiojNtR7bmk5osXgULs3fKuvQSIlLyjaeqYyNBVIzfJiXFEX1XAppiSl4sOsC2q8bi5i7j/Hkwr0ieS6rdPeE5+S+8rro8nl88jbM7ayzt/GJ7jaKruyIK+ldOikxCbi8aJesU0w0eHTsZrbHcH/nBXT86wPE3A1F+IXM2YzciG6yF6b2SW9nUCR2DlyCwhLP2831pzBg/0TsHrZMdoUlxyfBXMdzmRKbpJg2nv1mJ5qM747u696V2dJrfx6B84s1kRRZsIyKaGfzDO3cUcB2tpjWF34/7ENSVHyu5cpWLgenF6rj7PwdKCi37p5oOuXp6zIoAo/Ee8c+8+tSjD9LzOG9IycIDG8L3zE/ycH1IgMnxqyJoC74yI1sr8vAnRfw0l8fIPpuKMLy+bqs2qMxmmnbGAmfQd+jKDWd1AdWFW2xpdtcJD6Jg2uH+ujw0xj4DF6KhNBonfd57bWh+OmnH+X1e/fuoWFDT3m9YcOG2LlzG95/fyz27t2rLR8TEwN7e/tMdYjb0dG668+rfEHrIypKJT5Ae+BzQW4abn2bwX1I62yZMf81R3INzl5c8jquLvWRQUVGUbeCYV/HRXtbjE2zcXdE1G3DdtEYy3OZlcrMVM4AFQFaUTyX93ddkJtG1b7NUHNoq0xlxDlur85/G5HHFzETU1PZLVeQAO3ezotyKyqip1BkTDRjlSJvBqNc3WddzRYOZeXsv4hbIYppo8jonfxiU6ZZn/GPoxF1N6xA9T5vO51b1oRDHWc0/Th9goL40tHqy5dxe/NZnPt2l7Zcjf7NEHb5ASJv6x5jlZvAXRfkljHgqpX1dVnXBTdzeF2KWZrBR2/Iv6sgLkNO3IJzmzrZArSM7y3xusxvgBaw86Lc9MWhrguuLj+sHZd4f99lNB7bFRU9q8rruqxZs1ZuGYngbO9eH0yaNAWrV2eewHDxoh+8vNKDOMHMzAweHvXh53dJZ/15lS9ofURFqdRNEgg+cAWWjvYyuBD/gYlL8cEVfED3fxA2NRxlcHZ92R7c35Z9YPCDnedR4YUacGxdR069r/16B5kByLh0RElV0OfSsW1d2NZykoGX6NqsNbo9rBzttM+VPp7LoKdtrPq0jeJSDJwW+3VxaFAFNtUryaUKRBenx/td5W+6hV8MkMed2taV49fEYzC1KIPao9vD0skOoXmMXcoP0VWq6S4Vj19ezzBGK6OqXRvCxq28PC4yL00+6YmUhGTtkgV3tpxD9Z6eKN/AFaaWZeTyEWJ81fOOPyvKNoqMlEX5svK6CCabfNwdl37aXyS/oVeQdm7q/g12Dv1Ru4kg8ey3u3Dp5wOZxsfV6OMlx1EVhQe+V2DlaC8DNfG6FJfidfnQV/frMuxiIJxerA3bp5MCxKXTi7UQ8XQcm3OW12XdMe1hVQSvy4I8j5rymq5VE/On5TWPwS8Q1Xt7yWVfRB2uHevL97/ICOaXh4eHDM6mTZuO5ctXZDu+atVqdOrUET169IC5uTmmTp2C0NBQHDp0SGd9eZUvaH2Ug7QM3ZwG22D0VGqxQFEexFRmkdaNjIyEnZ0dlGpbi6n5KufgWQ2NPk1fuys2MBR+cl2s9A9gSyd7dPjrQxwYvBgJIZHw/OxlVOnVJNvaTJrjgpi1WP/9bjIQiLz+EBdmbcx17a7eJ2dD6fTxXIrnUQRllpXs5CzNqFshuPnb/kwTAAr6XKaqc/6w0CjvWU2ug2Yj12oLxYW5W/DkYvp6U1ZO9ui0fiz2D/pOzopz7doY9d/pAosKNvJv/uRSIK4s3Y3op1kTt95NUGd0O/kYUp8+hhu/+sq10nISn5q/70FDzn6Zbd/+t37HozN3UalJNbT7fjj+aZv+2qk/2luuc2ZZvixS4pNkZsdv6b5M61SJ4w3ebA9zOytZx8kvn28dtKJuo2uHemg2sZdsX9yjKFxb8V+eAVB+Q7ehOtq5L0M7238/XC6ZoUufbeNk96tmHTTBpU1ttJk7CJu6zpfdtbkxN8nfJ0MFz2py6QzxuowOCE1fBy3D67LrhrHYPTD9dSnUHd1ODqwXQW1iZLycnHLlp33yWLXeTVB3TDu5VIl4XYqg59ovvtpZn4V97ww6MzPbPt+3f8PjM3dR0asavL8fgX+9Z8n91i7l0Hvbx9nKb+v9rezWFV3snuN6wKVtHTkZJDYoAld/OyiX5sjJ4LNfZ7r9+++/YtSokXIGZ0YeHo0QGJj+3PXv3w/z5s1BlSpV5Lplb7zxlnbdsrZt28quUVvbctr75lY+P8cFtToFSlWcn9+ac0f88D/YWRl2/GtUfBLKvfuT4uOW3JTKAK24laQArbjl50OmuOU3QKO8GcOw3/wGaMXNGN47WQM0pWKAlvu5I5a8XTwB2vs/Kz5uyQ0/OYiIiIgUhgEaERERkcKU+FmcREREVHzEun1iM/Q5jR0zaEREREQKwwwaERER6Y+Yi1gEy+cUiKHPpwfMoBEREREpDDNoREREpD/8Lc5CYQaNiIiISGEYoBEREREpDLs4iYiISH/YxVkozKARERERKQwzaERERKQ/zKAVCjNoRERERArDAI2IiIhIYdjFSURERPrDLs5CYQaNiIiISGGYQSMiIiK9UavVUBs4o6UuAb/FyQCNjFq/U7OKuwlkQJNqfAmlm3NnOozB5ubToHRqdUpxN4Go2DBAIyIiIv3hGLRC4Rg0IiIiIoVhgEZERESkMOziJCIiIv1hF2ehMINGREREpDAM0IiIiEj/GTRDbwVw6NAh9OnTB5UrV4ZKpcKmTZuyLdsxffp0uLi4wMrKCl26dMHNmzehTwzQiIiIqFSLjY2Fp6cnli5dqvP4vHnz8N1332HZsmU4ceIEypYti27duiEhIUFvbeIYNCIiIirVevToITddRPZs0aJFmDZtGvr16yf3/fnnn3BycpKZtiFDhuilTcygERERkf6IVf2LYysi/v7+CA4Olt2aGvb29mjZsiWOHTsGfWEGjYiIiEqkqKioTLctLCzkVhAiOBNExiwjcVtzTB+YQSMiIiK9UacVzya4ubnJbJdm+/rrr2EsmEEjIiKiEikwMBB2dnba2wXNngnOzs7yMiQkRM7i1BC3vby8oC/MoBEREVGJXGbDzs4u01aYAM3d3V0Gafv27cvUdSpmc7Zq1Qr6wgwaERERlWoxMTG4detWpokB58+fR/ny5VG1alV89NFHmDVrFmrXri0Dts8++0yumda/f3+9talUBmgOjaui0cS+KOtWATEBYfCbuxkRfoE6yzq2qYuaI71hW9MZ6pRUhJ+7i8sLtyPh0bOBh07t68Pjg+6wdLRD5LWHuDD7X8TeC0VpUJTPpW0NR9T/qCfK1asM83JlsavTTKTE6G+NGSq5LG0t0GvKS/B4qR5My5gi9G4Yfhq8HMkJKTrLe7xUFz0nd4Gdkx0eXg7CP5O24vGdMHnMxFSFLh91QNMBjWFpa4nbR/2xcdo2xIbFoaQr37gqGk/sg7JVKyA2IAwX5mzBkxze31Yu5dB1ywSkxCVq94We8ceJ8avydZyoOJ0+fRodO3bU3h4/fry8HDVqFJYvX45PP/1UrpX29ttvIyIiAm3btsWuXbtgaWmptzaVugCtjJ0Vmi8YiWvf78L9HedQpWcTtFgwEvsHfKszGDCzscDtPw8j7Ky/nLbbYEIfNP1qCI6++bM8XrZqRTT5chDOTl2H0JO3UWt0ezT/ZjgODvkO6tSnoxRLqKJ+LtNS0hC01w931x+T9RAVhkoFjP5tKIKvP8I3nZcgISoBLvWdkZqi+/1Y0b0ChiwcgDVj/8Gt/+6g47veGPnzYCzs9iPSUtVo93Zr1OtYGz+88jviIuIxYGZPDF4wAL+PWo2S/v5uuXAErnzvg8Dt5+DWqwleXDACewYsyPWLk0+v+c91nEogI/gtzg4dOsj1znIifl3gyy+/lJuhlLoxaM4dPJDwOAoBm08jLTlVXiaGRcv9ujz0uYhHR64jNT4JqQnJ8F93BA4N3KAyTX/qXHt4IezMHTz67zrSklJw8zdfmDvYoLxXNZR0Rf1cxgaEInDLGUTfDjHwI6GSpE6H2ihX2R5bPt+J+MgEuRzSwyvB8guALk36N8Lt43dxbf9NpCSlYt+SQ7CpUBbVm6e/hxt0rYejK04iKiQaKYkp2LPoIOp414SDqz1KMpen7+97m9Lf3+IyITwalTvqfn8TUdEqdRk0u1rOiLoRlGmfuG1XO32WRl4qNHVH9N3H2uxY1vrE/hj/R3J/2Bl/lGRF/VwSFYUaLaoh7G44Bi0YgNptayDmcQwO/nwUZzde1FnepZ4Tgq48+1IgArmQm6FwqeeIO8fvQmWiAlTPysvb4gtKPSc8eRCJksquthMis7y/I28Ew65W5rWgsuq07gOozEzw5PJ9XPnOBzFZhnvkdZxKnozLXhjynMau1GXQTK3NkRKdOb2eHJ0AM+u8Z3bY1XFB3f91wZWF27X7zKzN5f2z1mdatuAzRUr7c0lUFKzLWaJma3fcOx2Ir15cgI1Tt6Pf5z3g3ryqzvLmZc0RH5X5dZwQnQCLp+/ha7430WZ0S9hXtoO5dRm8NK4D0tLUsLAp2e9xMyuLbP+3pUTH5/j+ToqIw8FRP2JPv2+x79XFiA0MQ+ulY2D29HnM6zgRlbIMmms3TzSanP7bWfHBEXKcmBhbkZGZjSWSImJzrce2phNaLB6FS/O3yjo0UuKS5P0zKmNjgdTYZwNhSwp9P5dEheHVryEGzOotr0c8iMDNI3cQ8TASx1aekvvunQnE5T3XUa9THfifCsh2/6TYJDmpICNxO/Hpe/jAD//JYO3//hoNUzNTHP71GDy61JXj0UqSKt094Tm5r7weFxyBxydvw9zOOvv7+4nu97cYuhBx5YG8LsaYXV60S9YpJho8OnYzz+NEVMoCtAc+F+Sm4da3GdyHtM6WzfFfcyTXgOLFJa/j6lIfPNj1rC4h6lYw7Os8W7hOjKeycXdEVAkcR6Xv55KoMM5vviQ3jWaveqJht/r5vn/QtRBU9njWLW9iZgLHWpXkJANBjEvbPnu33IRKNSui64ROCDx/HyXJ/V0X5KZRtW8z1ByaeY0n8X/d7dU5v7+zyWuctoHHjVMxURfDJAG18b+4Sl0XZ/CBK7B0tJfBhcrMVF5aVrRF8IHLOsvb1HCUAcX1ZXtwf9vZbMcf7DyPCi/UgGPrOjApY4rar3dAUmScXEKipCvq51IwMTeDSZn07w0m5qbyNlFBXPa5BjMLM7R8rZkcL+bm6SozXlf2XddZ/twmP9RsVR11O9SCqbkpOr3njdgncfA/eU8et61kIycdCBWql8erc/rgv9+OywkIJVnQ0/d31afvb3FpWcFW7tfFoUEV2FSvBJioYGplDo/3u8oPyfCLAfk6TkSZlbpPv+SoeJz6eCUafdoXDSf0QWxgKE6NX6kda2HpZI8Of32IA4MXIyEkEjWHtYW5gzU8xvWUm4bmuJh5eH7GBjQY30v+ZxZ5/aGsvzQMfC/q51Ksk9R58yfa/V13TZGX+/rNR3xQRDE8QjJGCdGJWP7GWvT7sodcCy0yOAqbZ+yQY9KE6s2rYszvr2FGoznydqh/GP4a/y/6TO8Ge2c7PLgUjD/fWieX2BBEcDZk0QDYOtoiNjwWJ9aeld2epeH9LdYoE+ugNf6kN2ICQnH842fvbysne3RaPxb7B32H+JBIWLuWR/13usCigo2cpf3kUiCOfrAcKU+7ivM6TiWY+Dg09EdiGoyeSp3bwh8ZftJA/MhoZGRkpt+0UpptLabCGPQ+ORtKx+eSlGhSDcOtQVRYc+5MhzHY3HwalK7fqVnF3QSjV5yf35pzh346BHYW5oY9d2ISKs5bp/i4JTelLoNGREREhqNOU8vN0Oc0dqVuDBoRERGR0jFAIyIiIlIYdnESERGR/nCSQKEwg0ZERESkMMygERERkf6I8fqGHrOvhtFjBo2IiIhIYRigERERESkMuziJiIhIb7gOWuEwg0ZERESkMMygERERkf5wmY1CYQaNiIiISGGYQSMiIiK9Uaelb4Y+p7FjBo2IiIhIYRigERERESkMuziJiIhIfzhJoFAYoBWDbS2mFncTSgxjeC5T1SooXb9Ts2AM5vp/CaWbg+nF3YQSY3PzaTAGxvL+IePCAI2IiIj0hpMECodj0IiIiIgUhgEaERERkcKwi5OIiIj0R/wspqG7HNUwesygERERESkMM2hERESkN2p1+mbocxo7ZtCIiIiIFIYZNCIiItIbLrNROMygERERESkMAzQiIiIihWEXJxEREekPf4uzUJhBIyIiIlIYZtCIiIhIbzhJoHCYQSMiIiJSmFKZQXNoXBWNJvZFWbcKiAkIg9/czYjwC9RZ1rFNXdQc6Q3bms5Qp6Qi/NxdXF64HQmPorRlnNrXh8cH3WHpaIfIaw9xYfa/iL0XWiraWRrbWGtMB1Tt/wLK2Foh7kE4ri7xQeiJW3he5RtXReOJfVC2agXEBoThwpwteJJDOzOqNuAFeE3pD78F23Fn7THt/jpj2qPagOYoY2uJ2AfhuPL9bjwugnYqSc+ePTFx4ido1KghkpOTcejQYXz00Xg8ePBAW6Zfv76YP38uXF1dcfbsObz55tu4fv16jnXmVb6g9RmzgrwmrVzKoeuWCUiJS9TuCz3jjxPjV+XruBLaWHt0e9QZ0+7ZHVQqmFmZ4+SnaxDke+W52klUUKUug1bGzgrNF4zE3fXH4dN5Fu5tOI4WC0bCzMZSZ3kzGwvc/vMw9vWZh/39v0FybCKafjVEe7xs1Ypo8uUgXF60Az5dZiP09B00/2Y4VKYmJb6dpbGNIoCsMawtTo1fCZ9OM3FnzRG8MG+YPM/zEPdvuXAE/DecwM5Os+XliwtG5NhODcuKtqg1vC0ibwZn2u/cvj5qDm+L4+NWYkfHWbi9+ihazH/tudupNPb2dpg7dz7c3KrD3b0WoqKisH79Ou3xOnXqYPXqlRg3bgLKl6+E/ft9sXnzRpiamuqsL6/yBa3PmBX2NenTaz62t58pN13BV17Hi7ONN5cf1O4X29kZfyM5Oh4hR288VztLO80vCRh6M3alLkBz7uCBhMdRCNh8GmnJqfIyMSxa7tfloc9FPDpyHanxSUhNSIb/uiNwaOCmDRpce3gh7MwdPPrvOtKSUnDzN1+YO9igvFe1Et/O0thGa9fyiLxyH9G3Q+TtBzvPw8TMRO5/Hi5P23lvU3o7xWVCeDQqd9TdTo3Gn/bBjd8OIDkqPtP+sq4OiLjyQNvO+0/bKfaXJGvXrsOOHTsQGxuLuLg4LFr0HVq2bKENmIYPHwZf3wPYvn07EhMTMXPmLDg6OsLb21tnfXmVL2h9xqywr8mS1MZq/Zrhvs9FpCWmFEl9RAVR6gI0u1rOiLoRlGmfuG1X2zlf96/Q1B3Rdx9DnZqmsz6xP8b/kdxf0ttZGtsYtMcPFhVsYVfHBTBRoUrvpoh/FKUNhArdztpOiMzSzsgbwbCr5ZTjfVw6NZAZv8Ad57MdeyDbaQP7p+2s2ie9nVG3H6Eka9++Ha5evYrU1FR5u3HjRjh//oL2eEpKCq5cuSr365JX+YLWZ8wK85oUOq37AN12TUSLb4fBplrFAh9XQhsFMczC8cXauLf5zHO1kcSSF6ri2YxcqRuDZmptjpTohEz7kqMTYGZtked9xYdy3f91wZnJa7X7zKzN5f2z1mda1qLEt7M0tjExPEZm2LxXvAu1Wi2zbKcnrpYZv+dhZmWR7bGnRMfn2E4xrqzB2O449sFynccTw2MRcuQ62v/5jradYhzN87ZTyby8vDBz5hcYOPBZl7SNjQ0iIiIylRO3bW1tddaRV/mC1mfMCvqaTIqIw8FRPyLyehBMrcxR980OaL10DPYP/g4psYl5HldCGzMSX2oibwXLsbBExaHEB2iu3TzRaHI/eT0+OAKhJ29nG4cjxiskRcTmWo9tTSe0WDwKl+ZvlXVopMQlZRvvUMbGAqkF/A/HGNrJNgK13+wExzZ14PvqQsQ9fIIKTaqj2ZyhOP7eH4i6mfmbfG6qdPeE5+S+8npccAQen7wNczvr7O18orudIjgL2HIGsYFhOo/XfasjnFrXwd5XFmnb2XzuEBwV7byRebyaMXnttaH46acf5fV79+6hYUNPeb1hw4bYuXMb3n9/LPbu3astHxMTA3t7+0x1iNvR0dE668+rfEHrMybP+5oUwwJEt7qQEpOAy4t2yTrFIP5Hx27meVwJbcwaoN1efSRf7SLShxIfoD3wuSA3Dbe+zeA+pHW2TIn/miO5fli/uOR1XF3qgwe7ntUlRN0KTu9GekqMVbJxd0RUAbu8jKGdbCNgX9cFD/ddkrM3hbCz/oi6GYyKLWoWKEC7v+uC3DSq9m2GmkNbZT5XHZccPyAqtagJs7IWqPH0PmVsLFGufmVU8KqOUxPXyvs+3Hc5WzvF/Yw5QFuzZq3cMhLB2d69Ppg0aQpWr16T6djFi37w8koP4gQzMzN4eNSHn98lnfXnVb6g9RmT531N6pTXQG21Mtso3ieWFWwRuDPz+58Kh+ugFU6pG4MWfOAKLB3t5Qe3ysxUXoqZcMEHLussb1PDUX5YX1+2B/e3nc12XAwSr/BCDTi2rgOTMqao/XoHJEXGyeUZSno7S2MbxfR9l04NYeVcTruERzmPKtnGuRVU0NN2Vn3aTnEpPiDEfl0Ovf4TfF9bggPDlspNZAVurfwPF77arG1n5c4NtO0UGQIH2U7jDc508fDwkMHZtGnTsXz5imzHV61ajU6dOqJHjx4wNzfH1KlTEBoaikOHDumsL6/yBa3PmBX0NenQoApsqleSYx5F96HH+13lVLrwiwH5Oq6ENmqIeh4euCKzbETFRaUWA1TyIKauizR+ZGQk7OzsoFTbWkzNVzkHz2po9Gn6ulixgaHwk+vmpL9BLZ3s0eGvD3Fg8GIkhETC87OXUaVXEzmGJyPNcUHMCKz/fjf5H0Xk9Ye4MGtj0ayDZgTtLG1tFFm9eu93Q+UuDVHGxgoJYdG4u/6YXMYjJ6nq/A1WLe9ZTa7nZCPXawvFhblb8ORi+npOVk726LR+LPYP+g7xT5+rjNosewNBB69o10ET7fT4oCtcuzSS2bWE0GjcWX8c/jm0s9+pWTAGKlXmpP/vv/+KUaNGyhmcGXl4NEJgYPpz179/P8ybNwdVqlSR65a98cZb2nXL2rZtK7tGbW3TA9m8yufnuFptHOP8NjefVqSvSdeujVH/nS5ycop4/zy5FIgrS3cj+unElLyOF1ZRtlEQQyK67fgUxz/8U66Rlh9Kfv8U5+e35twBQ4fAztzcsOdOSkLVtesUH7fkplQGaESGlN8ArTgp+QMmtwBNiUpSgEbG//5hgBap6LglN8r/346IiIiMFsegFU6pG4NGREREpHQM0IiIiIgUhl2cREREpDfytzEN3cWphtFjBo2IiIhIYZhBIyIiIr1Rq1VyM/Q5jR0zaEREREQKwwCNiIiISGHYxUlERET6k6aCOs3AXY5p7OIkIiIioiLGDBoRERHpd5kNAy97oeYyG0RERERU1JhBIyIiIr3hMhuFwwwaERERkcIwQCMiIiJSGHZxEhERkd6oi2GZDXUJWGaDAVox6H1ydnE3gQxofbPPirsJJYZanVLcTSgx+p2aBaXb3HxacTeBqNiwi5OIiIj0vsyGobeC+Pzzz6FSqTJt9erVQ3FiBo2IiIhKvQYNGmDv3r3a22ZmxRsiMUAjIiKiUs/MzAzOzs5QCnZxEhERkd7XQTP0VlA3b95E5cqVUaNGDQwbNgwBAQEoTsygERERUYkUFRWV6baFhYXcsmrZsiWWL1+OunXrIigoCF988QW8vb1x6dIl2NraojgwQCMiIiK9SUtTyc3Q5xTc3NyQ0YwZM+SEgKx69Oihvd64cWMZsFWrVg3r16/HG2+8geLAAI2IiIhKpMDAQNjZ2Wlv68qe6VKuXDnUqVMHt27dQnHhGDQiIiIqkcts2NnZZdryG6DFxMTg9u3bcHFxQXFhgEZERESl2oQJE3Dw4EHcvXsXR48exYABA2BqaoqhQ4cWW5vYxUlERESl2v3792UwFhYWhkqVKqFt27Y4fvy4vF5cGKARERGR3hR22YvnUdDzrVu3DkrDLk4iIiIihWEGjYiIiEp1Bk2JmEEjIiIiUhgGaEREREQKUyq7OB0aV0WjiX1R1q0CYgLC4Dd3MyL8AnWWdWxTFzVHesO2pjPUKakIP3cXlxduR8KjZz8f4dS+Pjw+6A5LRztEXnuIC7P/Rey9UAM+IjJ2lhVt0GxKP5T3qAyrSnbYPXQpIm4E51jerqYjvMZ1h0P9yrAoVxb/tp+N5JgE7fHqfZqg3uh2sl7xun189h7Of7sDccGRBnpERIZRvnFVNJ7YB2WrVkBsQBguzNmCJzn8f27lUg5dt0xASlyidl/oGX+cGL9Ke7vG0FaoMbgVLBzK4snlB7jw1SbE3g83yGMpqdLUKrkZ+pzGrtRl0MrYWaH5gpG4u/44fDrPwr0Nx9FiwUiY2VjqLG9mY4Hbfx7Gvj7zsL//N0iOTUTTr4Zoj5etWhFNvhyEy4t2wKfLbISevoPm3wyHyrTUPbX0HNRpagQfu4kjH6/JX/mUNATuuYSTn2/UefzRqTvY/8Yv2NR+Nrb2+AYx98PRfPqAIm41UfH/f95y4Qj4bziBnZ1my8sXF4zI8f9zDZ9e87G9/Uy5ZQzOXLs2Rq1hbXH8wz+xo9NshF8MQMsFwwET4/+wJ+NT6qII5w4eSHgchYDNp5GWnCovE8Oi5X5dHvpcxKMj15Ean4TUhGT4rzsChwZu2gDMtYcXws7cwaP/riMtKQU3f/OFuYMNyntVM/AjI2OWGB6L2xtOIvzyg3yVj74XCv/NZxF565HO4yJTlhQRJ6+rxGdLmho2VSsUaZuJipvL0//P721K//9cXCaER6NyR49C1lcfAVvPIuZeKNSpabj+y36UrVIeFbyqF3nbSxN1mqpYNmNX6ro47Wo5I+pGUKZ94rZdbed83b9CU3dE330s37y66hP7Y/wfyf1hZ/yLuPVE+VfRqyraLhoOc1srpKWk4uycbcXdJKIiZVfbCZFZ/j+PvBEMu1pOud6v07oPoDIzwZPL93HlOx8ZkAkqkSnL+rmuUsG+thPCzvL/czKsUhegmVqbIyX62VgdITk6AWbWef8+l10dF9T9XxecmbxWu8/M2lzeP2t9pmXz93tfRPoSej4Amzp8BfNy1qjRvxmi/HVn24iMlZmVRbb/f1Oi43P8/1xklQ+O+hGR14NgamWOum92QOulY7B/8HdIiU1EyJEbqPe/zniw2w+xgWHyugjazMrm3mVKucv425iGPKexK/EBmms3TzSa3E9ejw+OQOjJ23LcQkZivEJSRGyu9djWdEKLxaNwaf5WWYdGSlxStvEOZWwskBr7bBAqUVZVezRGsyl95fW4oEj4DPpeb+cSH0r+m86gx6aPsLX7fNlVT2SMqnT3hOfkp++b4Ag8Pnkb5nbW2f8/f6L7/3MxVCXiSvowgpSYBFxetEvWKSYaPDp2U3ZvWla0RYtvhqGMjSUCtp1FtP9jJEWmDxcgMqQSH6A98LkgNw23vs3gPqR1tsyY/5ojuQZnLy55HVeX+uDBrmd1CVG3gmFf59mv3YuxaTbujoi6HVKkj4NKloCdF+VmKCozU9nVaVm+LGIfRhjsvERF6f6uC3LTqNq3GWoObZWpjPj/+PbqnP8/zyZLpuXGHwflJpSxt4L7wJYIO3f3OVtOVHClbpJA8IErsHS0l4Ga+NASl+IbU/CByzrL29RwlMHZ9WV7cH/b2WzHH+w8jwov1IBj6zowKWOK2q93kN+2xHIcRAVhYm4mN3m9jGn6dTnCP+fyppry5k/LZ1hmw8rRTl63rGCDJp/0QtTdUAZnVKIEPf3/vOrT/8/FpWUFW7lfF4cGVWBTvZKclSm6OD3e7yr7wsRsTU32zaZaRXldfC40+exlBB+4iug7HB7wPNKQvsyGQTdwkoDRSY6Kx6mPV6LRp33RcEIfxAaG4tT4ldpxDJZO9ujw14c4MHgxEkIiUXNYW5g7WMNjXE+5aWiOxwaE4vyMDWgwvpf8jyLy+kNZv2YSAVF+vXpshvZ6lz//T176vv0bHp+5i4pe1eD9/Qj86z1L7rd2KYfe2z7Wlu+3Z5K83Nb7W8QFRaBcXRc0fLcLzG0t5fpooo7DY/80+GMi0vf/52KZDLEOWuNPeiMmIBTHP372/7mVkz06rR+L/YO+Q3xIJKxdy6P+O11gUcFGdvU/uRSIox8sl+PPhDK2lmgx7zW5XppYK01k664u3VPMj5JKK5VanfdQuqioKNjb2yMyMhJ2dunfypVoW4upMAa9T84u7iaQAa1v9hmUbtCZmcXdBKJsNjefBmPQ71T6FyclKs7Pb825z3Z+E7Zm5gY9d3RKEpru+1XxcUtuSl0XJxEREZHSMUAjIiIiUphSNwaNiIiIDEddDL/FqeZvcRIRERFRUWMGjYiIiPSazTJ0RkvNDBoRERERFTVm0IiIiEhvxKqghl4ZNA3Gjxk0IiIiIoVhgEZERESkMOziJCIiIr3hJIHCYQaNiIiISGGYQSMiIiK9SVOLTWXwcxo7ZtCIiIiIFIYBGhEREZHCsIuTiIiI9IaTBAqHGTQiIiIihWEGrRhsazG1uJtQYvQ+ORtKN/js11C6QZgJY6BSKf+/rJVexvH+NjcxhrXWjT8LQppJAoY/p7FjBo2IiIhIYRigERERESmM8vsLiIiIyGhxkkDhMINGREREpDDMoBEREZHepEElN0Of09gxg0ZERESkMMygERERkd6o1emboc9p7JhBIyIiIlIYBmhERERECsMuTiIiItKbNLVKboY+p7FjBo2IiIhIYZhBIyIiIr1RF8MyG2ous0FERERERY0BGhEREZHClMouTofGVdFoYl+UdauAmIAw+M3djAi/QJ1lHdvURc2R3rCt6Qx1SirCz93F5YXbkfAoSlvGqX19eHzQHZaOdoi89hAXZv+L2HuhpaKdRdlG2xqOqP9RT5SrVxnm5cpiV6eZSIlJQEnUs2dPTJz4CRo1aojk5GQcOnQYH300Hg8ePNCW6devL+bPnwtXV1ecPXsOb775Nq5fv55jnXmVL2h9xsBYn0erijZoOa0PyntUhnUlO2wf/COe3AjOsXyLqb3h3rOx9rZKpYKZlTl2DF2G8GtBeR4vDMuKNmg2pZ9so1UlO+weuhQRubTRrqYjvMZ1h0P9yrAoVxb/tp+N5AzvX9Eez4+6oXKH+jC1MMMD36s4O3cbUhOSC9W+wrazWm8v1BrYErbVK8pzB/13AxcW7tK2te6INqjWywtlXcohOTYRgbv94LdkL9JSUp+rnaUZ10ErnFKXQStjZ4XmC0bi7vrj8Ok8C/c2HEeLBSNhZmOps7yZjQVu/3kY+/rMw/7+38g3bNOvhmiPl61aEU2+HITLi3bAp8tshJ6+g+bfDIfK1KTEt7Oo25iWkoagvX44/+U/KOns7e0wd+58uLlVh7t7LURFRWH9+nXa43Xq1MHq1SsxbtwElC9fCfv3+2Lz5o0wNTXVWV9e5Qtan7Ew1udRnabGwyO3cHDcs7bm5uTsbfirzVfa7cKPvoi6G6oNvvI6XhiijcHHbuLIx2vyVz4lDYF7LuHk5xt1Hvcc1x1lq5THrle/w/beC2BVyRZeH/codPsK204zyzK4+J0Ptrw0Fz4Dv4dVRVs0ndxHe1xlYoJTX27Cps5fY9/on1GpmTsa/K/jc7eTqKBKXYDm3MEDCY+jELD5NNKSU+VlYli03K/LQ5+LeHTkOlLjk+S3Lf91R+DQwE0b2Lj28ELYmTt49N91pCWl4OZvvjB3sEF5r2olvp1F3cbYgFAEbjmD6NshKOnWrl2HHTt2IDY2FnFxcVi06Du0bNlC+0E/fPgw+PoewPbt25GYmIiZM2fB0dER3t7eOuvLq3xB6zMWxvo8JoTH4saGUwi7/CzTVxA1+zfBrc3nCn08PxLDY3F7w0mE57ON0fdC4b/5LCJvPdJ53LVjfVxbfgjJ0QkyW3X190Oo1tNLZtMM2c7bf5/C4zN35f+DSVHxuP3PKVT0qqo9fm3FYTy58kAGnPGPonBv+3lUfM7/z0s7zTIbht6MXakL0OxqOSPqRuZvleK2XW3nfN2/QlN3RN99DHVqms76xP4Y/0dyf0lvZ1G3sTRr374drl69itTU9G6Uxo0b4fz5C9rjKSkpuHLlqtyvS17lC1qfsSoNz2PFxlVg51YBd7acK9Tx4qIyER+YGT40TVQym2XjVqE4m4VKzaoj8mbOXworNa2OiJs5d5kS6UupG4Nmam2OlOjM45rENzoza4s872tXxwV1/9cFZyav1e4zszaX989an2lZixLfzqJuY2nl5eWFmTO/wMCBz7p7bWxsEBERkamcuG1ra6uzjrzKF7Q+Y1RansdaA5rh/uEbMgtXmOPFRYz1qj+mnTbYEdeFMjbP93/l83BuXRvu/ZvB941fdR6vMaAZKnhWxZ5hPxi8bSWJWPLC0MteqEvAMhslPkBz7eaJRpP7yevxwREIPXlbjp3KSIyZSorI/T8z25pOaLF4FC7N3yrr0EiJS8o25kr8h5Mam1ji2qnvNpZkr702FD/99KO8fu/ePTRs6CmvN2zYEDt3bsP774/F3r17teVjYmJgb2+fqQ5xOzo6Wmf9eZUvaH1KZazPY/UejeSkACE2KBLbXl2KwhAD7au91AD/Tf67UMdzU7VHYzSb0ldejwuKhM+g71GUzn2zQ04i6Lr2PZk1v77yPzi/WAuJEfHF0k7H5u5oOfNVHJ2wFpG3QnSep+E7XXDw3eVICI0p1DmInkeJD9Ae+FyQm4Zb32ZwH9I6WzbHf82RXAOKF5e8jqtLffBg17O6hKhbwbCv46K9LcZT2bg7IqqA46iMoZ36bmNJtmbNWrllJIKKvXt9MGnSFKxenXmA88WLfvDySg8+BDMzM3h41Ief3yWd9edVvqD1KZWxPo93d/rJ7XlV795QTq55eORmoY7nJmDnRbnpi8iui8H3GbNX8aHRcuyaodspgrNWc4fg+NQNeHTqjs7gzOvjnjj03gqdwRuRIZS6MWjBB67A0tFeBhcqM1N5aVnRFsEHLussb1PDUQYU15ftwf1tZ7Mdf7DzPCq8UAOOrevApIwpar/eAUmRcXIJiZLezqJuo2BibgaTMunfG0zMTeXtksjDw0MGFdOmTcfy5SuyHV+1ajU6deqIHj16wNzcHFOnTkFoaCgOHTqks768yhe0PmNhzM+jfK0/fX2L96S8rsq9W6Zm/6a4veW8nLlYmOP6bqM4bmqu+/1btnI5WJQvK6+Xq+siZ3BeXra/SNZDKEg7xZiz1vOG4uT0fxBy7Fa2427dGqHJhF44/MGfiLhe+Fmw9Ix4ORbHZuxUanXe7w4xdV2k8SMjI2FnZwel2tZiar7KOXhWQ6NP09fuig0Mhd+cLXjiFyCPWTrZo8NfH+LA4MVICImE52cvo0qvJtnW6tEcF8Ssxfrvd5PBSuT1h7gwa2PRrINmBO0syjZauZRD582fZDvHvn7zER+UedyPRu+Ts6F0KlX2IPP333/FqFEj5czDjDw8GiEwMH0duf79+2HevDmoUqWKXG/rjTfe0q631bZtW9mlZ2tbTnvf3MrndVytToExyPpcKu15FFZ65e//oeHnvsi2b8+bfyDkzF1UalIVnZYMl0tmaNjXqITeG97F5r7fIebBk2z3zet4VuYmeU/OGXRmZrZ9vm//JmdBipmN3t+PwL/es+R+a5dy6L3t42zlt/X+FnFBEXL9s6af9oK5vRXiQ6Jw7c//4L/pDIpCQdrZ4acxqNikOlITM/8/pDnec8t4WDvZITXp2Xsir65UXedXiuL8/Nace2fLsShrZtixhrEpiehx4jvFxy25KZUBGpUcxhqgKY2xBmhKlN8ArbjlJ0Cj/GGAlvu5t7f4sFgCtF4nFys+bslNqeviJCIiIlI6BmhERERECqP8/gIiIiIyWlwHrXCYQSMiIiJSGGbQiIiISG+KY9mLtBKwzAYzaEREREQKwwwaERER6Q3HoBUOM2hERERECsMAjYiIiEhh2MVJREREesNJAoXDDBoRERGRwjCDRkRERHqTplbJzdDnNHbMoBEREVGpt3TpUlSvXh2WlpZo2bIlTp48WaztYYBGREREpdpff/2F8ePHY8aMGTh79iw8PT3RrVs3PHr0qNjaxACNiIiI9EZdTFtBLFiwAG+99RbGjBkDDw8PLFu2DNbW1vj9999RXBigERERUYkUFRWVaUtMTMxWJikpCWfOnEGXLl20+0xMTOTtY8eOobiUqEkCvU/OLu4mlBjbWkwt7iaUGGp1SnE3ocTgc1m6qFTG8RE1CDOLuwmKJlb1N/SgffXTXxJwc3PLtF90YX7++eeZ9oWGhiI1NRVOTk6Z9ovb165dQ3Exjlc/ERERUQEFBgbCzs5Oe9vCwgLGggEaERER6U3a083Q5xREcJYxQNOlYsWKMDU1RUhISKb94razszOKC8egERERUallbm6OZs2aYd++fdp9aWlp8narVq2KrV3MoBEREVGpNn78eIwaNQovvPACWrRogUWLFiE2NlbO6iwuDNCIiIhIb9RqldwMfc6CGDx4MB4/fozp06cjODgYXl5e2LVrV7aJA4bEAI2IiIhKvffff19uSsEAjYiIiErkJAFjxkkCRERERArDAI2IiIhIYdjFSURERHqTpk7fDH1OY8cMGhEREZHCMINGREREev1dTM1vYxrynMaOGTQiIiIihWEGjYiIiPSGY9AKhxk0ei4Ojaui3er30ePQDHiveh/lGrnlWNaxTV20+ulNdN07DS/tmoxmXw+FpeOzH7Gt1KoO2q35QB7vumcqWn4/BrY1i28VZyJSlp49e+LgQV+Ehz9GSMhDbNjwF1xdXTOV6devL27cuIrY2CgcPnwQdevWzbXOvMoXtD6iosIAjQqtjJ0Vmi8Yibvrj8On8yzc23AcLRaMhJmNpc7yZjYWuP3nYezrMw/7+3+D5NhENP1qiPZ41I0gnPhgOXZ3mYU93b/GoyPX8cK8YQZ8RESkZPb2dpg7dz7c3KrD3b0WoqKisH79Ou3xOnXqYPXqlRg3bgLKl6+E/ft9sXnzRpiamuqsL6/yBa2PqCgxQKNCc+7ggYTHUQjYfBppyanyMjEsWu7X5aHPRRl0pcYnITUhGf7rjsChgRtUpukvQ3FfsWmo09Jg5VJOe5yISre1a9dhx44d8kes4+LisGjRd2jZsoU2YBo+fBh8fQ9g+/btSExMxMyZs+Do6Ahvb2+d9eVVvqD1Ue6TBAy9GTt+8lGh2dVyllmvjMRtu9rO+bp/habuiL77GOrUZz/KYelkj277pqHnf1+gwfheuLXiUKbjREQa7du3w9WrV5GamipvN27cCOfPX9AeT0lJwZUrV+V+XfIqX9D6iIoSJwlQoZlamyMlOiHTvuToBJhZW+R5X7s6Lqj7vy44M3ltpv0JIZGyu1TU7darKeJDIou83URk/Ly8vDBz5hcYOPDZMAkbGxtERERkKidu29ra6qwjr/IFrY904ySBwmEGjfLNtZsnuh+YLrf268YiNS5JjivLSIw/S4lLzLUeMfC/xeJRuDR/K0JP3tZZRtR99+8T8PzsZVhVdijSx0FExuG114YiOjpCbpcuPctkNWzYEDt3bsP774/F3r17tftjYmJgb2+fqQ5xOzr62dCJjPIqX9D6iIoSM2iUbw98LshNw61vM7gPaZ0tM+a/5kiuwdmLS17H1aU+eLDrWV06qQBTCzNYu5RD/MMnz/8AiMiorFmzVm4ZieBs714fTJo0BatXr8l07OJFP3h5eWpvm5mZwcOjPvz8LumsP6/yBa2PqCgxg0aFFnzgCiwd7WWgpjIzlZeWFW0RfOCyzvI2NRxlcHZ92R7c33Y22/HKLzWCdZXygEolM3ENx/dGSnwyIq89NMCjISKl8/DwkMHZtGnTsXz5imzHV61ajU6dOqJHjx4wNzfH1KlTEBoaikOHDumsL6/yBa2Pcu/iNPRm7JhBo0JLjorHqY9XotGnfdFwQh/EBobi1PiVchyaZsB/h78+xIHBi+XYsprD2sLcwRoe43rKTUNz3MrFAfXe6wpzBxs50zPiyn2ceP8PpMTm3mVKRKXDhAnjUalSJSxc+K3cNDw8GiEwMBA3btzA8OEjsXjxAlSpUgVnz55D374DtJMI2rZtK7tGbW3Lydt5lc/rOJE+qdRqdZ5xplhrRvS7R0ZGws7u2cKiVHJtazEVxqD3ydnF3QQi0hOVyjhyCGp1CpSqOD+/Nef+pfFEWJvmPXmsKMWlJuKti3ONOm5hFycRERGRwhjH1xMiIiIySupiGBOmLgFj0JhBIyIiIlIYBmhERERECsMuTiIiItIb8WN9hv7BvjQYP2bQiIiIiBSGGTQiIiLSG7VaJTdDn9PYMYNGREREpDAM0IiIiIgUhl2cREREpDecJFA4zKARERERKQwzaERERKQ3acXwSwJp/CUBIiIiIipqDNCIiIiIFIZdnERERKQ3orfR0D2Oahg/ZtCIiIiIFKZEZdD+9JoBY1DWLBXKZxyrMK9rOh1KN9LvZyhdckoojMH/uX4GpTM3NY73TmenRCidWp1S3E2gIpskYNj3RVoJSKExg0ZERESkMCUqg0ZERETKwjFohcMMGhEREZHCMEAjIiIiUhh2cRIREZHe8JcECocZNCIiIiKFYQaNiIiI9Cbt6Wbocxo7ZtCIiIiIFIYBGhEREZHCsIuTiIiI9EatTt8MfU5jxwwaERERkcIwg0ZERER6o4YKaQb+fWe1kfyedG6YQSMiIiJSGGbQiIiISG84Bq1wmEEjIiIiUphSmUGzqmiDF6f1QQWPyrB2tMPWwT/iyfXgHMurTE3g+U5H1OztiTI2Fgg+6Y/jM7ci4UlstrLeX78C9x6N86wzPyp4VoXXxD6wqVoBMffCcG7OFoT7BeZYvnq/Zqgz0huWFW2R8DgKV3/xRaDPRe3xMjaWaPRRd1TuUB8mZqaICQjDwbd+RWpisiLa6NymDuqMagf7Wk5IS0lF6Lm7uPjtDsQ/isLzsKxog+ZT+6K8R2VYVbLDriE/IOJGzn8bl9a14flhV1g72UGtVuPJ1SCcW7ATkbceaV8PXuO6oepLDWBqaY7IWyE4+80OWS6/nJ0d8cMP89G0mScqV3ZG8xc648KFy5nKTJr0Id54cwQcHOxx585dTJk8C3v3HtRZ34gRg/B/74xBnTo1ERcXj1279uHTTz5HZGT6c2dqaoq582bg1Vf7omxZa1y6dBXjx3+Gc2efvT6y6tmzJyZO/ASNGjVEcnIyDh06jI8+Go8HDx5oy/Tr1xfz58+Fq6srzp49hzfffBvXr1/Psc68yhe0vqy6v98W3T7w1t5WqQALa3P89NZfOL/zWrby5avYY/bxj5AQm6Tdd+OoP34cs05728rOAq981hWe3erBtIwpHt0Jw7ev/IHkhBQ8D1Fvv6kvoVHXejAT9fqH4buBy3XWa2KqQo/xHdD85cawsrXEjSP++GvKNsSExcnjHd9uhRYvN0b5KuWQEJOIs1svY9u8fUhNfr7lOss3ropGE/uirFsFxAaE4eLczXiSy/tbo1r/5vCc0h+XFmzHnXVHsx2v985LqDOmA05+sgrBB68+VxuJSqpSmUFTp6nx8Ogt+I5/9p9wbhqMaoMq3rWxY+Qv2NB5PpJiEtD2q5ezlXP1rg3L8jZF0sYydlZovXAEbq8/ga0dZ+P2hhPytgiydLGv64Imk/rg3FebsaX9TJybuxXNpg+ArXul9AIqFVovGgF1Sip8Xl6ELR1n48zsTTIQUkobxf1urDiEHb3mY1ffb5ESm4iWXw9BUfy9g47exOHxa/NV/smNIBx4bwU2dvgam7rMw8P/bqDtt0O1x2sPbglX77rYM/oX/NvxawQdvQXvhcMK1Ka0tDT47PbFq6+O0Xm8b9/uGDf+HQzoPxwVK9TG4kU/YcPff8DBoZzO8lbWVpg8aSaquDaCl2d7ODs74fvv52iPv/ve6+jVqyvaefeGk2M97PbxxcaNK3Jto729HebOnQ83t+pwd6+FqKgorF//7D1Tp04drF69EuPGTUD58pWwf78vNm/eKINBXfIqX9D6dNm15D+Mq/u1dlvx0SbERSbgsu+tXO83pfkC7X0yBmciwHt3+Wsy0JnR7nt87DEHqz7ditSU5wt8RL1v/z5U1ju7wxJMajQX6yZty7HeTv9rDY+OtbGw/++Y1uxbxEcnYMSiAdrjJiYqrP10KyZ7zceC/r+h1ovV0OOjDs/VRvH+brFgJPzXH8euzrPgv+E4Wi4YCbMc3t8aFhVtUXN4W0Td1P0lyK62M5y968kvaFS6fknA0JuxK5UBWkJ4LK6vP4WwS88yAbmp2qkerq05gfhH0UhNTMGFH31RuVUtlK387MPSzNoczSd0x/HZW4ukja4dPRD/OAp3N51GWnKqvEwMi0bljh46y5et7IDYhxF4fMZf3n586g7iQyJhV8NR3nZuXRvWzvY4P387kqPiZQd95PUgqFPTFNNGkUkLPnIDqfFJSE1Ixs01R1G+YRWZsXoeieGxuLXhFMIv5+/vnRAaIzdJBfkclXUpB5VZejtsXB0QcuoO4oIiZfDnv+WszMSal7POd5sePQrFT8uW4/SpczqPu9eohtOnz+PSpfSsz+rVf6NMGTO5X5eff1qBQ4eOIjExEU+eROCXn1egdZsWz+pzrwZf38MICLgvg8MVK9bB1dUFFSqUz7GNa9euw44dOxAbG4u4uDgsWvQdWrZsoQ2Yhg8fBl/fA9i+fbs878yZs+Do6Ahv72cZrIzyKl/Q+vKj9ZAmOL35UqGzXQ061kZ5V3v89dkOxEUkyHEt9y8HI+05A7T6HWvDobI9/pmxUwaQot4HudTbuFs9HFp+EpEh0UhOTMHOhQdRr11NmQEU9i07ioCLD+X9I4Ojceqfi3Bv7vZcbXTp4CGDqIDN6e9vcZkQFi3356bxp31x43dfJEWlZ/cyMVHBc+oA+M3fKuskopyVygCtUF93xaa9mX7dobaTdl/TsV1we9tFRAeEF8kpRTefCKAyEt1y9hnOmVHIsZtIiUuEY8uasq1OL9ZCGVsrhJ6/J49XbOaOmMBwNP/yVfTeOwUv/fUBqvZqoqg2ZlWpmTui7j5+riCysEQw+/LByRh4bDqaftIDV/84DPXTD887m87AoX5l2FRxkEFbjQHNEHohAEkROj6QCmnD+s2yG9TLqyFMTEwwctQQ3L8fhMtPA7a8eLdrDT+/Z11Hy/9Yg6ZNG6NGjWowMzPD668Pw7FjpxAWlv/Xa/v27XD16lWkpqZ/sDZu3Ajnz1/QHk9JScGVK1flfl3yKl/Q+vJSzsUWHu1r4sjas3mW/Wzfu5hz9mO88/sQONWsoN1fu1U1PL4bjtGLB2C+3yf4bN87ePFVTzyvWi2r4fG9cAxfOABfnZ+ASXv+D81faZxjeZWJKuN/QfK2ULme7veayKA9vBbyXG20q+WMqBuZ39/itsiA5cSlUwOYlbXA/R3ndR6vObQNom4FI+zc3edqGxmXNHXxbMauVI5BK6gH/91E/ddaIuj4bSRGxsPz3U5Qp6WhTFkLebySpxucXqiO7UN+KrJzmlpbIDkmIdO+5Oh4mFmnnzMrkXEK2HkBrb8dLoMGkdk58+W/SAxLzwSZ21nBsXkNnJ+3Fadm/IPyDVzR5rtRiHv4RI71UkIbs3aHevxfZ5yYlL9u6KIWFxyJje2/lplR9z5eiAt+1h0T8+AJIq4HofeWcbKLWGRkD36wskjPLzJsO3fsxbHjPnIcXGxsHAYNfF1mlvLSrVsnvP76a+jYoa9235079+QYt2vXT8jAJyTkMfr2eS3f7fHy8sLMmV9g4MBnXc42NjaIiIjIVE7ctrW11VlHXuULWl9eWg1qggdXQxDgl/PYwNjwOMzp/QsCLwXDwroMen7YHmPXjsDMTj8gISYJZctZoW4bd6ybtgMrxm1CNU9XfLBqGEIDn+DWiQAUlnU5S9Rp7Y6/p+/E6o83oaqnK/5vxWsID4zA7ZPZ672y/ybaj2mJ64fvIDYiHj3Hd0BamhqWttnfa62GNIF7MzfM7/UznoeptTmSo7O+vxNyfH+XsbWEx9juOP7Bcp3HrSs7wH3gizg4culztYuotCgVAZp7z0ZyUoAQGxSJLa8U7D8Iv98Oo4y1Obr9/jpMzExwZeUxJHeoi8TIODnY/sXP+uDE7G3PNZ7Lrbsnmk5J/0CNC4rAo5O3UcY+c5eZGKOVGJF9YoJ28P3wtvAd85MctC6yW2I8mAiYRLdhSlySDDrEeDEh7EIAHh68CmfvuvkO0PTdRg27mk5ou3gkzs/bhkcnbqOgqvVojBempv+9RTfkzoFLUFjiebu5/hQG7J+I3cOWyS7aFyb3lpMcNnWdh8QncXDtUA+dlo3GzsE/ICE0Wmc9Q4e+jKU/zJfXA+7dh5dX+1zPO23aeHTv0RkNPFrD3z8A3t6tsO6vX9Cj+6Bskwky6tChDZavWCKDOU33qPD9kjlyXFpVt8Z4/DgM/fr1gM/uv9GsaScEBaVnWl57bSh++ulHef3evXto2DA9U9SwYUPs3LkN778/Fnv37tXWGRMTA3v79C42DXE7Olr3c5BX+YLWJzQf0Aivzektr4ffj8DMzuntF1oP8sLeX44hN4lxybh3/qG8Hh+ViH9m7pZ11njBDVcO3EZibBLCH0bi4PJTssyd04G44HMNjbrUKVCA1qx/Qwz+6mk7H0TIQOvJw0gcXpFer//pQPjtvo4GnevoDND2/PAfLGwsMHbDaDlRwfeXY2j0Ul3EPonPdp5eEzrih+GrEPUo+xef3Lh284Tn5H7yelxwBELF+9vOKt/vb4+xPRCw5QxiA8N0HheTBq4t25M+xIKI8lQqAjT/HX5yK6y0pBSc/tZHboK9e0U0eb8TQv0ewMrRFvbuldBhQebB7F1/GY2LPx/E1VW5f0BoBO66ILeMwUytoa2yZZVurj6i8/7l6rog+OgNRD4dmCsuQ07ckjMjRfAjbrt2alDgx27INmqCM+8fxuDSkt0I3PnsXAVxb+dFuRUV0bVkam6mHUMnHsfV5Ye149Tu77sCr7FdUdHTTV7XZe3ajXLLL0+vRvjn760y8yWI8WUXL15Bp07tcgzQRHC27q9fMWL4O/D1/S/TMS/PRpg//3sEB6fPRN24cRtmfzUNrVo1l9eFNWvWyi0jEZzt3euDSZOmYPXqNZmOXbzoBy+vZ919ouvUw6M+/Pwu6WxfXuULWp9w6l8/uWVVr6077JxscHLjxedaQOn+lRB49ayP53Vm0yW5abQc6AnPHvmvNyUxFZtm7pab4FSrInpN6IR75+5nCs5ent4NP4xYjYfX0v/OBfHA54LcNKr2bYYaQ1pnKmNXxwW31+h+f1dqXlN2b2ruI4K5cvVdUd6rGk5PWotKLWrJ+zcY30seN7e1QpMZryJg6xlcXrijwO0l4yHeUYbucVTD+JXaMWgm5mZyk9fLmKZfzzjII8uyHGVd0r/Z21Ytj1af98OVVceQFBWf3hXWYyG2DV6m3YRDEzfg5sYzhW7fA98rsHK0l0GQysxUXlpWsMVDX90BQNjFQDi9WBu2Twfci0sxxkt0xQnifqYWZnB/pbkcqOvQoAoqt6uHoIPXFNNGcVsEZ5d/3It7W/MeN6Svv3fVrg1h41ZeHhcfMk0+6YmUhGQ8ufZQ+zjce3nBQkwKUKng2rE+rJzsZFawICwsLOQmlDE3l9c14xtPHD+Nl1/pjapVq8jbrVo3R/PmTXDhgu5gpV271vhr/W8YM/oD7NlzINvx4ydOY9jwgahYsYI8h8igVaniIpfbyImHh4cMzqZNm47ly7PP+Fy1ajU6deqIHj16wNzcHFOnTkFoaCgOHTqks768yhe0vrwmB5zfeVVmxXJTvYkrnGtVlGO6RBdn/yldZHx250x64HN+11WUsTCD9/Bmsowo37hrPVzcnf+lP3S56HNN1ttmWHq91bxcZUbs0h7d9do52sDBNf3/oErVy2PovD448OtxOcFAaNq3AV75vDuWjVojJxsUhaADV2DpaC8DNfH+Fpcicxx8QPcXhMNvLMOBYd/j4PAlcou4+gC3Vv2Hi19vlsd3956rPSa2hNAoXF60Azd+3V8k7SUqaVRqMcAlD2J6vehqiIyMhJ2dHZTqT68Z+S478vwX2fb5vPkHQk7fhWOTqui8dDjWtv5K7q/Y0BXeX78qA7WEJ3G48c9pXPrtcK5157YOWlmz/HWFVvCsJpelsHGrgOiA0PQ1xi6mr0Fk5WSPrhvGYvfA7+RMSKHu6HZw7/8CLMqXlWPlAradw5Wf9mnrc2jgCq9P+8hZk/GPInHtt4MIyGEwb34VZRubTX8Z1Xp7ybFqGWW8f1bJafn7jjHk7JfZ9u1/63c8OnMXlZpUQ7vvh+OftrPl/vqjvVHzlRdgWb4sUuKTEHb5AfyW7tOunSbWwmsyvjtc2tSBmVUZxAZF4PJvhxC4W3fwNNJP91igpOTsr48unV+W2TKRPRIZrldf7YNy5ewRHBSCpT/8jh+W/ibLtWnTElu3rUF5h5rpz9Gef+Dt/SLi4zOPGdIct7OzxfxvvkD37p3lOmj37gVizteLsWFD+odnckpotrb8/vuvGDVqpJzBmZGHRyMEBqb/jfv374d58+agSpUqct2yN954S7tuWdu2bWXXqK3ts9nOuZXPz/H/c/0M+RnfNef0x1gycjVuHM3cfe9Q2Q7Tfd/Dlx2X4snDKLzQryH6ftJRBkBJ8cm4e+4BNn29Fw+vP9bep5pXZQyZ1RMudSohIigKO747jJP/5JyZMzfN328AVvWsjIEze8Cpdnq9u78/jNNPs4E1mleVY9I+9UhfKkUEcCO/GwA7R1vEhMXi6Jqz2LP0WZZ0+n8foJyzHVKSnv3fIrpS57yU/oVRl85OeY9nLO9ZTc7KFOugxQSG4uKcLXjiF6B9f3f860P4Dl6s8/3Z+sc35BpnutZBE7psmoBLC7fnug5a35Pp70kqvOL8/Nac+4vak2FpmvvyLEUtITUBM25+rfi4JTelNkArTvkN0Chv+Q3QilNOAZqS6ArQlCg/AVpxy2+AVtzyE6AVNwZoz48BWqSi4xaU9jFoREREVDz4W5yFo/z0AxEREVEpwwCNiIiISGHYxUlERER6Uxy/jZkG48cMGhEREZHCMINGREREelMcv42ZxkkCRERERKVH9erV5YLfGbc5c9LXLCxKzKARERERFcCXX36Jt956S3vb1tYWRY0BGhEREelNSfwtTltbWzg7O+v1HOziJCIiohIpKioq05aYWDS/oCG6NCtUqIAmTZpg/vz5SElJQVFjBo2IiIhK5CQBNze3TPtnzJiBzz///LnqHjt2LJo2bYry5cvj6NGjmDx5MoKCgrBgwQIUJQZoREREVCIFBgZm+i1OCwsLneUmTZqEuXPn5lrX1atXUa9ePYwfP167r3HjxjA3N8f//vc/fP311znWXxgM0IiIiEhv1FDJzdDnFERwlp8fS//4448xevToXMvUqFFD5/6WLVvKLs67d++ibt26KCoM0IiIiKhUq1SpktwK4/z58zAxMYGjo2ORtokBGhEREVE+HDt2DCdOnEDHjh3lTE5xe9y4cRg+fDgcHBxQlBigERERkd6I8fqGniSg1lO9YozZunXr5EQDMSPU3d1dBmgZx6UVlRIVoF2PNoUxmH37i+JuAhnQm1ZroXRJycEwBu+6Kf+9s/jedBiDbS2mQumMoY1C75Ozi7sJZCBi9ubx48cNcq4SFaARERGRsvC3OAuHC9USERERKQwDNCIiIiKFYRcnERER6U1J/C1OQ2AGjYiIiEhhmEEjIiIiveEkgcJhBo2IiIhIYZhBIyIiIr1RP/1n6HMaO2bQiIiIiBSGARoRERGRwrCLk4iIiPSGkwQKhxk0IiIiIoVhBo2IiIj0hgvVFg4zaEREREQKU+oyaOVc7fHJoQ+RGJuk3ed//C5Wvr1OZ/n277SVm4ZKBZhbm2P1O+txZfc1ue+FQU3g/XZr2FayQVRINPZ/fwgXt14ywKMhY+XkXAnffT8TTZs1hIuLE1q17Au/i1e1xxd/9yUGD+2rvW1iYgJrayu0adUPF85fyVbfa8MG4O3/G4batWsgPi4ePj4HMWXS14iMjJbHhw1/GT8s+wrx8Qna+8z5eikWLfgFJU2399ui2/vemd6zFtbm+Pmtv3B+V/p7NiO3hs4YNrcPKlR1gEqlQvDNx9g0Zy9unQjQlinnbItXZ3RDPe+a8vbd8/exZPhqlHQOjaui0cS+KOtWATEBYfCbuxkRfoE6yzq2qYuaI71hW9MZ6pRUhJ+7i8sLtyPhUZQ8blvDEfU/6oly9SrDvFxZ7Oo0EykxCaWqnUQFUeoCNI15bRYiIToxz3IHf/xPbhoNutXDgDl9cePgLXnbxcMZfb7oieVjVstAr0Zrd4z8dSiCrgbj8a1QvT4GMl7qtDTs3XMI8+b+gIOH/8l2/MOx0+Wm8cGHr2PM64N1BmeCCN4+mzIPJ0+el9d//f0bLFz8BV4fPV5b5vKlG2j94rOgr6TyWfKf3DS8etTH8Pl9cflA+ns2q/D7kfj57fUIfxCZXr57Pby7/DVMbPINkhNSYG5VBh+tH4Xjf1/AyglbkJyQDLeGLijpythZofmCkbj2/S7c33EOVXo2QYsFI7F/wLc6AxYzGwvc/vMwws76A2o1Gkzog6ZfDcHRN3+Wx9NS0hC01w931x+T9ZS2dpZmnCRQOOziLKBmg5rI7FhKYoq87VClHCIeRMjgTLhz1B+RQZFwrFWpmFtKSvboURh++XkNzpy+mK/yo0YNxMo/swdyGr/+sgaHD59EYmISnjyJxG+/rkWr1s2KsMXGq/WQJji95ZIMtnSJjYjXBmci25aWpoaljQXsKtnIfS8O8kJMeBx2fXdYZt7TUtW4d+EhSjrnDh5IeByFgM2nkZacKi8Tw6Llfl0e+lzEoyPXkRqfhNSEZPivOwKHBm5QmaZ/zMQGhCJwyxlE3w4ple0kKqhSm0Ebu/MdmJiZ4P6FB9g1dy9C74TleR87Z1vU9q6JH1/+Tbvv5uHb6PBeW9RsUwN3jt5BzbY1YWVriXunn3WPED2PFi29ULNWNaxemXOAllVb7xa45Hc9077addzhf+84YuPisMfnED6f8a22C7SkEl2THu1rYm6fX/Ms+82libAoaw5TMxMc33AeYYERcn/tltUQERSF9/58DdWbVEFYwBNs/cYXl311Z+RKCrtazoi6EZRpn7htV9s5X/ev0NQd0XcfQ52aBn0ylnaWZmp1+mbocxq7UhegxT2Jw48DfsXDK8Gy66Lj++0w5s/h+K77j0iMeTYuTZdmr3oh+FoIHl569p9Bcnwyzm/yw4ifB8PEzFS+yTdO2oKY0FgDPBoqDUaNHoRdO31l1i0/XuraTt6na+ch2n1H/juFli/0gr9/IKpWdcWSH2bhp1/mYcigd1CStRrcBA+uhiDQL/MHuC4TGs5FGUszNOlRH2YWz/5rLFvOCnVaV8cv/1uPH19fh0ada+OtnwZhdtcf8fjuE5RUptbmSInO3EWYHJ0AM2uLPO9rV8cFdf/XBWcmr4W+GUs7iQqqxAdonn0bot+s3vK66Ir8rscy3L+Y3j0hxqDt/HoPPPs1QtWmbrh56HaudTV9xQtHfj+eaV+zgV7wfrMVlr3yO0Kuh8CprhNG/DoECVGJuH7gph4fGRmTQUP64rvvv5TXAwMeonmznvm6X9my1nj5lR4YM+rZWLLctG//ohx/NmzIe7h8+YZ2/927zwZM37t3H598PAsnTm+DlZVlpokDxqh5/0YYOif9PR5+PwKzuvyoPdZqoBf2/XIs33WJbtCT//ph2t53EHI7FLdPBSIxLgl3zgTigk96RlJcBvg9RP12NfH47mmUFK7dPNFocj95PT44AqEnb8vxXRmZ2VgiKSL3L5+2NZ3QYvEoXJq/VdZRWttJz4jcpKHzk2kwfiU+QLuw5ZLcnjcXWrO1O2wdbXB+U+YxQ5U9nOWEAZFZE8TlrcN3UKd9TQZopLV+3Ra5FdSrA3shOjoGu30O5is4W7nme4wZNQ4HDuQelKSlpf/3JWYtGrtTm/zkllXdtu6wc7LByX/zN84vI9MypqjkXkEGaPevhKBum+oo6R74XJCbhlvfZnAf0jpbxsl/zZFcg54Xl7yOq0t98GDXhVLdTqLnVeomCVTxdEWlmhWhMlHB3LoMun3aWcZnAWfv5zk54LLPtWwzPwPO3UetdjXhWDt9UoC4FOPURBcqUW4sLMzlJpibl5HXswZMI0cPxOqVG7UBVU68vVtg1doleOuNT7Bv77MZjBpdu7WXS3sIlV2dMe+badiz+zDi4uJRkicHnN95FfFRuc/Wbti5NlzrOcLEVCW7OMUyHQ4udrh14p48fuKfC3LWpign/jziUty+crBkZ12CD1yBpaO9DIBUZqby0rKiLYIPXNZZ3qaGowx6ri/bg/vbzuosY2JuBpMy6XkBE3NTebu0tJOooErdq6581XLoMq6jXLNMjB8LvPAAf4xehcSY9P/E7V3s8KHPu1jc7QdEBqWvi2NlbwmPrvWw4vU12eoT2Tn7yvYY8fMQlK1QFnERcTjz93mc2XDe4I+NjEtYxLMPEM1SGz26DpOzMYV69WqheXNPvDH642z3bd3mBWzc9CucK3nJ25OnfgA7OxusWLkoUznN8XbtX8SPy76GrZ0NwsMjsGuHL774fAFKKutylvDqVh9LR2Zfq8yhsh0+2/8eZnZaiicPo2BT3hqvfNYV9s52cnb2w2sh+GHUGoTeSx9fJi5/+d8GWeb1pa/i8d1wOR5Nc7ykSo6Kx6mPV6LRp33RcEIfxAaG4tT4lXJ8l2DpZI8Of32IA4MXIyEkEjWHtYW5gzU8xvWUm4bmuJVLOXTe/Il2f9ddU+Tlvn7zER8UUeLbWZpxmY3CUanVeffvRUVFwd7eHpGRkbCzs4NSTa2ZPsZH6Wbffra+FZV8Nla1oXQx8cbRHf+u2xdQuh8CZ8AYbGsxtbibUGL0PjkbSlWcn9+ac/+vymRYmFga9NyJaQn46f7Xio9bclPqMmhERERkQMWwzAZKQAat1I1BIyIiIlI6BmhERERECsMuTiIiItIbroNWOMygERERESkMM2hERESkN/wtzsJhBo2IiIhIYRigERERESkMuziJiIhIbzhJoHCYQSMiIiJSGGbQiIiISG/EL0rm41cli/ycxo4ZNCIiIiKFYQaNiIiI9CZNnb4Z+pzGjhk0IiIiIoVhgEZERESkMOziJCIiIr0RvY2G7nFUw/iVqABtXsASGINFliugdOZlbGEMnkSfh9KZmyn/ubS2rA5jkJwSA6X7UTUTxkCtToHS7Wg5pbibQFRsSlSARkRERMrCSQKFwzFoRERERArDAI2IiIhIYdjFSURERHrDLs7CYQaNiIiISGGYQSMiIiI9L7Nh4N/ihPFjBo2IiIhIYZhBIyIiIr3hGLTCYQaNiIiISGEYoBEREREpDLs4iYiISG/U6vTN0Oc0dsygERERESkMM2hERESkN2KJjTSDL7OhhrFjBo2IiIhIYUpFgObs7IiNG//EvXsXkZz8CJ6eDbOVmTTpI9y6dQZhYbdx6tQ+dOnSIV91z5w5RdbZt28P7b5Ro4bg0qWjCA29haCgq1i//g+4ubnm0cZKWP/3T7h15yhiE26jceP6mY4v/n4mQkIvarfH4ZdkOS+vBjrr69q1PU6e3oH7QWcR+PAMtm5fgQYN6miPly1rLeu87X8MD4LPYdnPc2FlZZlrG52cKmLNX4tw5cZuPIk+j4aN6mYrU7myI/74cz7uBh6W29///oD8+OW3r7PV2X9AV/jsXYEHIcdw6MhfKEnEc7n6r4W4fMMH4dFn0bDRs79N5udyLvwDD8rt73+X5ljfa8P74sTZjbh7/yBu3t2HFau/gWsVZ+3xiZP/h0dPTiIg6D/tNuDlrnm+Jjf8/Qtu3zmBuIS7aNzYI9Px776fjUehl7VbaPhVWS6n16R3uxfl8Yz3WbDwC+3xYcNfwaHDmxAUchF3/E/ix2VzYW9vl2sb09vphI3/rsK9gEtITgnV/f6ePA63bp9DWLg/Tp3ajy4v5fP9PWuqrDPj+9vU1BTffjtLni807A4OHNyGpk09c62nZ8+eOHjQF+HhjxES8hAbNvwFV9fM/yf069cXN25cRWxsFA4fPoi6dbO/vwpSvqD1GSuHxlXRdtUH6Hbwc7Rd+T7KNXQrdHmLCrZoNn8EOm2bhJ4nvoJtbRcDPAKiUhygpaWpsXu3L159dZTO4+I/3/Hj30X//sNRoUJNLFq0DH///QccHMrlWm/jxg3Qu3dXPHwYnGm/r+9/aN++NypWrIXq1b1w585d/PLL4jzamIY9uw9h8KD/03n8ww8+g1PFxtpt5hcLcePGHZw/f1ln+YsXr6Bv71Go4tIU1d1aYNdOX6xdv0x7/Ou5U+Du7oZmTbrBo247uLg4Yt43n+XZxn17jmL40PE6j1tbW2LL9l9xye86GtbvhprVO2D2zJyDCo2u3bxRybF8tv1PnkTix6WrsWD+byhpNM/liFyey83bf8YlvxtoVL8HalXvlOtzeejgKfR46XVUr9IeDev2wF3/+/j+hxmZyvjsOoyqLm21278bd+fRRjX27D6IwYPe1nl87AdT4VixgXb78otvcePG7Rxfk0JERFSm+4wf96yN1lZWmDp1DqpXbY5mTV+SX6wWfTcz1zamtzMNu3324dVXRuTy/n4P/fu9hgrl3bFo0Y/4++8V+Xt/9+qW7f393ntvolfvbvBu2x2OlWrBx2e/DBBzIwLNuXPnw82tOtzdayEqKgrr16/THq9Tpw5Wr16JceMmoHz5Sti/3xebN2+UwaAueZUvaH3GqoydFV74dhTubTiGPV1m4t7fx/HCglEws7EsVHm1Wo3Hx2/gzKe5/z2pcJMEDL0Zu1IRoD169BjLlv2BU6fO6Txeo0Y1nD59HpcuXZW3V6/egDJlysj9OTExMcFPPy3A2LGTkZSUlOlYQMB9hIWFy+sqlUp+gNSq5Z5HG8Pwy8+rceb0xXw9ppGjB2Llig05Hg8Ofiw3TRtSU9NQrZorzMzShx327dsV337zk/zAjIyMxvx5P2Loa/1haWmRY52PH4fjt1/X4+yZSzqPvzasH8LCn+Db+b8iJiYOqampOHc25w9rwcbGGrPnTMD4j2ZnO3bwwAls+nc3HgY9Qkkjnsvff92As2d0Pz9Dh/VFeHgEvp3/W4bn8kqO9d0PDEJ4WESm11zNmlWfq42PHoXi559X4fTpC/kqP2r0YPyZy2syL7/8sgqHDx1HYmKiDM5//WU1Wrd+oQje39Vx+vQ5He/v6nm8vxdh7IeTsr2/3d2rwXf/Yfk+F8/ziuVr4OrqggoVsn/J0Fi7dh127NiB2NhYxMXFYdGi79CyZQttwDR8+DD4+h7A9u3b5eOfOXMWHB0d4e3trbO+vMoXtD5j5dTeAwmPoxC4+TTSklPlZWJYNJw7eBSqfFJ4DAL+OYHIK/cN/EiISmmAlpf16zfJb+teXg3lf8yii/L+/SBcunQtx/t8+OH/wc/vCg4fPqbzeJs2LfH48U1ERwfgo4/+D3PmLCqy9rZo2QS1alXHqpX/5FquipuL7L4Mj7yC+d9+hm/mL0NKSoo8pjJRyQ9yDfG4RRenqLewWrdthocPHmHDP0tw595B+B5ag5e6ts31PtM/H4u/1m7DndsBhT5vSdSmbVM8fBCC9f98j9v3fLH/0Gp06dom1/u0bOUlu0IfPj6Gd98fhgXfZM48tmvXHLfu7cfJc/9i6vT3YGFhXmTtbdGy6dPX5N95BuSiy/TmrWP4ffkiVK7slGPZtt4tcckv5/dgfq1f/y+cncT7u9HT9/dQ3L//UBuw6fLhR+/Az+8yDh86mu3YH3+sll2aIsATX3hef2MEjh07qf1Slh/t27fD1atXZeAtNG7cCOfPPwuExfv0ypWrcr8ueZUvaH3GSnRBRt14mGlf1I0g2NZyLpLyVDTSimkzdpzF+TRTsGPHHhw/vkemuGNj4zBw4Bj5zVMX8Q363XdfR4sWXXKs88iRE6hUqbb8Vv3668Nw9eqNImvv6DGDsHOHr8y65UZkVVydm8DGpiyGDX9ZBp0aPrsOYMIn/we/i+kfUp98+o68tLWzKXS7HBzs4N2uOUYOn4Chgz9C1+7eWL5yPtq2GgT/O4HZyrdo6Ym23i+gfdshhT5nSVXOwR7e7V7AqOGf4LXB49C1e1ssXzkP3q2G6HwuhRPHzsPdrT3KVyiHEaP649q1O9pjm//dg5Ur/kVQ0GPUq1cDy36dBZuyVpg88Zsiae+YMYOxc8d++V7KyY3rt/Fii564du0WKlWqgDnzpuHvf35Dm9Z95Psuo65dO2D0mMHo0mlg0by/d+7B8RN7M7y/R+Xx/n4DLZp30nn8zp17uHDBD9dvnJaBT0jIY/TpPTjf7fHy8sLMmV9g4MBnr3sbGxtERKRnQDXEbVtbW5115FW+oPUZKzMrcyTHJGTalxKTADNriyIpT1ScSmQGbejQV/Dkib/czp8/lGf5adM+Ro8eXeDh0QrW1q54+eWRWLPmZ52DjYUff/wWM2bMwZMnmf8D1EV8q/7jjzXYtGkVrK2ttfsHD+mrHfB/6uzOfD82Mbj/5Vd6YsXy9fm+T0xMLH7+aZWcCFCtehW579MJMxEY+BDHT27Df8c2Y/u2fXK/pptMGDioJwKDjsrt6Mncs3VCbGw8Tp64gB3bfOUHl7i8cP4qOnVqla1smTJmWPTdZ/h43GwkJ6dn9UqyVwf10A7OP3oy725AEUScPHERO7YdePpcHsCF89fQsdOLed5X/A1X/bkJ69YvlmPZBBGsPXz4SAYoV6/exswvlqD/K5knCQwe0k87eP/02dzHp2V/TfbCiuW5T+QQgcyVKzdkt6C4/v67k9GocX3Url0jU7n2HVrhtz8WYuiQ/8Ply9ez1TN06Kt4EnFXbucv/Jdn+6Z9NgE9uneBR/2WsLZywcsvj8CaNb/m/P5etgAzpn+d4/t7yZJ5qFrNDW5VGsCmrCvGj5uC3Xv+hYvLsyzMa68NRXR0hNwuXXqWyWrYsCF27tyG998fi71792r3x8TEwN7ePtN5xO3o6GidbcirfEHrMxaVu3miq+8MuXmv/RAp8Ukok2W8mVlZC6TE6Q6+C1qeiob4f6c4NmNXIgO0tWv/gYODu9y8vNrlWV50ffz99xY5mF/8UQ8dOoqLFy+jUyfd9+3cuR2+/XamnKEpNjFD848/luCbb77UWV4EI+XK2cPRsaJ231/rtmgH/Ddv+myGWF5eHdQb0VEx2O1zEAUhujPF+LJq1dIDNDH27J3/TULtmm1Qv0473Ll9D8FBj+TEA40N63fAzaW13Fq3eCXPc4gB7fnl7OKIOnXdsXLNAty66ys3Yev2X/Du+8NR0vy9fqd2cH7rFnlnhS773Xyu85UxM4N9OVtUrKR7XJRaxy8J/7Vus3bw/gtNc5/hmdHAQX3ka9LH50CB2qjrP1ARnK1e8yPGjPoQB3yzdy8Ka9f+DYdy1eXm5Zl7F7rg5dUYf/+T4f198Ej6+7tze53lO3duj28XzEJQ8HW5yff38h/wzbez5HFPr0b4c8U6BAeHyC7KjRu3IjIyCq1aNdfWsWbNWtjalpNbw4ae2uBs714fTJ48FatXr8l0zosX/eDl9WwmqOg69fCoDz8/3eM98ypf0PqMxUOfC9jd8Qu5HR66GNE3g2CXZaalXZ3KiL4dovP+BS1PVJxKZICmi4WFhdwEc/My8rpmDNbx46fxyit9ULVqevDSunULNG/eFBcu6P7PrHp1T7zwQiftJmZ5TZjwGWbN+lYeF2PYxKBhwcnJEQsXfoXr12/h7t3cx1mJMUGacUFlZBvNM40TS697IFat2iizELl5dWBvOclB3N/e3laOQRNZmfPn0h+TyKQ5OlaQ1z09PTBn/lTMmrU4z28dGdtonqWN69ZuRWPP+ujW3VvuE5fi9r592T9oH9wPRmOPHmjXerB2E14fPRF/Lt8or4vxQqJ+EeCKU4jr4pwlRe7P5TY09qwnu4nFPnEpbu/fdyzHZTbEshyC+LvOmf8pbt68i4B76eNtevXpCIfy6RmVWrWrYdqM97B18/7net9ojBo1GKtW/Z3na7Jd+1baDG758uWw+PtZMqN265a/dhmONWuX4c3Xx2Hv3rwz3/lt5/Hjp/DKy7re334666perRFeaNZBu8n398fTMGvmfHn8xPHTGD5iECpWrCDP0a9fT1SpUjnXMW0eHh4yOJs2bTqWL1+R7fiqVavRqVNH9OjRA+bm5pg6dQpCQ0Nx6JDu5yGv8gWtz1iFHLwCS0d7VOnTDCozU3lpUdEWwQcuF7q8ibmZ3OT1Mqbp17O85okModQEaDExgXITjh71kde9vdO73r75Zgn27z+MAwe2Ijz8jlwS47PPZmP//kPaAf+iu1TjwYOgTJv4Fh0W9gQREZHyuOg6OXJkFyIi/HHy5F4kJyejT5+hebYxPPKq3IRD//0rr7f1bqE9Xq9eLTRv4aWze7N1mxdkd6mGmLG5dccKue+C3z6ZOevbaxSiomLk8UaN6uO/o5vlemorVn2HxYt+xR+/PZv2n5Pg0JNyE/YdWCWvt27TTN4WSzuMHjEBn8/8CAEPj2DqZ+9j1PCP5X5Nl6mmq1R8mIsut4ybZnajmLUoDB7aW9a/+Pvpcn00cf3U2c0oKYJCj8tN2Htgpbzeuk1TeVs8Z2NGfIIvZn6Iew8PY+pn72L08E+0z6XoMs3YVdqoUR3s9v0TgcFH4PvfGtltPOjlD7TH+/XvgpNnN8rjYuKBCPSmT12YZxufRF6Xm3Dov83yuhi8n/U1uVxH92brNs1ld6mG+CKwZ+96PA67gtNndsuszisvv6EN7KZM/RB2djb4c9WSTGul5UdM7AO5CUeP7ZHXvdu1lre/mf899vsexoGD2xH+5C5++fV7fDZtFvbve/r+bvui7C7N/f0drn1/f/rpdNwPfICz5w7JddBmzJiIMaPfw40bt3Js34QJ41GpUiUsXPittutTbG5u6Wtw3bhxA8OHj8TixQsQERGGl17qgr59B2gnEbRt21aW18irfF7HS4rkqHicnvAnqg9uja77psvL0x//iZTo9HFmlk72sjtUXOanvND98JdyE9r88a68Xr5J4SdPESAS9sWxGTuVOh8dtWLNHjF+ITIyEnZ2eS8cWVzKlEnPICiduanyB+qal1F+GwWxuK3SlbdND7qULCE5/zMQi1NySvoXDCVLSc17bKoSqNXKH/u5o+UUGAOxqK1SFefnt+bc/Rw+RRkTw07ESE5LxOYn8xQft+SGsziJiIhIb9KK4bc40/hbnERERESlx+zZs9G6dWu5MkO5crp/kSQgIAC9evWSZcQi0Z988ol2HdL8YgaNiIiIKJ/Er4sMHDgQrVq1wm+/Zf8pQjHWUwRnzs7OOHr0KIKCgjBy5Ej5CyZffZX/7nAGaERERKQ3orPR0MuSqfVY9xdffCEvly9frvP47t27ceXKFbnWoZOT09PFqWdi4sSJ+Pzzz+XM6vxgFycRERGVSFFRUZm2nH5BpCgdO3YMjRo1ksGZRrdu3eT5L1/O38x0gQEaERER6X2SgKE3QSxlI2aSaravv/4a+hYcHJwpOBM0t8Wx/GKARkRERCVSYGCgXGpDs02ePFlnuUmTJsmFp3Pbrl27ZtC2cwwaERER6Y0Yf2boRS/UT08o1kDLzzpoH3/8MUaPHp1rmRo1Mv9ucE7E5ICTJ9MXdNcICQnRHssvBmhERERUqlWqVEluRUHM7hRLcTx69EgusSHs2bNHBoriZ9/yiwEaERERUT6JNc7Cw8PlpVhS4/z59F+0qVWrFmxsbNC1a1cZiI0YMQLz5s2T486mTZuG9957T/ubwfnBAI2IiIj0pqT9ksD06dOxYsUK7e0mTZrIS19fX3To0AGmpqbYtm0b3nnnHZlNK1u2LEaNGoUvv0z/jdf8YoBGRERElE9i/bOc1kDTqFatGnbs2IHnwQCNiIiI9CZNXQwZNDV/i5OIiIiIihgDNCIiIiKFYRcnERER6Y366T9Dn9PYlagALSUlHMYgOfkRlM7Jvg2MgUql/JewWp0CpTOG59FYnktjkZJ6AErXvFpQcTeBqNgYx//KREREZJRELiutGM5p7DgGjYiIiEhhmEEjIiIivSlpC9UaCjNoRERERArDAI2IiIhIYdjFSURERHqjVhfDMhtqdnESERERURFjBo2IiIj0hpMECocZNCIiIiKFYYBGREREpDDs4iQiIiK9YRdn4TCDRkRERKQwzKARERGR3qTnzwz7a5xqg//6Z9FjBo2IiIhIYZhBIyIiIr3hGLTCKfEZtJ49e+LgQV+Ehz9GSMhDbNjwF1xdXTOV6devL27cuIrY2CgcPnwQdevWzbXOvMoXtD5j4ehUAX+unYsL1zYjJPIIGjSqnel467ZN5P47D/Zot6/mj8+1TpfKlfDLipm4cW+X3NZtXKA9tubvbzLVdS9kP4KeHEb58vY51se/d9Hhc1maqGCiqgNTk5YwNWkLU5MWUKmcMxw3hYmq/tNjraFSVcujvrzKF7S+7GzffQMV1/yCin/+qN3MatcsfHlTU9i8PhwVfl+CCr9/D5sxwwCTEv8RSQpW4l999vZ2mDt3PtzcqsPdvRaioqKwfv067fE6depg9eqVGDduAsqXr4T9+32xefNGmJqa6qwvr/IFrc+YqNPSsH/fcYx+bVKOZSIjolHD9SXtNuWTZwFXVtbWlti47Xtc8buFJg1eRv0avTBn1s/a46+9OiFTXatXbMWhA6cRHh6ZY538excdPpeliQpAElLTLiA17T+kpl2DiaomVHCQR01UtQFVGaSmHUdq2jmYqFygUjnlWFte5QtaX07iffYjdOQ72i3l5u1Cl7d+uQ/K1KuNJ+On4sn4aShTvw6sB/QucJuIikqJD9DWrl2HHTt2IDY2FnFxcVi06Du0bNlC+5/+8OHD4Ot7ANu3b0diYiJmzpwFR0dHeHt766wvr/IFrc+YPH78BMt//Rfnzl4tkvoGv9YT4WGRWPjNCsTGxCE1NRXnz17TWdbCwhwvD+qKNSu35Von/95Fh89laZKGNPVdAAlPb0dBjQioVCJbbQKVyhFpaf4AUkSYgzT1AxlU6ZZX+YLWZxiWHb0R+89WpEVEyi1241ZYduJrryi7OA29GbsSH6Bl1b59O1y9elUGA0Ljxo1w/vwF7fGUlBRcuXJV7tclr/IFra+kKWtjJbtAz135Fz/8MgPOLhVzLNuqrRcePnwkuzKv3d2J3Qd/Q+eXWuks27N3O6SlpWHH1oMFag//3kWHz2VpYgIV7KBWx4rcElQq8VER8+ywWlwvm8N98ypf0PpyZtm+teyOdPh2Fqx6dwNUqkKVV5W1hmnF8ki5G6Atm3o3EKaVKkJlZVXgdhEVhVI1ScDLywszZ36BgQOHaPfZ2NggIiIiUzlx29bWVmcdeZUvaH0lyc0b99C57WjcuH4PFSqWw5dffYCVf81D1/ZvQK3O/m3GwcEObbyb4o0RUzFyyES81K01fv1zFjq2GYm7dx5kKjtsVB/8vc4HycniG3f+8O9ddPhcli4mqrpQIw5qPBad3VCrRVD+7D2slpmvnD4+TPMon9fx/InfuRcxK9dDHRMDs1rusBv3LqBWI3777gKXV1laprcjLk5bPi02/brKyhLq+PgCtY0yS3v6z9DnNHYlLoP22mtDER0dIbdLl559G2/YsCF27tyG998fi71792r3x8TEwN4+86BzcTs6Olpn/XmVL2h9SvbKwK7aAfoHj6/Ks/zjR+G4dtVfZrrE9Y8/nIcGDWuhZi03neVjY+Jx6sQl7Nx+GCkpqfLy4vnr6NCpRaZyVau5yEBuzcqt2erg37vo8LkkzfgwlcoKaWmXnu5JffpR8Sw7pZLBVE5flvIqX9D6dEvxvwe1eG2o1Ui5eQdxm7bDolWLQpVXJ6R37aqsn2XLNNfV8ZpuXyLDKnEB2po1a2FrW05uDRt6aj9g9u71weTJU7F69ZpM5S9e9IOXV3o5wczMDB4e9eHnp/nPCQUqX9D6lOyfDbu1A/Tbvzi84BXoyJpldPnSrXxV89qI3jh35qoM/rLi37vo8Lmk9ODMDqlpF58GUoLIJKkzd0GqbMRXrBxqyat8Qesrmv9vciuvjo1Damg4zKpX1e4T11NDw5g9o2JT4gK0rDw8POQHzLRp07F8+Ypsx1etWo1OnTqiR48eMDc3x9SpUxAaGopDhw7prC+v8gWtz9iIwfpiE8zLmMnrqqfjOESWS2S7NN2XcxdMwPWr/rhz+77Outav24XGnnVk16aoQ1yK2777TmrLmJiYyMkEurJnuvDvXXT4XJbG4MxezuTMnM1Kg1r9CCYm7rJ7ErCCicoVaeqgHGrKq3xB69PNolVz2f0omNWoDut+vZB44nShyyccOAzrAX2gsreTm5jBmbCPr72ioFapoValGXhTw9iV+ABtwoTxqFSpEhYu/FbbfSM2N7f0brcbN25g+PCRWLx4ASIiwvDSS13Qt+8A7UDotm3byvIaeZXP67ixC3jkKzdhl++v8nqrNl7ydsPGtbFpx1L4P9yLg8dXwszMFMMHfyq7PIWWrTxld6nGPf8HeGPkNHz25bu4fX83Jk17G6+PmCr3a3Ts3BL29jb49599+Wof/95Fh89laWIBExOxxp01TE1awdTEW25ibTQhTX0TUKc8PdZEBlNqdYj23iYmjaBSPcs+5VU+r+P5YdmtM8r/8K1cz8x27NuI370f8dt8tMdt3hopt/yWj/tnK1Ju3kL5hV/JLeX6TcT9m/uscSJ9Uql1jd7OQqx/JMaCREZGws7ODkqlUhnHnAe1umBjLYqDk30bGINHUSegdMbw9+Z7p/RJST0ApXsyNHvmVokqrf8DSlWcn9+aczezfwumqvSeF0NJVSfhTOQvio9bSnUGjYiIiMjYMEAjIiIiUhjj6NcgIiIioyTWJFNxHbQCYwaNiIiISGGYQSMiIiK90fw6pqHPaeyYQSMiIiJSGGbQiIiISG/SVGlQqTgGraCYQSMiIiJSGAZoRERERArDLk4iIiLSGy6zUTjMoBEREREpDDNoREREpDfMoBUOM2hERERECsMAjYiIiEhh2MVJREREesNfEigcZtCIiIiIFKZEZdDU6pTibkKJERJ5pLibQAbE907pY2baAUpXab3y20h5S0MqVEg1+DmNHTNoRERERApTojJoREREpCxq+c/QY9DUMHbMoBEREREpDAM0IiIiIoVhFycRERHpTZoqDSoVf0mgoJhBIyIiIlIYZtCIiIhIz8tsGDYflMZlNoiIiIioqDFAIyIiIlIYdnESERGRHhn+tzjBSQJEREREVNSYQSMiIiK9SVOLAfsmxXBO48YMGhEREZHCMINGREREeiPGnxn+tzjTYOyYQSMiIiJSGAZoRERERArDLk4iIiLSGzVSoTZwPkjNXxIgIiIioqLGDBoRERHpTZocsG/YQfvp5zRuzKARERER5dPs2bPRunVrWFtbo1y5cjrLqFSqbNu6detQEMygEREREeVTUlISBg4ciFatWuG3337Lsdwff/yB7t27a2/nFMzlhAEaERER6Y1a/jP0OmhqvdX9xRdfyMvly5fnWk4EZM7OzoU+D7s4iYiIqESKiorKtCUmJhrs3O+99x4qVqyIFi1a4Pfff4daXbCgkRk0IiIi0hu1WiyzoTL4OQU3NzdkNGPGDHz++efQty+//BKdOnWS49R2796Nd999FzExMRg7dmy+62CARkRERCVSYGAg7OzstLctLCx0lps0aRLmzp2ba11Xr15FvXr18nXezz77THu9SZMmiI2Nxfz58xmgERERkTIU5zIbdnZ2mQK0nHz88ccYPXp0rmVq1KhR6Pa0bNkSM2fOlF2sOQWJWTFAIyIiolKtUqVKctOX8+fPw8HBId/BmcAAjYiIiCifAgICEB4eLi9TU1Nl8CXUqlULNjY22Lp1K0JCQvDiiy/C0tISe/bswVdffYUJEyagIBigERERkZ5/i1NVYn6Lc/r06VixYkWmMWaCr68vOnTogDJlymDp0qUYN26cnLkpArcFCxbgrbfeKtB5VOp8zPsUU1Pt7e0RGRmZr75cIiIiKn7F+fmtObeTfRuYqAybD0pTpyAk8ohRxy3MoBEREZHeqNVphl+oVs3f4iQiIiKiIsYAjYiIiEhh2MVJREREJXIdNGPGDBoRERGRwjCDRkRERCXytziNGTNoRERERArDDBoRERHpjVr+M/AyG8hziVfFYwaNiIiIyBgzaJofGxCrAhMREZFx0Hxu5+NHg8gYA7To6Gh56ebmpu/2EBERURETn+PiZ5eK75cEDD1JIA2lIkCrXLkyAgMDYWtrC5XKsE8yERERFY7InIngTHyOUwkM0ExMTFClShX9t4aIiIiKVHFlzp4Ry2wY/pzGjpMEiIiIiBSGARoRERGRwnAdNCIiItLzgH1OEigoZtCIiIiIFIYZNCIiItIbZtAKhxk0IiIiIoVhBo2IiIj0Jg1pUBk6gwZm0IiIiIioiDFAIyIiIlIYdnESERGR3nCSQOEwg0ZERESkMMygERERkd6o1aml4pxFjRk0IiIiIoVhgEZERESkMOziJCIiIr1RQy1XQzP8OY0bM2hERERECsMMGhEREZWoJS/UXGaDiIiIiIoaAzQiIiIihWEXJxEREekNuzgLhxk0IiIiIoVhBo2IiIj0Rm3gJTaK65xFjRk0IiIiIoVhBo2IiIj0hmPQCocZNCIiIiKFYYBGREREpDDs4iQiIiK9YRdn4TCDRkRERKQwzKARERGRHhVHNisNxo4ZNCIiIiKFYYBGREREpDDs4iQiIiK94SSBwmEGjYiIiEhhmEEjIiIiveFvcRYOM2hERERECsMMGhEREemNWq02+LIXanlO48YMGhEREZHCMEAjIiIiUhh2cRIREZEepQJQGficahg7ZtCIiIiIFIYZNCIiItLzorGGzaCpOUmAiIiIiIoaAzQiIiIihWEXJxEREemR4bs4wUkCRERERFTUmEEjIiIi/SmGSQLgJAEiIiIiKmrMoBEREZHeqIthPJiaY9CIiIiIqKgxQCMiIiJSGHZxEhERkR5xmY3CYAaNiIiISGGYQSMiIiI9UhdDQksNY8cMGhEREZHCMEAjIiIiUhh2cRIREVGJWwnN2DFAIyIiIj0z/oDJ0BigERERUZEzNzeHs7MzgoODi+X8zs7Osg3GSqVWl4BfFCUiIiLFSUhIQFJSUrGc29zcHJaWljBWDNCIiIiIFIazOImIiIgUhgEaERERkcIwQCMiIiJSGAZoRERERArDAI2IiIhIYRigERERESkMAzQiIiIiKMv/Ay6i585+t6P3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_values(V_random, title=\"Value function: random policy\")\n",
    "plot_policy(policy=random_policy, title=\"Random Policy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5a82a3b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_policy = np.array([\n",
    "    A_DOWN,\n",
    "    A_DOWN,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_DOWN,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_UP,\n",
    "    A_DOWN,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_UP,\n",
    "    A_UP,\n",
    "    A_LEFT,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_UP,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_UP,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_DOWN,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_RIGHT,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_LEFT,\n",
    "    A_LEFT,\n",
    "    A_RIGHT,\n",
    "    A_RIGHT,\n",
    "    A_UP,\n",
    "    A_LEFT,\n",
    "    A_DOWN,\n",
    "    A_UP,\n",
    "    A_UP,\n",
    "    A_LEFT,\n",
    "    A_UP,\n",
    "    A_DOWN,\n",
    "    A_LEFT,\n",
    "])\n",
    "\n",
    "V_my_policy = policy_evaluation(policy=my_policy, P=P, R=R, gamma=gamma)\n",
    "\n",
    "plot_values(V=V_my_policy, title=\"Value function: my policy\")\n",
    "plot_policy(policy=my_policy, title=\"My policy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "446c93e4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "studies",
   "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": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}