{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep Otello AI\n", "\n", "The game reversi is a very good game to apply deep learning methods to.\n", "\n", "Othello also known as reversi is a board game first published in 1883 by eiter Lewis Waterman or John W. Mollet in England (each one was denouncing the other as fraud).\n", "It is a strickt turn based zero-sum game with a clear Markov chain and now hidden states like in card games with an unknown distribution of cards or unknown player allegiance.\n", "There is like for the game go only one set of stones with two colors which is much easier to abstract than chess with its 6 unique pieces.\n", "The game has a symmetrical game board wich allows to play with rotating the state around an axis to allow for a breaking of sequences or interesting ANN architectures, quadruple the data generation by simulation or interesting test cases where a symetry in turns should be observable if the AI reaches an \"objective\" policy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Content\n", "\n", "* [The game rules](#the-game-rules) A short overview over the rules of the game.\n", "* [Some common Otello strategies](#some-common-otello-strategies) introduces some easy approaches to a classic Otello AI and defines some behavioral expectations.\n", "* [Initial design decisions](#initial-design-decisions) an explanation about some initial design decision and assumptions\n", "* [Imports and dependencies](#imports-and-dependencies) explains what libraries where used" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The game rules\n", "\n", "Othello is played on a board with 8 x 8 fields for two player.\n", "The board geometry is equal to a chess game.\n", "The game is played with game stones that are black on one siede and white on the other.\n", "\n", "![Othello game board example](reversi_example.png)\n", "\n", "The player take turns.\n", "A player places a stone with his or her color up on the game board.\n", "The player can only place stones when he surrounds a number of stones with the opponents color with the new stone and already placed stones of his color.\n", "Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n", "All stones thus surrounded will be flipped to be of the players color.\n", "Turns are only possible if the player is also changing the color of the opponents stones. If a player can't act he is skipped.\n", "The game ends if both players can't act. The player with the most stones wins.\n", "If the score is counted in detail unclaimed fields go to the player with more stones of his or her color on the board.\n", "The game begins with four stones places in the center of the game. Each player gets two. They are placed diagonally to each other.\n", "\n", "\n", "\"Startaufstellung.png\"\n", "\n", "## Some common Othello strategies\n", "\n", "As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n", "If the player occupies huge homogenous stretches on the board it can be attacked easier.\n", "The boards corners provide safety from wich occupied territory is impossible to loos but since it is only possible to reach the corners if the enemy is forced to allow this or calculates the cost of giving a stable base to the enemy it is difficult to obtain.\n", "There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n", "Those different values are score modifiers for a traditional greedy algorithm.\n", "If a players stone has captured such a filed the score reached is multiplied by the modifier.\n", "The total score is the score reached by the player subtracted with the score of the enemy.\n", "The scores change in the course of the game and converges against one. This gives some indications of what to expect from an Othello AI.\n", "\n", "\"ComputerPossitionScore\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial design decisions\n", "\n", "At the beginning of this project I made some design decisions.\n", "The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n", "I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n", "I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n", "The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n", "\n", "I wanted to implement different agents as classes that act on those game stacks.\n", "\n", "Since computation time is critical all computational have results are saved.\n", "The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext blackcellmagic\n", "%load_ext line_profiler\n", "%load_ext memory_profiler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and dependencies\n", "\n", "The following direct dependencies where used for this project:\n", "```toml\n", "jupyter = \"^1.0.0\"\n", "matplotlib = \"^3.6.3\"\n", "numpy = \"^1.24.1\"\n", "pytest = \"^7.2.1\"\n", "python = \"3.10.*\"\n", "scipy = \"^1.10.0\"\n", "tqdm = \"^4.64.1\"\n", "jupyterlab = \"^3.6.1\"\n", "torchvision = \"^0.14.1\"\n", "torchaudio = \"^0.13.1\"\n", "```\n", "* `Jupyter` and `jupyterlab` on pycharm was used as an IDE / Ipython was used to implement this code.\n", "* `matplotlib` was used for visualisation and statistics.\n", "* `numpy` was used for array support and mathematical functions\n", "* `tqdm` was used for progress bars\n", "* `scipy` contains fast pattern recognition tools for images. It was used to make an initial estimation about where possible turns should be.\n", "* `torch` supplied the ANN functionalities." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "import abc\n", "import itertools\n", "import os.path\n", "from abc import ABC\n", "from enum import Enum\n", "from typing import Final\n", "from IPython.display import clear_output\n", "from pathlib import Path\n", "import glob\n", "import copy\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from ipywidgets import interact\n", "from scipy.ndimage import binary_dilation\n", "from tqdm.notebook import tqdm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constants\n", "\n", "Some general constants needed to be defined. Such as board game size and Player and Enemy representations. Also, directional offsets and the initial placement of blocks." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "BOARD_SIZE: Final[int] = 8 # defines the board side length as 8\n", "PLAYER: Final[int] = 1 # defines the number symbolising the player as 1\n", "ENEMY: Final[int] = -1 # defines the number symbolising the enemy as -1\n", "EXAMPLE_STACK_SIZE: Final[int] = 1000 # defines the game stack size for examples\n", "IMPOSSIBLE: Final[np.ndarray] = np.array([-1, -1], dtype=int)\n", "IMPOSSIBLE.setflags(write=False)\n", "SIMULATE_TURNS: Final[int] = 70\n", "VERIFY_POLICY: Final[bool] = False\n", "TRAINING_RESULT_PATH: Final[Path] = Path(\"training_data\")\n", "if not os.path.exists(TRAINING_RESULT_PATH):\n", " os.mkdir(TRAINING_RESULT_PATH)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The directions array contains all the numerical offsets needed to move along one of the 8 directions in a 2 dimensional grid. This will allow an iteration over the game board.\n", "\n", "![8-directions.png](8-directions.png \"Offset in 8 directions\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1, -1],\n", " [-1, 0],\n", " [-1, 1],\n", " [ 0, -1],\n", " [ 0, 1],\n", " [ 1, -1],\n", " [ 1, 0],\n", " [ 1, 1]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DIRECTIONS: Final[np.ndarray] = np.array(\n", " [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n", " dtype=int,\n", ")\n", "DIRECTIONS.setflags(write=False)\n", "DIRECTIONS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another constant needed is the initial start square at the center of the board." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1, 1],\n", " [ 1, -1]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "START_SQUARE: Final[np.ndarray] = np.array(\n", " [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n", ")\n", "START_SQUARE.setflags(write=False)\n", "START_SQUARE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating new boards\n", "\n", "The first function implemented and tested is a function to generate the starting environment as a stack of games.\n", "As described above I simply placed a 2 by 2 square in the center of an empty stack of boards." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, -1, 1, 0, 0, 0],\n", " [ 0, 0, 0, 1, -1, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_new_games(number_of_games: int) -> np.ndarray:\n", " \"\"\"Generates a stack of initialised game boards.\n", "\n", " Args:\n", " number_of_games: The size of the board stack.\n", "\n", " Returns: The generates stack of games as a stack n x 8 x 8.\n", "\n", " \"\"\"\n", " empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n", " empty[:, 3:5, 3:5] = START_SQUARE\n", " return empty\n", "\n", "\n", "get_new_games(1)[0]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "test_number_of_games = 3\n", "assert get_new_games(test_number_of_games).shape == (\n", " test_number_of_games,\n", " BOARD_SIZE,\n", " BOARD_SIZE,\n", ")\n", "np.testing.assert_equal(\n", " get_new_games(test_number_of_games).sum(axis=1),\n", " np.zeros(\n", " [\n", " test_number_of_games,\n", " 8,\n", " ]\n", " ),\n", ")\n", "np.testing.assert_equal(\n", " get_new_games(test_number_of_games).sum(axis=2),\n", " np.zeros(\n", " [\n", " test_number_of_games,\n", " 8,\n", " ]\n", " ),\n", ")\n", "assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n", "del test_number_of_games" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualisation tools\n", "\n", "In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n", "For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n", "White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_othello_board(\n", " board: np.ndarray | torch.Tensor,\n", " action: np.ndarray | None = None,\n", " ax=None,\n", ") -> None:\n", " \"\"\"Plots a single otello board.\n", "\n", " If a matplot axis object is given the board will be plotted into that axis. If not an axis object will be generated.\n", " The image generated will be shown directly.\n", "\n", " Args:\n", " board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n", " ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n", " \"\"\"\n", " if isinstance(board, torch.Tensor):\n", " board = board.cpu().detach().numpy()\n", " assert board.shape == (8, 8)\n", " plot_all = False\n", " if ax is None:\n", " fig_size = 3\n", " plot_all = True\n", " fig, ax = plt.subplots(figsize=(fig_size, fig_size))\n", "\n", " ax.set_facecolor(\"#0f6b28\")\n", " if action is not None:\n", " ax.scatter(action[0], action[1], s=350 if plot_all else 200, c=\"red\")\n", " for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n", " if board[x_pos, y_pos] == ENEMY:\n", " color = \"white\"\n", " elif board[x_pos, y_pos] == PLAYER:\n", " color = \"black\"\n", " else:\n", " continue\n", " ax.scatter(x_pos, y_pos, s=280 if plot_all else 140, c=color)\n", " for x_pos in range(-1, 8):\n", " ax.axhline(x_pos + 0.5, color=\"black\", lw=2)\n", " ax.axvline(x_pos + 0.5, color=\"black\", lw=2)\n", " ax.set_xlim(-0.5, 7.5)\n", " ax.set_ylim(7.5, -0.5)\n", " ax.set_xticks(np.arange(8))\n", " ax.set_xticklabels(list(\"ABCDEFGH\"))\n", " ax.set_yticks(np.arange(8))\n", " ax.set_yticklabels(list(\"12345678\"))\n", " ax.set_xlabel(\n", " f\"W{np.sum(board == ENEMY)} / {np.sum(board == 0)} / B{np.sum(board == PLAYER)}\"\n", " )\n", " if plot_all:\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "\n", "plot_othello_board(get_new_games(1)[0], action=np.array([3, 3]))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def plot_othello_boards(boards: np.ndarray, actions: np.ndarray | None = None) -> None:\n", " \"\"\"Plots multiple boards into subplots.\n", "\n", " The plots are shown directly.\n", "\n", " Args:\n", " boards: Plots the boards given into subplots. The maximum number of boards accepted is 70.\n", " \"\"\"\n", " assert len(boards.shape) == 3\n", " assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n", " assert boards.shape[0] < 70\n", "\n", " if actions is not None:\n", " assert len(actions.shape) == 2\n", " assert actions.shape[1] == 2\n", " assert boards.shape[0] == actions.shape[0]\n", "\n", " plots_per_row = 4\n", " rows = int(np.ceil(boards.shape[0] / plots_per_row))\n", " fig, axs = plt.subplots(rows, plots_per_row, figsize=(12, 3 * rows))\n", " for game_index, ax in enumerate(axs.flatten()):\n", " if game_index >= boards.shape[0]:\n", " fig.delaxes(ax)\n", " else:\n", " action = actions[game_index] if actions is not None else None\n", " plot_othello_board(boards[game_index], action=action, ax=ax)\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def drop_duplicate_boards(\n", " boards: np.ndarray, actions: np.ndarray | None\n", ") -> tuple[np.ndarray, np.ndarray | None]:\n", " \"\"\"Drop boards that follow each other and are duplicates will be dropped.\n", "\n", " Args:\n", " boards: A set of boards to be reduced.\n", "\n", " Returns:\n", " A sequence of boards where boards that where equal are dropped.\n", " \"\"\"\n", " non_duplicates = ~np.all(boards == np.roll(boards, axis=0, shift=1), axis=(1, 2))\n", " return (\n", " boards[non_duplicates],\n", " np.roll(actions, axis=0, shift=1)[non_duplicates]\n", " if actions is not None\n", " else None,\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hash Otello Boards\n", "\n", "\n", "### TODO ADD a text here" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [], "source": [ "def hash_board(board: np.ndarray) -> int:\n", " assert board.shape == (8, 8) or board.shape == (64,)\n", " return hash(tuple(board.reshape(-1)))\n", "\n", "\n", "def count_unique_baords(boards: np.ndarray) -> int:\n", " return np.unique(\n", " np.apply_along_axis(hash_board, axis=1, arr=boards.reshape(-1, 64))\n", " ).size\n", "\n", "\n", "a = count_unique_baords(np.random.randint(-1, 2, size=(10000, 8, 8)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find possible actions to take\n", "\n", "The frist step in the implementation of an AI like this is to get an overview over the possible actions that can be taken in a situation.\n", "Here was the design choice taken to first find fields that are empty and have at least one neighbouring enemy stone.\n", "This was implemented with element wise check for a stone and a binary dilation marking all fields neighboring an enemy stone.\n", "For that the `SURROUNDING` mask was used. Both aries are then element wise combined using and.\n", "The resulting array contains all filed where a turn could potentially be made. Those are then check in detail.\n", "The previous element wise operations on the numpy array increase the spead for this operation dramatically.\n", "\n", "The check for a possible turn is done in detail by following each direction step by step as long as there are enemy stones in that direction.\n", "If the board end is reached or en empty filed before reaching a field occupied by the player that direction does not surround enemy stones.\n", "If one direction surrounds enemy stone a turn is possible.\n", "This detailed step is implemented as a recursion and need to go at leas one step to return True." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[[1, 1, 1],\n", " [1, 0, 1],\n", " [1, 1, 1]]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SURROUNDING: Final = np.array(\n", " [[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]\n", ") # defines the binary dilation mask to check if a field is next to an enemy stones\n", "SURROUNDING" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "31 ms ± 10.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "2.93 s ± 428 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "array([[[False, False, False, False, False, False, False, False],\n", " [False, False, False, False, False, False, False, False],\n", " [False, False, False, True, False, False, False, False],\n", " [False, False, True, False, False, False, False, False],\n", " [False, False, False, False, False, True, False, False],\n", " [False, False, False, False, True, False, False, False],\n", " [False, False, False, False, False, False, False, False],\n", " [False, False, False, False, False, False, False, False]]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def _recursive_steps(\n", " board: np.ndarray,\n", " rec_direction: np.ndarray,\n", " rec_position: np.ndarray,\n", " step_one: int = 0,\n", ") -> int:\n", " \"\"\"Check if a player can place a stone on the board specified in the direction specified and direction specified.\n", "\n", " Args:\n", " board: The board that should be checked for a playable action.\n", " rec_direction: The direction that should be checked.\n", " rec_position: The position that should be checked.\n", " step_one: Defines if the call of this function is the firs or not. Should be kept to the default value for proper functionality.\n", "\n", " Returns:\n", " True if a turn is possible for possition and direction on the board defined.\n", " \"\"\"\n", " rec_position = rec_position + rec_direction\n", " if np.any((rec_position >= BOARD_SIZE) | (rec_position < 0)):\n", " return 0\n", " next_field = board[tuple(rec_position.tolist())]\n", " if next_field == 0:\n", " return 0\n", " if next_field == -1:\n", " return _recursive_steps(\n", " board, rec_direction, rec_position, step_one=step_one + 1\n", " )\n", " if next_field == 1:\n", " return step_one\n", "\n", "\n", "def get_possible_turns(boards: np.ndarray, tqdm_on: bool = False) -> np.ndarray:\n", " \"\"\"Analyses a stack of boards.\n", "\n", " Args:\n", " boards: A stack of boards to check.\n", " tqdm_on: Uses tqdm to track the progress.\n", "\n", " Returns:\n", " A stack of game boards containing boolean values showing where turns are possible for the player.\n", " \"\"\"\n", " assert len(boards.shape) == 3, \"The number fo input dimensions does not fit.\"\n", " assert boards.shape[1:] == (\n", " BOARD_SIZE,\n", " BOARD_SIZE,\n", " ), \"The input dimensions do not fit.\"\n", "\n", " poss_turns = boards == 0 # checks where fields are empty.\n", " poss_turns &= binary_dilation(\n", " boards == -1, SURROUNDING\n", " ) # checks where fields are next to an enemy filed an empty\n", " iterate_over = itertools.product(\n", " range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n", " )\n", " if tqdm_on:\n", " iterate_over = tqdm(iterate_over, total=np.prod(boards.shape))\n", " for game, idx, idy in iterate_over:\n", " if poss_turns[game, idx, idy]:\n", " position = idx, idy\n", " poss_turns[game, idx, idy] = any(\n", " _recursive_steps(boards[game, :, :], direction, position) > 0\n", " for direction in DIRECTIONS\n", " )\n", " return poss_turns\n", "\n", "\n", "# some simple testing to ensure the function works after simple changes\n", "# this testing is complete, its more of a smoke-test\n", "test_array = get_new_games(3)\n", "expected_result = np.zeros_like(test_array, dtype=bool)\n", "expected_result[:, 4, 5] = expected_result[:, 2, 3] = True\n", "expected_result[:, 5, 4] = expected_result[:, 3, 2] = True\n", "np.testing.assert_equal(get_possible_turns(test_array), expected_result)\n", "\n", "\n", "%timeit get_possible_turns(get_new_games(10)) # checks turn possibility evaluation time for 10 initial games\n", "%timeit get_possible_turns(get_new_games(EXAMPLE_STACK_SIZE)) # check turn possibility evaluation time for EXAMPLE_STACK_SIZE initial games\n", "\n", "# shows a singe game\n", "get_possible_turns(get_new_games(3))[:1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Besides the ability to generate an array of possible turns there needs to be a functions that check if a given turn is possible.\n", "On is needed for the action space validation. The other is for validating a players turn." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def move_possible(board: np.ndarray, move: np.ndarray) -> bool:\n", " \"\"\"Checks if a turn is possible.\n", "\n", " Checks if a turn is possible. If no turn is possible to input array [-1, -1] is expected.\n", "\n", " Args:\n", " board: A board where it should be checkt if a turn is possible.\n", " move: The move that should be taken. Expected is the index of the filed where a stone should be placed [x, y]. If no placement is possible [-1, -1] is expected as an input.\n", "\n", " Returns:\n", " True if the move is possible\n", " \"\"\"\n", " if np.all(move == -1):\n", " return not np.any(get_possible_turns(np.reshape(board, (1, 8, 8))))\n", " return any(\n", " _recursive_steps(board[:, :], direction, move) > 0 for direction in DIRECTIONS\n", " )\n", "\n", "\n", "# Some testing for this function and the underlying recursive functions that are called.\n", "assert move_possible(get_new_games(1)[0], np.array([2, 3])) is True\n", "assert move_possible(get_new_games(1)[0], np.array([3, 2])) is True\n", "assert move_possible(get_new_games(1)[0], np.array([2, 2])) is False\n", "assert move_possible(np.zeros((8, 8)), np.array([3, 2])) is False\n", "assert move_possible(np.ones((8, 8)) * 1, np.array([-1, -1])) is True\n", "assert move_possible(np.ones((8, 8)) * -1, np.array([-1, -1])) is True\n", "assert move_possible(np.ones((8, 8)) * 0, np.array([-1, -1])) is True" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def moves_possible(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n", " \"\"\"Checks if a stack of moves can be executed on a stack of boards.\n", "\n", " Args:\n", " boards: A board where the next stone should be placed.\n", " moves: A stack stones to be placed. Each move is formatted as an array in the form of [x, y] if no turn is possible the value [-1, -1] is expected.\n", "\n", " Returns:\n", " An array marking for each and every game and move in the stack if the move can be executed.\n", " \"\"\"\n", " arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n", " for game in range(boards.shape[0]):\n", " if np.all(\n", " moves[game] == -1\n", " ): # can be all or any. All should be faster since most times neither value will be -1.\n", " arr_moves_possible[game] = not np.any(\n", " get_possible_turns(np.reshape(boards[game], (1, 8, 8)))\n", " )\n", " else:\n", " arr_moves_possible[game] = any(\n", " _recursive_steps(boards[game, :, :], direction, moves[game]) > 0\n", " for direction in DIRECTIONS\n", " )\n", " return arr_moves_possible\n", "\n", "\n", "np.testing.assert_array_equal(\n", " moves_possible(np.ones((3, 8, 8)) * 1, np.array([[-1, -1]] * 3)),\n", " np.array([True] * 3),\n", ")\n", "\n", "np.testing.assert_array_equal(\n", " moves_possible(get_new_games(3), np.array([[2, 3], [3, 2], [3, 2]])),\n", " np.array([True] * 3),\n", ")\n", "np.testing.assert_array_equal(\n", " moves_possible(get_new_games(3), np.array([[2, 2], [1, 1], [0, 0]])),\n", " np.array([False] * 3),\n", ")\n", "np.testing.assert_array_equal(\n", " moves_possible(np.ones((3, 8, 8)) * -1, np.array([[-1, -1]] * 3)),\n", " np.array([True] * 3),\n", ")\n", "np.testing.assert_array_equal(\n", " moves_possible(np.zeros((3, 8, 8)), np.array([[-1, -1]] * 3)),\n", " np.array([True] * 3),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reword functions\n", "\n", "For any kind of reinforcement learning is a reword function needed.\n", "For otello this would be the final score, the information who won or changes to the score.\n", "A combination of those three would also be possible.\n", "It is probably not be possible to weight the current score to high in a reword function since that would be to close to a classic greedy algorithm.\n", "But some direct influence would increase the learning speed.\n", "In the next section are all three reword functions implemented to be combined and weight later on as needed." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "771 µs ± 215 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "69 µs ± 21.7 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n", "68.6 µs ± 16.5 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" ] } ], "source": [ "def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n", " \"\"\"Evaluates the board at the end of the game.\n", "\n", " All unused fields are added to the score of the player that has more stones with his color up.\n", " This score only applies to the end of the game.\n", " Normally the score is represented by the number of stones each player has.\n", " In this case the score was combined by building the difference.\n", "\n", " Args:\n", " boards: A stack of game bords ot the end of the game.\n", "\n", " Returns:\n", " the combined score for both player.\n", " \"\"\"\n", " score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n", " player_1_won = score1 > score2\n", " player_2_won = score1 < score2\n", " score1_final = 64 - score2[player_1_won]\n", " score2_final = 64 - score1[player_2_won]\n", " score1[player_1_won] = score1_final\n", " score2[player_2_won] = score2_final\n", " return score1 - score2\n", "\n", "\n", "def evaluate_boards(boards: np.ndarray) -> np.ndarray:\n", " \"\"\"Counts the stones each player has on the board.\n", "\n", " Args:\n", " boards: A stack of boards for evaluation.\n", "\n", " Returns:\n", " the combined score for both player.\n", " \"\"\"\n", " return np.sum(boards, axis=(1, 2))\n", "\n", "\n", "def evaluate_who_won(boards: np.ndarray) -> np.ndarray:\n", " \"\"\"Checks who won or is winning a game.\n", "\n", " Args:\n", " boards: A stack of boards for evaluation.\n", "\n", " Returns:\n", " The information who won for both player. 1 meaning the player won, -1 means the opponent lost. 0 represents a patt.\n", " \"\"\"\n", " return np.sign(np.sum(boards, axis=(1, 2)))\n", "\n", "\n", "_boards = get_new_games(EXAMPLE_STACK_SIZE)\n", "%timeit final_boards_evaluation(_boards)\n", "%timeit evaluate_boards(_boards)\n", "%timeit evaluate_who_won(_boards)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Execute a chosen action\n", "\n", "After an evaluation what turns are possible there needs to be a function that executes a turn.\n", "This next sections does that." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "class InvalidTurn(ValueError):\n", " \"\"\"\n", " This error is thrown if a given turn is not valid.\n", " \"\"\"" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "288 ms ± 90.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n", " \"\"\"Executes a single move on a stack o Othello boards.\n", "\n", " Args:\n", " boards: A stack of Othello boards where the next stone should be placed.\n", " moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n", "\n", " Returns:\n", " The new state of the board.\n", " \"\"\"\n", "\n", " def _do_directional_move(\n", " board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n", " ) -> bool:\n", " \"\"\"Changes the color of enemy stones in one direction.\n", "\n", " This function works recursive. The argument step_one should always be used in its default value.\n", "\n", " Args:\n", " board: A bord on which a stone was placed.\n", " rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n", " rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n", " step_one: Set to true if this is the first step in the recursion. False later on.\n", "\n", " Returns:\n", " True if a stone could be flipped.\n", " All changes are made on the view of the numpy array and therefore not included in the return value.\n", " \"\"\"\n", " rec_position = rec_move + rev_direction\n", " if np.any((rec_position >= 8) | (rec_position < 0)):\n", " return False\n", " next_field = board[tuple(rec_position.tolist())]\n", " if next_field == 0:\n", " return False\n", " if next_field == 1:\n", " return not step_one\n", " if next_field == -1:\n", " if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n", " board[tuple(rec_position.tolist())] = 1\n", " return True\n", " return False\n", "\n", " def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n", " \"\"\"Executes a turn on a board.\n", "\n", " Args:\n", " _board: The game board on wich to place a stone.\n", " move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n", "\n", " Returns:\n", " All changes are made on the view of the numpy array.\n", " \"\"\"\n", " if np.all(move == -1):\n", " if not move_possible(_board, move):\n", " raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n", " return\n", "\n", " # noinspection PyTypeChecker\n", " if _board[tuple(move.tolist())] != 0:\n", " raise InvalidTurn(\"This turn is not possible.\")\n", "\n", " action = False\n", " for direction in DIRECTIONS:\n", " if _do_directional_move(_board, move, direction):\n", " action = True\n", " if not action:\n", " raise InvalidTurn(\"This turn is not possible.\")\n", "\n", " # noinspection PyTypeChecker\n", " _board[tuple(move.tolist())] = 1\n", "\n", " boards = boards.copy()\n", " for game in range(boards.shape[0]):\n", " _do_move(boards[game], moves[game])\n", " return boards\n", "\n", "\n", "%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n", "\n", "plot_othello_board(\n", " do_moves(\n", " get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n", " )[0],\n", " action=np.array([2, 3]),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## An abstract reversi game policy\n", "\n", "For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n", "This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "class GamePolicy(ABC):\n", " \"\"\"\n", " A game policy. Proposes where to place a stone next.\n", " \"\"\"\n", "\n", " def __init__(self, epsilon: float):\n", " \"\"\"\n", "\n", " Args:\n", " epsilon: the epsilon / greedy value. Should be between zero and one. Set the mixture of policy and exploration. One means only the policy is used. Zero means only random policies are used. All mixtures inbetween between are possible.\n", " \"\"\"\n", " if 0 > epsilon > 1:\n", " raise ValueError(\"Epsilon should be between zero and one.\")\n", " self._epsilon: float = epsilon\n", "\n", " @property\n", " def epsilon(self):\n", " return self._epsilon\n", "\n", " @property\n", " @abc.abstractmethod\n", " def policy_name(self) -> str:\n", " \"\"\"The name of this policy\"\"\"\n", " raise NotImplementedError()\n", "\n", " @abc.abstractmethod\n", " def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n", " \"\"\"The internal policy is an unfiltered policy. It should only be called from inside this function\n", "\n", " Args:\n", " boards: A board where a policy should be calculated for.\n", "\n", " Returns:\n", " The policy for this board. Should have the same size as the boards array.\n", " \"\"\"\n", " raise NotImplementedError()\n", "\n", " def get_policy(self, boards: np.ndarray) -> np.ndarray:\n", " \"\"\"Calculates the policy that should be followed.\n", "\n", " Calculates the policy that should be followed.\n", " This function does include the usage of epsilon to configure greediness and exploration.\n", "\n", " Args:\n", " boards: A set of boards that show the environment where the policy should be calculated for.\n", "\n", " Returns:\n", " A vector of indices. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n", " \"\"\"\n", " assert len(boards.shape) == 3\n", " assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n", "\n", " if self.epsilon <= 0:\n", " policies = np.random.rand(*boards.shape)\n", " else:\n", " policies = self._internal_policy(boards)\n", " if self.epsilon < 1:\n", " random_choices = self.epsilon <= np.random.rand((boards.shape[0]))\n", " policies[random_choices] = np.random.rand(np.sum(random_choices), 8, 8)\n", "\n", " # todo talk to team about backpropagation of score and epsilon for greedy factor\n", "\n", " # todo possibly change this function to only validate the purpose turn and not all turns\n", " possible_turns = get_possible_turns(boards)\n", " policies[possible_turns == False] = -1.0\n", " max_indices = [\n", " np.unravel_index(policy.argmax(), policy.shape) for policy in policies\n", " ]\n", " policy_vector = np.array(max_indices, dtype=int)\n", " no_turn_possible = np.all(policy_vector == 0, 1) & (policies[:, 0, 0] == -1.0)\n", "\n", " policy_vector[no_turn_possible, :] = IMPOSSIBLE\n", " return policy_vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A first policy\n", "\n", "To quantify the quality of a game AI there needs to be some benchmarks.\n", "The easiest benchmark is to play against a random player.\n", "The easiest player to use as a benchmark is the random player.\n", "For this and testing purpose the random policy was implemented." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "class RandomPolicy(GamePolicy):\n", " \"\"\"\n", " A policy playing a random turn by setting epsilon to 0.\n", " \"\"\"\n", "\n", " def __init__(self, epsilon: float = 0):\n", " _ = epsilon\n", " super().__init__(epsilon=0)\n", "\n", " @property\n", " def policy_name(self) -> str:\n", " return \"random\"\n", "\n", " def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n", " pass\n", "\n", "\n", "rnd_policy = RandomPolicy(1)\n", "assert rnd_policy.policy_name == \"random\"\n", "assert rnd_policy.epsilon == 0\n", "\n", "rnd_policy_result = rnd_policy.get_policy(get_new_games(10))\n", "assert np.any((5 >= rnd_policy_result) & (rnd_policy_result >= 3))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "class GreedyPolicy(GamePolicy):\n", " \"\"\"\n", " A policy playing always one of the strongest turns.\n", " \"\"\"\n", "\n", " def __init__(self, epsilon: float = 1):\n", " _ = epsilon\n", " super().__init__(1)\n", "\n", " @property\n", " def policy_name(self) -> str:\n", " return \"greedy_policy\"\n", "\n", " def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n", " policies = np.random.rand(*boards.shape)\n", " poss_turns = boards == 0 # checks where fields are empty.\n", " poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n", " for game, idx, idy in itertools.product(\n", " range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n", " ):\n", "\n", " if poss_turns[game, idx, idy]:\n", " position = idx, idy\n", " policies[game, idx, idy] += np.sum(\n", " np.array(\n", " list(\n", " _recursive_steps(boards[game, :, :], direction, position)\n", " for direction in DIRECTIONS\n", " )\n", " )\n", " )\n", " return policies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Putting the game simulation together\n", "Now it's time to bring all together for a proper simulation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Playing a single turn\n", "\n", "The next function needed is used to request a policy, verify that the turn is legit and place a stone and turn enemy stones if possible." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.56 s ± 1.02 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "The slowest run took 6.39 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "7.69 s ± 4.77 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def single_turn(\n", " current_boards: np, policy: GamePolicy\n", ") -> tuple[np.ndarray, np.ndarray]:\n", " \"\"\"Execute a single turn on a board.\n", "\n", " Places a new stone on the board. Turns captured enemy stones.\n", "\n", " Args:\n", " current_boards: The current board before the game.\n", " policy: The game policy to be used.\n", "\n", " Returns:\n", " The new game board and the policy vector containing the index of the action used.\n", " \"\"\"\n", " policy_results = policy.get_policy(current_boards)\n", "\n", " # if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\n", " # todo deactivate the policy verification after some testing.\n", " if VERIFY_POLICY:\n", " assert np.all(moves_possible(current_boards, policy_results)), (\n", " current_boards[(moves_possible(current_boards, policy_results) == False)],\n", " policy_results[(moves_possible(current_boards, policy_results) == False)],\n", " np.where(moves_possible(current_boards, policy_results) == False),\n", " )\n", " return do_moves(current_boards, policy_results), policy_results\n", "\n", "\n", "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n", "VERIFY_POLICY = False # type: ignore\n", "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n", "VERIFY_POLICY = True # type: ignore\n", "_turn_result = single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n", "plot_othello_boards(_turn_result[0][:8], _turn_result[1][:8])\n", "del _turn_result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulate a stack of games\n", "This function will simulate a stack of games and return an array of policies and histories." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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in8jgynwyOJaBfpLkyL4gAxkcme9Mhhj7yeo5pZpD2hlueT4ZyOBaBtvzHWN9W7iwP2xnsD2fDGRwlPXt4MK+IIP9+WRwK4MjrG8HF/YFGcjgynxXMnTA6qIUR0qRgQz2M9ieL7lZlPylz14G2/PJQIYjMjjWUfRTamewPZ8MjmWgnyQ5si/IQAZH5juTIcZ+srco1SugvPmjfB9bvXK7TF2TvF7pVuaTgQyuZbA9X5KqVpRKB8JWZrfJUj9JbuwP2xlszycDGQ7nVEfRTymfwfZ8MriVgX6KcmFfkIEMrsx3JUOs/cSJzgEAAAAAAOA7FqUAAEDSeP5+ngoAAECX8frFP1bPKQUAAHqWifvqNWdzpc4sP6ix+0PKjEgNadLWPkG9Wpyte8cXaENhlu2YAAAALXj9Yg+LUuiWJgwer9EDR6h3Vo5q6+tUuneHNu7ebDsWAEhKzY4aUdmgJSUVmlxxUI2e9LaRHpZUIyk3Io37OKQrPgnp6k2VeqUoW9dMK9KOgkzbsYGUk4r91JbwvnqF9zdIjREpI02BPpkK8AsnYJWNfnLx9Uuq9ROLUug2sjKCunjSubp6ylxNHHLcEd/fUPaObl99jx5e96TqG0MWEgJIZancUZdsq9LSkj1qCButkHSrkda3cbuTjHSdpEsqDmrNqh1aMK1ID4/J8zcskIJSuZ8OZZoiaiytVmjjfkU+OvLfmdY/qOCEPsoYnScvnbOcAH6w2U8uvX5J5X7qWf8a9FjHDhiu1378Ny2Zc6NOGDS2zducMGislsy5Ua/9+G8aWTjM54QAUlkqd9Ql26p0+7MV2hE2OlHSlZI2tnPbjZ9+/0RJO8NGdzxbrku2VfkVFUhJqdxPhwpXNqjmvvd0sGRPm7/wSVLko5AOluxRzX3vKVzZ4HNCIPXY7CeXXr+kej+xKAXnHTtguJ773ioVFwyU53kKpAXavF0gLSDP81RcMFDPf/+BHvuiCoBbUrmjRlY2aGnJHm2T9HlJuz+9PtLO7Zuv3y3pTEmlkpaW7NGIHvbiCnBFKvfTocKVDap9aKdMbVNMtze1Tap9aGeP+8UPcInNfnLp9Qv9xKIUHJeVEdSjC+9UbjBH6YHY3m2aHkhXbjBHj12zXFkZwSQnBJDKUr2jbiupUChsNENStaRwjPcLf3r7L0tqCBstKalIVkQgZaV6PzUzTRHVPb5LaohIsX6YlpHUEL2faWrv11QAnWW7n1x5/UI/RcW1KLVs2TJNmDBBeXl5ysvL05lnnqmnnnoqWdkAXTzpXA3pWxxzWTVLD6RraL9BuuiUmUlKBtfQT7AhlTtq4of1mlxxUA9Lel+xv6BrFv70fg9LmlxxUBP31Sc6olPoKPgtlfvpUI2l1TI1TbH/wtfMSKamSY2l1UnJ5RL6CX6z2U8uvX6hn6LiWpQaPHiwfv3rX2vdunV64403NG3aNF1wwQX65z//max8SHFXT5mriOncCnA4EtbVU+YmOBFcRT/BhlTuqDlbKtXoSbeq84ddpyl6/0ZPmrO5MmHZXERHwW+p3E+HCm3cb/X+3QH9BL/Z7CeXXr/QT1Fx7YfZs2fr3HPP1ejRozVmzBjdeOON6t27t9asWZOsfEhhEwaP18Qhx7X7/uKOBNICOmno8ZoweHyCk8FF9BP8luoddWb5Qb396afUdPbg8YiktxT9+OXPVRxMWDYX0VHwU6r3U7Pwvvp2Txocq8hHIYV7+JGc9BP8ZLufXHn9Qj99ptPnlAqHw1q1apXq6up05plntnu7UCik6urqVhcgFqMHjkjI44xK0OOg+6Cf4IdU76ix+0PakqDH2ipp3Cc992PoDxdLR9FP6IpU76dm4f2JORFwTzqhcEfoJySb7X5y5fUL/fSZuBelNm3apN69eysYDOpb3/qWHn30UR133HHt3n7x4sXKz89vuQwZMqRLgZE6emflJORxchP0OHAf/QQ/pXJHecYoMyLVJOjxqiVlRqKP25PF01H0E7oilfuplcYEnQS4oWecTPho6Cf4xWY/OfX6hX5qEfei1NixY7V+/XqtXbtW3/72tzVv3jy988477d5+0aJFqqqqarmUlZV1KTBSR219XUIepyZBjwP30U/wUyp3lPE8NaRJuQl6vDxJDWnRx+3J4uko+gldkcr91EpGgj5oPLPnf2A5/QS/2Ownp16/0E8t4jvdvaTMzEyNGjVKkjRp0iS9/vrr+t3vfqc//vGPbd4+GAwqGOwZHykLf5Xu3ZGYx9nzXkIeB+6jn+CnVO+orX2CGvdxYt5yN07Slr49/7kYT0fRT+iKVO+nZoE+mYl5nILEPI7L6Cf4xXY/ufL6hX76TJeX1SKRiEKh1DkPBPyzcfdmbSh7R+FIvB/UGRWOhLV+1z+16YNEvWsY3Q39hGRK9Y56tThbJ3jSSerap9ecLOl4T1pTlJ2wbN0FHYVkSfV+ahYozFJa/64tnqT1DypQmJWgRN0H/YRksd1Prrx+oZ8+E9d+WLRokV566SXt3LlTmzZt0qJFi/Tiiy9qzpw5ycqHFHf76nuU5nWuLgJpAd2++p4EJ4Kr6CfYkModde/4AmUY6Tp17dNrrpOUYaKP15PRUfBbKvfToYIT+li9f3dAP8FvNvvJpdcv9FNUXD8JH374ob7+9a9r7NixOvvss/X666/rmWee0fTp05OVDynu4XVPquyTcjWFm+K6X1O4Sbs+/kCPvPlUkpLBNfQTbEjljtpQmKVXirJ1iaRhkuL9YOfAp/e7WNIrRdna0AP+0nc0dBT8lsr9dKiM0XnyctOleE/54klebroyRuclJZdL6Cf4zWY/ufT6hX6KiuucUsuXL09WDqBN9Y0hfWXpN/Tc91YpN5ij9EDHP7JN4SbVhOp04ZKrVN/IYcepgn6CDaneUddMK9KaVTv0dNjo84p+Ck0sB+MHFD056DOSMgOerplWlMyYTqCj4LdU76dmXnqacs4fqtqHdkY/pSqWD8nyJGVG7+eld/+TCHeEfoLfbPeTK69f6KeonvGvQI/27oc7dfbNl6m8cq8ktfv+4+bryyv36uybL9N7+973LSOA1JXKHbWjIFMLphVpjKRXJQ3+9Pr2Xlw0Xz/409uPlrRgWpF29ICTdAIuSuV+OlSgIFO9Lxkur3dsf4/3eqer9yXDe8QJhAFX2ewnl16/0E+d+PQ9wIb39r2v0391ni46ZaaunjJXJw09/ojbbNq9RbevvkePvPlUj/nrHoDuIZU76uExefJktLRkjzaFjR6WdKukt9q47URFz8FwsaJ/YfzmtCI9PKZnHHoOuCqV++lQgYJM5f7rSDWWViu0cb8iHx3570zrH1RwQp/oW2p6yBEIgMts9pNLr19SvZ9YlEK3Ud8Y0n1rH9N9ax/ThMHjNWrgCOVm5aimvk7b9+7Qxt2bbUcEkMJSuaMeGpOvdQOytaSkQvMrDmqOJ71tpK2KHhKfJ2mspBO86ElBXy7O1rVTOUIK8Esq99OhvPQ0ZY4vUOb4AoX31Stc2RB9y0xmmgIFmT3iU6yA7sZmP7n0+iWV+4lFKXRLG3dvTpkXUAC6n1TsqB0FmTrvomGauK9eczZX6nMVB3XRJyFlRqSGNGlL36BWFGXr3vEFPf6k5oDLUrGf2hIozOrRv+QB3ZGNfnLx9Uuq9ROLUgAAIGE2FGZpQ+ExLV97xsh48X6sDAAAgH94/WJPz3ozIgAAcAov6AAAQHfD6xf/sCgFAAAAAAAA33nGGOPnwOrqauXn50eH5/j/7kFzoEkykjzJ62Xn3YtkIINLGWzPlyRT1yRJqqqqUl6evU/ist1PkiP7g59JMpChdQYHOop+IoMr88ngWAb6SZIj+4IMZHBkvjMZYuwnq+eUag5pZ7jl+WQgg2sZbM93jPVt4cL+sJ3B9nwykMFR1reDC/uCDPbnk8GtDI6wvh1c2BdkIIMr813J0AGri1IcKUUGMtjPYHu+5GZR8pc+exlszycDGY7I4FhH0U+pncH2fDI4loF+kuTIviADGRyZ70yGGPvJ3qJUr4Dy5o/yfWz1yu0ydU3yeqVbmU8GMriWwfZ8SapaUSodCFuZ3SZL/SS5sT9sZ7A9nwxkOJxTHUU/pXwG2/PJ4FYG+inKhX1BBjK4Mt+VDLH2Eyc6BwAAAAAAgO9YlAIAAAAAAIDvWJQCAAAAAACA76ye6Bzd14TB4zV64Aj1zspRbX2dSvfu0Mbdm32bH95Xr/D+BqkxImWkKdAnU4HCLN/mS/a3AYC2ufDctN1RtucDaBv9FOXCdgDQmgvPSxf6yYUMqYZFKcQsKyOoiyedq6unzNXEIccd8f0NZe/o9tX36OF1T6q+MZTw+aYposbSaoU27lfkoyMfP61/UMEJfZQxOk9eenIOArS9DQC0zYXnpu2Osj0fQNvopygXtgOA1lx4XrrQTy5kSGUsSiEmxw4YrkcX3qkhfYsVMZE2b3PCoLFaMudG3TBzoS5ccpXe2/d+wuaHKxtU9/gumZr2P1Yy8lFIB0v2qP71j5Rz/lAFCjITNl+yvw0AtM2F56btjrI9H0Db6KcoF7YDgNZceF660E8uZEh1LPOhQ8cOGK7nvrdKxQUD5XmeAmmBNm8XSAvI8zwVFwzU899/QCMLhyVkfriyQbUP7ZSpbb8oDmVqm1T70E6FKxsSMl+yvw0AtM2F56btjrI9H0Db6KcoF7YDgNZceF660E8uZACLUuhAVkZQjy68U7nBHKUHYjuwLj2Qrtxgjh67ZrmyMoJdmm+aIqp7fJfUEJFMrHeS1BC9n2lqe9U/Hra3AYC2ufDctN1RtucDaBv9FOXCdgDQmgvPSxf6yYUMiOrSotSvf/1reZ6n73znOwmKA9dcPOlcDelbHHNhNUsPpGtov0G66JSZXZrfWFodPZQy1qJoZiRT06TG0uouzZfsbwN0Dv3U87nw3LTdUbbno3Pop56PfopyYTsgfnRUz+bC89KFfnIhA6I6vSj1+uuv649//KMmTJiQyDxwzNVT5rb7HuOOhCNhXT1lbpfmhzbut3p/yf42QPzop9TgwnPTdkfZvj/iRz+lBvopyoXtgPjQUT2fC89LF/rJhQyI6tSiVG1trebMmaM77rhDffr0SXQmOGLC4PGaOOS4dt9j3JFAWkAnDT1eEwaP79T9w/vq2/z0g3hEPgopvK++0/e3vQ0QP/opNbjw3LTdUbbnI370U2qgn6Jc2A6IDx3V87nwvHShn1zIgM90alFq4cKFOu+88/SlL32pw9uGQiFVV1e3uqB7GD1wREIeZ1QnHye8P3EnAe4s29sA8aOfUoMLz03bHWV7PuJHP6UG+inKhe2A+MTaUfRT9+XC89KFfnIhAz4T3xtJJa1atUpvvvmmXn/99Zhuv3jxYv385z+POxjs652Vk5DHye3s4zQm6ORxDZ1/HOvbAHGhn1KHE89N2x1lez7iQj+lDvopyontgJjF01H0U/flxPPSgX5yIgNaxHWkVFlZma6//nrde++9ysrKiuk+ixYtUlVVVculrKysU0Hhv9r6uoQ8Tk1nHycjQR8Omdn5x7G+DRAz+im1OPHctN1RtucjZvRTaqGfopzYDohJvB1FP3VfTjwvHegnJzKgRVxHSq1bt04ffvihTjnllJbrwuGwXnrpJS1ZskShUEiBQOv3pwaDQQWDfJxrd1S6d0diHmfPe526X6BPZkLmBwo6/zi2twFiRz+lFheem7Y7yvZ8xI5+Si30U5QL2wGxibej6Kfuy4XnpQv95EIGfCauRamzzz5bmzZtanXdFVdcoXHjxumGG2444gUVureNuzdrQ9k7OmHQ2E6dDC8cCWvT7i3a9MGWTs0PFGYprX+wSyehS+sfVKAwtr9Kt8X2NkDs6KfU4sJz03ZH2Z6P2NFPqYV+inJhOyA2dFTqcOF56UI/uZABn4nreLPc3FydcMIJrS45OTnq16+fTjjhhGRlhEW3r75HaV7nDksMpAV0++p7ujQ/OKFrn/zR1ftL9rcBYkM/pR4Xnpu2O8r2/REb+in10E9RLmwHdIyOSi0uPC9d6CcXMiCKN0HiqB5e96TKPilXU7gprvs1hZu06+MP9MibT3VpfsboPHm56ZIX5x09yctNV8bovC7Nl+xvAwBtc+G5abujbM8H0Db6KcqF7QCgNReely70kwsZENXlRakXX3xRt9xySwKiwEX1jSF9Zek3VBOqi7m4msJNqgnV6cIlV6m+sfOHREqSl56mnPOHRk8iF2theJIyo/fz0ru+7mp7G6Dz6KeezYXnpu2Osj0fnUc/9Wz0U5QL2wGdQ0f1XC48L13oJxcyIIotiQ69++FOnX3zZSqv3Csp+l7itjRfX165V2fffJne2/d+QuYHCjLV+5Lh8nrHdgo0r3e6el8yPKEnnrO9DQC0zYXnpu2Osj0fQNvopygXtgOA1lx4XrrQTy5kQJwnOkfqem/f+zr9V+fpolNm6uopc3XS0OOPuM2m3Vt0++p79MibTyX8L1uBgkzl/utINZZWK7Rxf5snpUvrH1RwQp/ooZhJWLm2vQ0AtM2F56btjrI9H0Db6KcoF7YDgNZceF660E8uZEh1LEohZvWNId239jHdt/YxTRg8XqMGjlBuVo5q6uu0fe8Obdy9OanzvfQ0ZY4vUOb4AoX31Stc2SA1RKTMNAUKMn359APb2wBA21x4btruKNvzAbSNfopyYTsAaM2F56UL/eRChlTGohQ6ZePuzVZfPAQKs6yXg+1tAKBtLjw3bXeU7fkA2kY/RbmwHQC05sLz0oV+ciFDquHYMwAAAAAAAPiORSkAAAAAAAD4jkUpAAAAAAAA+M4zxhg/B1ZXVys/Pz86PMf/U1qZA02SkeRJXi87p9QiAxlcymB7viSZuiZJUlVVlfLy8qxkkOz3k+TI/uBnkgxkaJ3BgY6in8jgynwyOJaBfpLkyL4gAxkcme9Mhhj7yeqJzptD2hlueT4ZyOBaBtvzHWN9W7iwP2xnsD2fDGRwlPXt4MK+IIP9+WRwK4MjrG8HF/YFGcjgynxXMnTA6qIUR0qRgQz2M9ieL7lZlPylz14G2/PJQIYjMjjWUfRTamewPZ8MjmWgnyQ5si/IQAZH5juTIcZ+srco1SugvPmjfB9bvXK7TF2TvF7pVuaTgQyuZbA9X5KqVpRKB8JWZrfJUj9JbuwP2xlszycDGQ7nVEfRTymfwfZ8MriVgX6KcmFfkIEMrsx3JUOs/cSJzgEAAAAAAOA7FqUAAAAAAADgOxalAAAAAKQkz98PIgcAHMbqic4BAAAAwC8T99VrzuZKnVl+UGP3h5QZkRrSpK19gnq1OFv3ji/QhsIs2zEBIGWwKIVOCe+rV3h/g9QYkTLSFOiTqYCP/wO3Pd+VDACO5MJz03YG2/MBtM2F52aqZhhR2aAlJRWaXHFQjZ6UMfEkafo4KTdXmTU1OnHLFo3bsF5Xb6rUK0XZumZakXYUZCY1E+CSVO0GFzNMGDxeoweOUO+sHNXW16l07w5t3L3Z1wx+YlEKMTNNETWWViu0cb8iH4WO+H5a/6CCE/ooY3SevPTEvzPU9nxXMgA4kgvPTdsZbM8H0DYXnpupnuGSbVVaWrJHgcygNH++Mq67Tjr55CNul/HWW9Ktt+qMB1ZpzaodWjCtSA+PyUtoFsAlqd4NLmXIygjq4knn6uopczVxyHFHfH9D2Tu6ffU9enjdk6pvPDJjd8aiFGISrmxQ3eO7ZGqa2r1N5KOQDpbsUf3rHynn/KEKJPCvS7bnu5IBwJFceG7azmB7PoC2ufDcTPUMl2yr0u3PVkijRyvt73+Xhg2TIpG2bzxhgnTXXUr/6U+VNn267nh2uzwZPTQmPyFZAJekeje4lOHYAcP16MI7NaRvsSKm7X46YdBYLZlzo26YuVAXLrlK7+17P6EZbOJPpehQuLJBtQ/tlKlt/4l6KFPbpNqHdipc2dAj5ruSAcCRXHhu2s5gez6Atrnw3Ez1DCMrG7S0ZE90QWrNGmnQIMnzpECg7TsEAtHvDxqktLVrpVGjtLRkj0bQl+hhUr0bXMpw7IDheu57q1RcMFCe5ymQ1nY/BdIC8jxPxQUD9fz3H9DIwmEJy2BbXItSP/vZz+R5XqvLuHHjkpUNDjBNEdU9vktqiEixfjiJkdQQvZ9paucvUd1kvisZ0DH6KfW48Ny0ncH2fMSOjkotLjw3ySDdVlKhtMxg9Aip3FwpIyO2O2ZkSLm5Snv2WQUyg1pSUtGlHK6jn1KL7eclGT6TlRHUowvvVG4wR+mB2N7Elh5IV24wR49ds1xZGcEuZ3BB3EdKHX/88aqoqGi5vPzyy8nIBUc0llZHD2WM99NyjWRqmtRYWt2t57uSAbGhn1KLC89N2xlsz0d86KjU4cJzM9UzTPywXpMrDirjsq9F37IX64JUs4wMafhwpX/1Mk2uOKiJ++o7naU7oJ9SR6p3g0sZLp50rob0LY55QapZeiBdQ/sN0kWnzOxyBhfEvSiVnp6uY445puXSv3//ZOSCI0Ib96f0/V3JgNjQT6nFheem7Qy274/40FGpw4XnZqpnmLOlUo2epOuua/8cUh0Jh6Vrr1WjJ83ZXNnpLN0B/ZQ6Ur0bXMpw9ZS57Z5DqiPhSFhXT5nb5QwuiHtRqrS0VMXFxRo5cqTmzJmjXbt2JSMXHBDeV9/mpw/EI/JRSOFO/mXJ9nxXMiB29FPqcOG5aTuD7fmIHx2VGlx4bpJBOrP8oDImnhT9lL32ziHVkUBAmjRJGRNP0ucqDnbuMboJ+ik12H5ekuEzEwaP18Qhx7V7DqmOBNICOmno8ZoweHynM7girkWpM844QytXrtTTTz+tZcuWaceOHfqXf/kX1dTUtHufUCik6urqVhd0D+H9iTsJb3ec70oGxIZ+Si0uPDdtZ7A9H/GJt6Pop+7LhecmGaSx+0NSos6LNHasxn3Ssz6C/VD0U+qw/bwkw2dGDxyRkAyjEvQ4NsX15sWZMz97z+KECRN0xhlnaNiwYXrwwQd11VVXtXmfxYsX6+c//3nXUsKOxgSdALehk49je74rGRAT+inFuPDctJ3B9nzEJd6Oop+6MReemymewTNGmRFFT26eCHl5yoxEH9d4XmIe0yH0UwpJ8W5wKUPvrJyERMhN0OPYFPfb9w5VUFCgMWPGaPv27e3eZtGiRaqqqmq5lJWVdWUk/JTRpR+Pz2R28nFsz3clAzqFfurhXHhu2s5gez66pKOOop+6MReemymewXieGtIkHeVo6bhUV6shTT1yQaot9FMPluLd4FKG2vq6hESoSdDj2NSlvVFbW6t3331XRUVF7d4mGAwqLy+v1QXdQ6BPZmIep6Bzj2N7visZ0Dn0U8/mwnPTdgbb89E1HXUU/dR9ufDcJIO0tU9Q2rIlIRm0ZYu29O0ZH70eC/qp57L9vCTDZ0r37khIhtI97yXkcWyKa1Hqe9/7nlavXq2dO3fqH//4h77yla8oEAjo8ssvT1Y+WBQozFJa/679Dzitf1CBwqxuOd+VDIgN/ZRaXHhu2s5gez7iQ0elDheem2SQXi3OVuOG9dJbb0U/Ra8zwmFp3To1btygNUXZnXuMboB+Sh22n5dk+MzG3Zu1oewdhSOd66dwJKz1u/6pTR8kaPHdorgWpXbv3q3LL79cY8eO1Ve/+lX169dPa9asUWFhYbLywbLghD4pfX9XMqBj9FPqceG5aTuD7fsjdnRUanHhuZnqGe4dX6AMI+nWW6W0Tr45JBCQbrtNGSb6eD0V/ZRaUr0bXMpw++p7lOZ1rp8CaQHdvvqeLmdwQVwnOl+1alWycsBRGaPzVP/6RzK1TZKJ446e5PVOV8borh3Oa3u+KxnQMfop9bjw3LSdwfZ8xI6OSi0uPDdTPcOGwiy9UpStMx5YpfSf/lQaNEjKyIj9ARobpQ8+UNODD2htUbY29OCjSumn1JLq3eBShofXPakbZi5UccFApQdiX5ppCjepvHKvHnnzqS5ncAFnN8VReelpyjl/aPQkbrGe29GTlBm9n5f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G06tXL7Np06ZOb/P73//ePPPMM6asrMwsW7bMjBs3zpx99tmmubm59e8lmaFDh5qnnnrKrF271nz5y182ffv2NR999FFMsydPnmyysrJMbm6u6dmzp5FkxowZYyoqKtpsp7S01Pzf//2fMcaYFStWGEnm0KFDJ/y3A4hOtB1ljDH33Xefyc3NNZJMSUmJ2bZt2wlnxaOjjDHml7/8pVmxYoXZsGGDuf/++01hYaH5/ve/f8LZu3fvNtnZ2SfskmHDhpns7GyTm5trcnJyjCQzceLENvf51re+ZXw+n5k4caJ57bXXzIoVK8wZZ5xhpkyZcsL5ACIXbT898MAD5oUXXjBlZWVm0aJFZtCgQeaqq6464ax49FMwGDQXX3yx+Y//+A9jjDEVFRVGknnnnXdOODte/XTgwAHzy1/+0qxevdq8+eab5rbbbjMej8c888wzJ5wPIHKp9hqvxQ9/+MPW12Gf/vSnzYcffnjC2fHqp2MdPXrU9O7d2/znf/7nCWcj+ViU6qbKy8uNJPP3v//d/OUvfzGnnHKKMcaYPXv2GJ/PZ44ePWr+/d//3YwcObL1Ps8884wZNWqUqaura/1dOIX13HPPtdlOON5//30jybz88svGGGMef/xxI8k88MADrbdpaGgw/fr1M//7v/8b0+zJkyeb2bNnm/LyclNeXm5ef/11c8kll5jRo0eb2tpaY4wx3//+9821117beh8WpYDEiqajWlRXV5v33nvPrFy50lx22WXmrLPOMkePHu10Vjw6qiMPP/ywyczMNA0NDZ3e5je/+Y2ZOnXqCWcNGzbM/Nu//ZspLy837733nnnllVfMxIkTzXnnndf6pO6GG24wkszWrVtb77du3TojyWzZsiWifxuAE4uln47V8sLwRAvn8einX//612bSpEmtfRHuolS8+qkjX/3qV81nPvOZiP5dALqWaq/xWhw4cMBs3brV/PWvfzWTJk0yl1xyiQkGg51uJxH9tHjxYpOZmWn27t0b0b8JicdnlLqpUaNGafDgwVqxYoVWrFjReoG44uJiDRkyRH//+9+1YsUKTZ06tfU+y5cv1/vvv6/CwkJlZmYqMzN0ybEZM2a0+0aHYz377LO6/PLLI8o3cuRI9evXT9u2bZOk1s81H3sNKZ/Pp5EjR2rnzp0xzy4oKNCoUaM0atQoTZo0SQ8//LDKy8v1xBNPSAr92//whz+0/rsvvPBCSVK/fv30ox/9KKJ/G4CuRdNRLQoKCjR69Gidf/75euqpp7RlyxYtW7as01nx6KiOTJw4Uc3Nzdq+fXvMs/v166dRo0Zp9OjRmjp1qu6+++7WfSCFOjIzM1Njxoxpvc+4ceMk6YQdCSBysfTTsVo+QnKiHolHPy1fvlyrVq2Sz+dTZmamRo0aJUn61Kc+peuuuy7m2V31U0cmTpx4wn83gOik2mu8Fv369dOYMWM0bdo0LVmyRM8999wJv6EzEf300EMP6Qtf+MIJr48FO1iU6samTJmiV199Va+++mqbwjn//PP1/PPP680332zzWePbb79dZWVlWr9+feuPJP3qV7/SwoULO5xhjNGf/vSniD7vK0m7d+/WRx991LoYNWHCBPl8Pm3durX1Nk1NTdq+fbuGDRsW19mS5PV6JYU+cy2Fvvlvw4YNrf/ulq89/dvf/qa5c+dGvH0AXYu0ozpiQu/4ld/v7/Tv49FRHVm/fr0yMjI0YMCADv/+8OHDWrFiRVw6atKkSWpubm7z1c7vvfeeJHXakQCiF49+anke1VmPxKuf7rnnnjbPYZ577jlJ0hNPPKE777yzw23Es586sn79+rAvpAwgMqn0Gq8jwWBQkjp97paIfqqoqNCKFSvafKEWHGLxXVpIsEceecT06NGj3dsUH3vsMZOXl2ckmcrKyhNuQ128tfOtt94yvXv3Nk1NTZ3epq6uztx6661m1apVpqKiwrz88svmrLPOMqNHj27zsZfvfve7ZtCgQebFF180W7ZsMddff70ZMGCAOXjwYNSzjQl9fO+GG24wVVVVpqqqyqxfv97MmDHD5OTkdPqxFz6+ByRepB31/vvvm5///Odm7dq1ZseOHeaNN94wl112menTp4/Zt29fhzPi1VF///vfza9+9Suzfv168/7775tFixaZ/v37m6997WudbvcPf/iDOf3007vcD8OGDTM//elPTVVVlamsrDRr1qwxkydPNv3792+95kIgEDBnnXWWOf/8883bb79t1q5dayZOnGimTZvW5fYBRC7Sftq2bZv56U9/atauXWsqKirMM888Y0aOHNl6jZeOxPM51LHC+fhePPvp0UcfNYsXLzabN282mzdvNnfeeafJyMgwjzzySJfbBxC5VHqNt3r1anPvvfead955x2zfvt288sor5rzzzjMnn3xypx0Wz35qcccdd5ji4uITfuwY9rAo1Y21PCkZO3Zsm99v37699QLBXemqsO644w4zc+bME27jyJEj5qKLLjL9+/c3WVlZZtiwYeaGG25o93nexsZG8y//8i9mwIABJi8vz3zuc5874YX1wpltTGhRSlLrT+/evc3kyZPN8uXLO70Pi1JA4kXaUXv27DHTp083AwYMMFlZWWbw4MHmK1/5ygmvqRSvjlq3bp2ZOHGiKSgoMDk5OWbcuHHm5z//+QmvJzVr1izzb//2byecbUzoSdWxHdW/f39zySWXtHtBuWfPHnP11VebXr16mYEDB5rZs2ef8IsgAEQv0n7auXOnOf/8802fPn2Mz+czo0aNMj/4wQ9MTU1NpzPi+Ryqo+wnWpSKZz89+uijZty4caZnz54mPz/fnHPOOeYPf/hDl9sGEJ1Ueo1XVlZmpkyZ0tqNw4cPN9/+9rfN7t27O91uvJ8/BQIBM3jwYPOv//qvXW4TdniMMSbx78dCd1VaWqo77rhDX/rSl9JqNoDUYKsnmpubNXDgQD3//PM655xzkjobQGqgnwC4in5CMnFNKUStsbFRM2bM0PTp09NqNoDUYLMnDh48qO9///s6++yzkz4bgPvoJwCuop+QbLxTCgAAAAAAAEnHO6UAAAAAAACQdCxKAQAAAAAAIOlYlAIAAAAAAEDSsSgFAAAAAACApGNRCgAAAAAAAEnHohQAAAAAAACSjkUpAAAAAAAAJB2LUgAAAAAAAEg6FqUAAAAAAACQdCxKAQAAAAAAIOlYlAIAAAAAAEDSsSgFAAAAAACApGNRCgAAAAAAAEmXmeyBwWBQlZWVysvLk8fjSfZ4AA4yxqiurk7FxcXKyLC3Vk4/AeiICx1FPwHoCP0EwFXh9lPSF6UqKys1ZMiQZI8FkAJ27dqlwYMHW5tPPwE4EZsdRT8BOBH6CYCruuqnpC9K5eXlffKHnt5kj5eOBOzOJwMZXMtge/4xGdr0gwXW+0ly6vGwlsH2fDKQoZMMNjuKfiKDM/PJ4GQG+smdx4IMZLA+37EMXfVT0helWt/S2dOrgq+PTvZ41T66Taa+WZ7cTOXPHpX0+WQgg2sZbM+XpJqF5dKRgPW3fNvuJ8mNx8N2BtvzyUCG47nQUfQTGVyZTwa3MtBPIS48FmQggyvzXckQbj9xoXMAAAAAAAAkHYtSAAAAAAAASDoWpQAAAAAAAJB0LEoBAAAAAAAg6ViUAgAAAAAAQNKxKAUAAAAAAICkY1EKAAAAAAAASceiFAAAAAAAAJIu4kWp1157TZdddpmKi4vl8Xj09NNPJyAWAESOfgLgKvoJgKvoJwA2RbwoVV9fr/Hjx+u+++5LRB4AiBr9BMBV9BMAV9FPAGzKjPQO06dP1/Tp0xORBQBiQj8BcBX9BMBV9BMAmyJelIqU3++X3+9v/XNtbW2iRwJAWOgnAK6inwC4in4CEE8Jv9D5/PnzVVBQ0PozZMiQRI8EgLDQTwBcRT8BcBX9BCCeEr4oNW/ePNXU1LT+7Nq1K9EjASAs9BMAV9FPAFxFPwGIp4R/fM/n88nn8yV6DABEjH4C4Cr6CYCr6CcA8ZTwd0oBAAAAAAAAx4v4nVKHDx/Wtm3bWv9cUVGh9evXq0+fPho6dGhcwwFAJOgnAK6inwC4in4CYFPEi1Jr167VlClTWv/8z//8z5Kk6667To8++mjcggFApOgnAK6inwC4in4CYFPEi1IXXHCBjDGJyAIAMaGfALiKfgLgKvoJgE1cUwoAAAAAAABJx6IUAAAAAAAAko5FKQAAAAAAACQdi1IAAAAAAABIOhalAAAAAAAAkHQsSgEAAAAAACDpWJQCAAAAAABA0nmMMSaZA2tra1VQUBAanpuZzNGSJHOkWTKSPJKnZ/Lnk4EMrmWwPV+STH2zJKmmpkb5+flWMkj2+0ly5PHgmCQDGdpmcKCj6CcyuDKfDI5loJ8kOfJYkIEMjsx3JkOY/WQn3cdaQtoZbnk+GcjgWgbb8x1jfV+48HjYzmB7PhnI4Cjr+8GFx4IM9ueTwa0MjrC+H1x4LMhABlfmu5KhC1YXpXinFBnIYD+D7fmSm0XJf+mzl8H2fDKQoV0GxzqKfkrvDLbnk8GxDPSTJEceCzKQwZH5zmQIs5/sLUr19Cp/9qikj619dJtMfbM8PTOtzCcDGVzLYHu+JNUsLJeOBKzM7pClfpLceDxsZ7A9nwxkOJ5THUU/pX0G2/PJ4FYG+inEhceCDGRwZb4rGcLtJy50DgAAAAAAgKRjUQoAAAAAAABJx6IUAAAAAAAAks7qhc6RukoHj9PogSPUKydXhxvqVb6vQmW7N9uOBQD0EwBn0U8AXEU/wRYWpRC2nCyfZky4RHMmz9L4Iae0+/sNu97VgysXaem659TQ5LeQEEC6op8AuIp+AuAq+gku4ON7CMvJA4brzTv+ogUz79Rpg0o6vM1pg0q0YOadevOOv2hk/2FJTgggXdFPAFxFPwFwFf0EV7AohS6dPGC4Xr51iYoLB8rj8cib4e3wdt4Mrzwej4oLB+qVHzxBcQFIOPoJgKvoJwCuop/gEhalcEI5WT4tm/uQ8ny5yvSG92nPTG+m8ny5evqmh5WT5UtwQgDpin4C4Cr6CYCr6Ce4JqJFqfnz5+vss89WXl6eBgwYoCuvvFJbt25NVDY4YMaESzSkT3HYhdUi05upoX0H6eqzpicoGdAW/ZR+6CekEjoqvdBPSCX0U3qhn+CaiBalVq5cqblz52r16tV66aWX1NTUpIsuukj19fWJygfL5kyepaAJRnXfQDCgOZNnxTkR0DH6Kf3QT0gldFR6oZ+QSuin9EI/wTURLY++8MILbf786KOPasCAAVq3bp3OP//8uAaDfaWDx3X4LQzh8mZ4dcbQU1U6eBxfJ4qEo5/SC/2EVENHpQ/6CamGfkof9BNcFNM1pWpqaiRJffr06fQ2fr9ftbW1bX6QGkYPHBGX7YyK03aASNBP3Rv9hFTXVUfRT6mLfkKqo5+6L/oJLop6USoYDOp73/ueJk2apNNOO63T282fP18FBQWtP0OGDIl2JJKsV05uXLaTF6ftAOGin7o/+gmpLJyOop9SF/2EVEY/dW/0E1wU9aLU3LlztWnTJi1ZsuSEt5s3b55qampaf3bt2hXtSCTZ4Yb4fI68Lk7bAcJFP3V/9BNSWTgdRT+lLvoJqYx+6t7oJ7goskvuf+ymm27Sn//8Z7322msaPHjwCW/r8/nk8/G1kamofF9FfLaz94O4bAcIB/2UHugnpKpwO4p+Sl30E1IV/dT90U9wUUTvlDLG6KabbtKyZcu0fPlyjRjBZ0m7s7Ldm7Vh17sKBANR3T8QDGj9zn9o454tcU4GtEc/pRf6CamGjkof9BNSDf2UPugnuCiiRam5c+dq0aJFWrx4sfLy8rR3717t3btXR48eTVQ+WPbgykXK8ET3KU9vhlcPrlwU50RAx+in9EM/IZXQUemFfkIqoZ/SC/0E10R0NN5///2qqanRBRdcoKKiotafJ554IlH5YNnSdc9p18FKNQeaI7pfc6BZOz/aoz++/XyCkgFt0U/ph35CKqGj0gv9hFRCP6UX+gmuieiaUsaYROWAoxqa/Lrqvm/q5VuXKM+Xq0xv14dMc6BZdf56XbngejU0+ZOQEqCf0hH9hFRCR6UX+gmphH5KL/QTXBP1t+8hfby/f7suvOtaVVbvk6ROP4Pc8vvK6n268K5r9cGBHUnLCCA90U8AXEU/AXAV/QSXRPXte0g/HxzYoXN+dqmuPmu65kyepTOGntruNht3b9GDKxfpj28/zwo6gKShnwC4in4C4Cr6Ca5gUQpha2jya/Gap7V4zdMqHTxOowaOUF5Oruoa6rVtX4XKdm+2HRFAmqKfALiKfgLgKvoJLmBRClEp272ZkgLgJPoJgKvoJwCuop9gC9eUAgAAAAAAQNKxKAUAAAAAAICkY1EKAAAAAAAASecxxphkDqytrVVBQUFoeG7yL2lljjRLRpJH8vS0c0ktMpDBpQy250uSqW+WJNXU1Cg/P99KBsl+P0mOPB4ck2QgQ9sMDnQU/UQGV+aTwbEM9JMkRx4LMpDBkfnOZAizn6xe6LwlpJ3hlueTgQyuZbA93zHW94ULj4ftDLbnk4EMjrK+H1x4LMhgfz4Z3MrgCOv7wYXHggxkcGW+Kxm6YHVRindKkYEM9jPYni+5WZT8lz57GWzPJwMZ2mVwrKPop/TOYHs+GRzLQD9JcuSxIAMZHJnvTIYw+8neolRPr/Jnj0r62NpHt8nUN8vTM9PKfDKQwbUMtudLUs3CculIwMrsDlnqJ8mNx8N2BtvzyUCG4znVUfRT2mewPZ8MbmWgn0JceCzIQAZX5ruSIdx+4kLnAAAAAAAASDoWpQAA3ZInud/jASBF0A0AALjD6jWlAACIl/EHGjRzc7XOrTyqkkN+ZQelxgxpa2+fVhX30OPjCrWhf47tmACSjG4AAMBdLEohJZUOHqfRA0eoV06uDjfUq3xfhcp2b05qhsCBBgUONUpNQSkrQ97e2fIm+UmtCxkA20ZUN2rB8ipNqjqqJo+0yUhLJdVJygtKYz/y6+sH/ZqzsVpvFPXQTVOLVFGYndBMts9N2/NdyYD0dnw3ZI0/Q5o2VsrLU3ZdnU7fskVjN6xPq24gg1sZgBa8tiGDaxmSiUUppIycLJ9mTLhEcybP0vghp7T7+w273tWDKxdp6brn1NDkT0gG0xxUU3mt/GWHFPyw/YyMfj75Snsra3S+PJmJ+XSsCxkAV1zzXo3uW75XjQGjhZLuMdL6Dm53hpFukXRN1VGtXlKhG6cWaemY/LhmsX1u2p7vSgZA+qQbvNk+afZsZd1yi3Tmme1ul/XOO9I992jiE0u6bTeQwa0MQAte25DBtQy2sCiFlHDygOFaNvchDelTrKAJdnib0waVaMHMO3Xb9Lm6csH1+uDAjrhmCFQ3qv7ZnTJ1nX+1ZfBDv44u36uGtz5U7uVD5Y3zf3F1IQPgimveq9GDL1XpPUkXS9qhzi+UWCbpG5J+IunFgNFvX6qUR0ZPjSmISxbb56bt+a5kAKRPukGjRyvjr3+Vhg2Tgh0/d1BpqfTII8r80Y+UMW2afvvStm7VDWRwKwPQgtc2ZHAtg03da4kN3dLJA4br5VuXqLhwoDwej7wZ3g5v583wyuPxqLhwoF75wRMa2X9Y3DIEqht1+KntMoc7L4pjmcPNOvzUdgWqG7tVBsAVI6sbdd/yvXpP0nmSdn/8+05edrb+frekcyWVS7pv+V6NiMP5YfvctD3flQyA9Ek3aPRoZaxeLQ0aJHk8krfj5w7yekN/P2iQMtaskUaN6jbdQAa3MgAteG1DBtcy2MaiFJyWk+XTsrkPKc+Xq0xveG/sy/RmKs+Xq6dvelg5Wb6YM5jmoOqf3Sk1BqVwv7DHSGoM3c80d/YyObUyAC65d3mV/AGjiyXVSgqEeb/Ax7f/vKTGgNGC5VUx5bB9btqe70oGoMW9y6uUke0LvUMqL0/KygrvjllZUl6eMl56Sd5sX8p3AxncygC04LUNGVzL4IKIFqXuv/9+lZaWKj8/X/n5+Tr33HP1/PPPJyoboBkTLtGQPsVhl3aLTG+mhvYdpKvPmh5zhqby2tBbKSP9BmkjmbpmNZXXdosMrqOf0sf4/Q2aVHVUSxX6yF64C1ItAh/fb6mkSVVHNf5AQ9RZbJ+btue7kiEV0FGJ19INWdf+U+gje+EuSLXIypKGD1fml65N+W4gg1sZXEc/JQ+vbcjgWgYXRLQoNXjwYP3iF7/QunXrtHbtWk2dOlVXXHGF/vGPfyQqH9LcnMmzOv2cdVcCwYDmTJ4VcwZ/2SGr93clg+vop/Qxc0u1mjzSPYr+7b4ZCt2/ySPN3FwddRbb56bt+7uSIRXQUYnX0g265ZbOryHVlUBAuvnmlO8GMriVwXX0U/Lw2oYMrmVwQUTP5y+77DJdcsklGj16tMaMGaM777xTvXr10urVqxOVD2msdPA4jR9ySqefs+6KN8OrM4aeqtLB46LOEDjQ0OG3H0Qi+KFfgRj+a6sLGVIB/ZQ+zq08qk0ff8tetG9aDkp6R9ImI3266mhU27B9btqe70qGVEFHJd65lUeVNf6M0LfsdXYNqa54vdKECcoaf0bKdgMZ3MqQCuin5OC1DRlcy+CKqK8pFQgEtGTJEtXX1+vcc8/t9HZ+v1+1tbVtfoBwjB44Ii7bGRXDdgKH4nch4lTOkGrop+6t5JBfW+K0ra2Sxh6M7gmB7XPT9nxXMqSicDqKfopcySG/NHZsnDZWkrLdQAa3MqQa+ilxeG1DBtcyuCLiRamNGzeqV69e8vl8+va3v61ly5bplFNO6fT28+fPV0FBQevPkCFDYgqM9NErJzcu28mLZTtNcbp4XGMM23EhQ4qgn7o/jzHKDkp1cdperaTsYGi7EbN9btqe70qGFBJJR9FPkWnpBuXlxWeD+fmp2w1kcCtDiqCfEo/XNmRwLoMjIl6UKikp0fr167VmzRp95zvf0XXXXad3332309vPmzdPNTU1rT+7du2KKTDSx+GG+rhspy6W7WTF6Qsqs2PYjgsZUgT91P0Zj0eNGVKcXnYqX1JjRmi7EbN9btqe70qGFBJJR9FPkWnpBtXFacm6tjZ1u4EMbmVIEfRT4vHahgzOZXBEZJf9l5Sdna1Ro0ZJkiZMmKC33npLv/71r/XAAw90eHufzyefL/avrkT6Kd9XEZ/t7P0g6vt6e2fHJYO3MPrtuJAhVdBP6WFrb5/GfhTbZ/BbjJW0pU90x4Dtc9P2fFcypJJIOop+itzW3j6dviVOH+7dsiVlu4EMbmVIFfRT4vHahgyuZXBFzMtqwWBQfn98XhwAxyrbvVkbdr2rQDDSL3wPCQQDWr/zH9q4J/onqN7+OcroF9v/6Wb088nbPyelM6Qq+ql7WlXcQ6d5pDMU27fvnSnpVI+0uqhHVNuwfW7anu9KhlRGR8XXquIeatqwXnrnndC36EUjEJDWrVNT2YaU7QYyuJUhVdFP8cdrGzK4lsEVET2fnzdvnl577TVt375dGzdu1Lx58/Tqq69q5syZicqHNPfgykXK8ET3stOb4dWDKxfFnMFX2tvq/V3J4Dr6KX08Pq5QWUa6RbF9+94tkrJMaHvRsn1u2r6/KxlSAR2VeC3doHvukTKiXLL2eqV77035biCDWxlcRz8lD69tyOBaBhdEdEbs379fX/va11RSUqILL7xQb731ll588UVNmzYtUfmQ5paue067DlaqOdAc0f2aA83a+dEe/fHt52POkDU6X568TCnSy0p4JE9eprJG53eLDK6jn9LHhv45eqOoh66RNExSpF+s7P34fjMkvVHUQxti+C9Mts9N2/NdyZAK6KjEa+mG5ieWSDt2SE1NkW2gqUnavl3NTz6R8t1ABrcyuI5+Sh5e25DBtQwuiGhR6uGHH9b27dvl9/u1f/9+vfzyy5QVEqqhya+r7vum6vz1YZd3c6BZdf56XbngejU0xf62Y09mhnIvHxq6iFy4heGRlB26nycz9ovPuZDBdfRTerlpapGyvR69oNDFysNdmPJ+fPsXJWV7PbppalFMOWyfm7bnu5IhFdBRyXHT1CIFGv0KXnRR6KLn4S5MNTVJdXUKTpumQKM/5buBDG5lcB39lDy8tiGDaxlc0D3+FejW3t+/XRfeda0qq/dJUqefw275fWX1Pl1417X64MCOuGXwFmar1zXD5ekV3ncDeHplqtc1w+N64TkXMgCuqCjM1o1TizRG0ipJgz/+fWf/p9by+8Ef3360pBunFqkiDueH7XPT9nxXMgDSJ92g8nIFJ06U9uwJ/UVn15hq+f2ePaHbb9vWbbqBDG5lAFrw2oYMrmWwLeJv3wNs+ODADp3zs0t19VnTNWfyLJ0x9NR2t9m4e4seXLlIf3z7+bj8V4TjeQuzlfeVkWoqr5W/7JCCH7afkdHPJ19p79BbMROwcu1CBsAVS8fkyyOj+5bv1caA0VJJ90h6p4PbjlfoGlIzFHqH1A1Ti7R0TPze8mz73LQ935UMgHRsN7wv7ynjlPmla6Wbb5YmTGh/4/XrpXvvVfOTTyjQ6NeN04q7VTeQwa0MQAte25DBtQw2sSiFlNHQ5NfiNU9r8ZqnVTp4nEYNHKG8nFzVNdRr274Kle3enPAMnswMZY8rVPa4QgUONChQ3Sg1BqXsDHkLs5Py7QcuZABc8dSYAq0b0EMLlldpdtVRzfRIm4y0VVKtQh/VK5F0mid0UfPXi3vo5inxeRfE8Wyfm7bnu5IBkNp2w6THHlPT7x5T1vgzpJISKT9fqq2Vtm5V04b1yjLS6uIeunnKiG7ZDWRwKwPQgtc2ZHAtgy0sSiElle3enJSiPhFv/xzr5eBCBsC2isJsXXr1MI0/0KCZm6v16aqjuvqgX9lBqTFD2tLHp4VFPfT4uMKYLlwcCdvnpu35rmRAemvXDbs3a2zZ+jbdsPq0wrTqBjK4lQFowWsbMriWIZlYlAIAdAsb+udoQ/+TWv/sMUbGE+nXmQDobugGAADc1b0+jAgAwMd40QmgI3QDAADuYFEKAAAAAAAASecxxphkDqytrVVBQUFoeG7yPz1ojjRLRpJH8vS08+lFMpDBpQy250uSqW+WJNXU1Cg/P37fehQp2/0kOfJ4cEySgQxtMzjQUfQTGVyZTwbHMtBPkhx5LMhABkfmO5MhzH6yek2plpB2hlueTwYyuJbB9nzHWN8XLjwetjPYnk8GMjjK+n5w4bEgg/35ZHArgyOs7wcXHgsykMGV+a5k6ILVRSneKUUGMtjPYHu+5GZR8l/67GWwPZ8MZGiXwbGOop/SO4Pt+WRwLAP9JMmRx4IMZHBkvjMZwuwne4tSPb3Knz0q6WNrH90mU98sT89MK/PJQAbXMtieL0k1C8ulIwErsztkqZ8kNx4P2xlszycDGY7nVEfRT2mfwfZ8MriVgX4KceGxIAMZXJnvSoZw+4kLnQMAAAAAACDpWJQCAAAAAABA0rEoBQAAAAAAgKSzeqFzpK7SweM0euAI9crJ1eGGepXvq1DZ7s1Jmx840KDAoUapKShlZcjbO1ve/jlJm+9KBgDtuXBu2s5gez6Ajtl+/iS50Q8uZADQFv3kToZ0w6IUwpaT5dOMCZdozuRZGj/klHZ/v2HXu3pw5SItXfecGpr8cZ9vmoNqKq+Vv+yQgh+2335GP598pb2VNTpfnszEvAnQhQwA2nPh3LSdwfZ8AB2z/fxJcqMfXMgAoC36yZ0M6YxFKYTl5AHDtWzuQxrSp1hBE+zwNqcNKtGCmXfqtulzdeWC6/XBgR1xmx+oblT9sztl6jr/Wsngh34dXb5XDW99qNzLh8pbmB23+a5kANCeC+em7Qy25wPomO3nT5Ib/eBCBgBt0U/uZEh3LPOhSycPGK6Xb12i4sKB8ng88mZ4O7ydN8Mrj8ej4sKBeuUHT2hk/2FxmR+obtThp7bLHO68KI5lDjfr8FPbFahujMt8VzIAaM+Fc9N2BtvzAXTM9vMnyY1+cCEDgLboJ3cygEUpdCEny6dlcx9Sni9Xmd7w3liX6c1Uni9XT9/0sHKyfDHNN81B1T+7U2oMSibcO0lqDN3PNHe86p9qGQC058K5aTuD7fkAOmb7+ZPkRj+4kAFAW/STOxkQEtOi1C9+8Qt5PB5973vfi1McuGbGhEs0pE9x2IXVItObqaF9B+nqs6bHNL+pvDb0Vspwi6KFkUxds5rKa2Oa70oGRI5+6v5cODdtZ7A9H9Ghn7o/28+fJDf6wYUMiBwd1b3RT+5kQEjUi1JvvfWWHnjgAZWWlsYzDxwzZ/KsTj9j3JVAMKA5k2fFNN9fdsjq/V3JgMjQT+nBhXPTdgbb90fk6Kf0YPv5k+RGP7iQAZGho7o/+smdDAiJalHq8OHDmjlzpn7729+qd+/e8c4ER5QOHqfxQ07p9DPGXfFmeHXG0FNVOnhcVPcPHGjo8NsPIhH80K/AgYao7+9CBkSGfkoPLpybtjPYno/I0U/pwfbzJ8mNfnAhAyJDR3V/9JM7GfCJqBal5s6dq0svvVSf+9znuryt3+9XbW1tmx+khtEDR8RlO6Oi3E7gUPwuAhz1fR3IgMjQT+nBhXPTdgbb8xE5+ik92H7+JLnRDy5kQGTC7Sj6KXXRT+5kwCci+yCppCVLlujtt9/WW2+9Fdbt58+fr5/85CcRB4N9vXJy47KdvGi30xSni8c1xrAdFzIgbPRTGnHh3LSdwfZ8RIR+Sh/Wnz9JbvSDCxkQtkg6in5KXfSTQxnQKqJ3Su3atUvf/e539fjjjysnJyes+8ybN081NTWtP7t27YoqKJLvcEN9XLZTF+12suL05ZDZMWzHhQwIC/2UZlw4N21nsD0fYaOf0ov150+SG/3gQgaEJdKOop9SF/3kUAa0iuidUuvWrdP+/ft11llntf4uEAjotdde04IFC+T3++X1tv18qs/nk88X+9dGIvnK91XEZzt7P4jqft7e2XGZ7y2MfjsuZEB46Kf04sK5aTuD7fkIH/2UXmw/f5Lc6AcXMiA8kXYU/ZS66Cd3MuATES1KXXjhhdq4cWOb333961/X2LFjddttt7V7QoXUVrZ7szbselenDSqJ6mJ4gWBAG3dv0cY9W6Ka7+2fo4x+vpguQpfRzydv//D+q7SrGRAe+im9uHBu2s5gez7CRz+lF9vPnyQ3+sGFDAgPHZU+6Cd3MuATEb3fLC8vT6eddlqbn9zcXPXt21ennXZaojLCogdXLlKGJ7q3JXozvHpw5aKY5vtKY/vmj1jv70oGdI1+Sj8unJu2M9i+P8JDP6Uf28+fJDf6wYUM6BodlV7oJ3cyIIQPQeKElq57TrsOVqo50BzR/ZoDzdr50R798e3nY5qfNTpfnrxMyRPhHT2SJy9TWaPzY5rvSgYA7blwbtrOYHs+gI7Zfv4kudEPLmQA0Bb95E4GhMS8KPXqq6/q7rvvjkMUuKihya+r7vum6vz1YRdXc6BZdf56XbngejU0Rf+WSEnyZGYo9/KhoYvIhVsYHknZoft5MmNfd3UhA6JDP3VvLpybtjPYno/o0U/dm+3nT5Ib/eBCBkSHjuq+6Cd3MiCEPYkuvb9/uy6861pVVu+TFPoscUdafl9ZvU8X3nWtPjiwIy7zvYXZ6nXNcHl6hXcJNE+vTPW6ZnhcLzznQgYA7blwbtrOYHs+gI7Zfv4kudEPLmQA0Bb95E4GRHihc6SvDw7s0Dk/u1RXnzVdcybP0hlDT213m427t+jBlYv0x7efj8sK+rG8hdnK+8pINZXXyl92qMOL0mX088lX2jv0VswErFy7kAFAey6cm7Yz2J4PoGO2nz9JbvSDCxkAtEU/uZMh3bEohbA1NPm1eM3TWrzmaZUOHqdRA0coLydXdQ312ravQmW7Nyd0viczQ9njCpU9rlCBAw0KVDdKjUEpO0PewuykfPuBCxkAtOfCuWk7g+35ADpm+/mT5EY/uJABQFv0kzsZ0hmLUohK2e7NSSmpznj751gvBxcyAGjPhXPTdgbb8wF0zPbzJ8mNfnAhA4C26Cd3MqQb3nsGAAAAAACApGNRCgAAAAAAAEnHohQAAAAAAACSjkUpAAAAAAAAJJ3HGGOSObC2tlYFBQWh4bnJv866OdIsGUkeydPTznXeyUAGlzLYni9Jpr5ZklRTU6P8/HwrGST7/SQ58nhwTJKBDG0zONBR9BMZXJlPBscy0E+SHHksyEAGR+Y7kyHMfrL67XstIe0MtzyfDGRwLYPt+Y6xvi9ceDxsZ7A9nwxkcJT1/eDCY0EG+/PJ4FYGR1jfDy48FmQggyvzXcnQBauLUrxTigxksJ/B9nzJzaLkv/TZy2B7PhnI0C6DYx1FP6V3BtvzyeBYBvpJkiOPBRnI4Mh8ZzKE2U/2FqV6epU/e1TSx9Y+uk2mvlmenplW5pOBDK5lsD1fkmoWlktHAlZmd8hSP0luPB62M9ieTwYyHM+pjqKf0j6D7flkcCsD/RTiwmNBBjK4Mt+VDOH2Exc6BwAAAAAAQNKxKAUAAAAAAICkY1EKAAAAAAAASWf1QudIXYEDDQocapSaglJWhry9s+Xtn5O0+aWDx2n0wBHqlZOrww31Kt9XobLdm5M2X7K/DyQ39gPgGs5N9gHgKs7NEPYD4B7OyxAX9oMLGZKJRSmEzTQH1VReK3/ZIQU/9Lf7+4x+PvlKeytrdL48mfF/E15Olk8zJlyiOZNnafyQU9r9/YZd7+rBlYu0dN1zamhqny8ebO8DyY39ALiGc5N9ALiKczOE/QC4h/MyxIX94EIGW1iUQlgC1Y2qf3anTF3nX+sY/NCvo8v3quGtD5V7+VB5C7PjNv/kAcO1bO5DGtKnWEET7PA2pw0q0YKZd+q26XN15YLr9cGBHXGbL9nfB5Ib+wFwDecm+wBwFedmCPsBcA/nZYgL+8GFDDZ1ryU2JESgulGHn9ouc7jzk+RY5nCzDj+1XYHqxrjMP3nAcL186xIVFw6Ux+ORN8Pb4e28GV55PB4VFw7UKz94QiP7D4vLfMn+PpDc2A+Aazg32QeAqzg3Q9gPgHs4L0Nc2A8uZLAtokWpH//4x/J4PG1+xo4dm6hscIBpDqr+2Z1SY1Ay4d5JUmPofqa54xXvcOVk+bRs7kPK8+Uq0xveG/syvZnK8+Xq6ZseVk6WL6b5kv19ILmxH1xHP6Ufzk32QSqho9IL52YI+yE10E/phfMyxIX94EIGF0T8TqlTTz1VVVVVrT+vv/56InLBEU3ltaG3EYZ7krQwkqlrVlN5bUzzZ0y4REP6FIddVi0yvZka2neQrj5rekzzJfv7QHJjP6QC+im9cG6yD1INHZU+ODdD2A+pg35KH5yXIS7sBxcyuCDiRanMzEyddNJJrT/9+vVLRC44wl92yOr950ye1enni7sSCAY0Z/KsmOZL9veB5MZ+SAX0U3rh3GQfpBo6Kn1wboawH1IH/ZQ+OC9DXNgPLmRwQcSLUuXl5SouLtbIkSM1c+ZM7dy5MxG54IDAgYYOr/wfieCHfgUONER139LB4zR+yCmdfr64K94Mr84YeqpKB4+L6v6S/X0gubEfUgX9lD44N9kHqYiOSg+cmyHsh9RCP6UHzssQF/aDCxlcEdGi1MSJE/Xoo4/qhRde0P3336+Kigp99rOfVV1dXaf38fv9qq2tbfOD1BA4FJ+Lp0V7EbbRA0fEZf6oGLZjex9IbuyHVEA/pRfOTfZBqom0o+in1MW5GcJ+SB30U/rgvAxxYT+4kMEVEX2Ic/r0Tz67WVpaqokTJ2rYsGF68skndf3113d4n/nz5+snP/lJbClhR1OcLpzWGN12euXkxmV8XizbsbwPJEf2Qwqgn9IM5yb7IMVE2lH0Uwrj3AxhP6QM+imNcF6GOLAfnMjgiIg/vneswsJCjRkzRtu2bev0NvPmzVNNTU3rz65du2IZiWTKiunw+ER2dNs53FAfl/F1sWzH8j6QHNkPKYh+6uY4N9kHKa6rjqKfUhjnZgj7IWXRT90Y52WIA/vBiQyOiOlfcPjwYb3//vsqKirq9DY+n0/5+fltfpAavL2z47Odwui2U76vIi7zy/d+EPV9be8DyY39kIrop+6Nc5N9kOq66ij6KXVxboawH1IX/dR9cV6GuLAfXMjgiogWpW699VatXLlS27dv19///nddddVV8nq9+vKXv5yofLDI2z9HGf18MW0jo59P3v45Ud23bPdmbdj1rgLBQFT3DwQDWr/zH9q4Z0tU95fs7wPJjf2QCuin9MK5yT5INXRU+uDcDGE/pA76KX1wXoa4sB9cyOCKiBaldu/erS9/+csqKSnRl770JfXt21erV69W//79E5UPlvlKe1u9/4MrFynDE90b+rwZXj24clFM8yX7+0ByYz+4jn5KP5yb7INUQkelF87NEPZDaqCf0gvnZYgL+8GFDC6I6EhYsmSJKisr5ff7tXv3bi1ZskQnn3xyorLBAVmj8+XJy5Q8Ed7RI3nyMpU1Ora38y5d95x2HaxUc6A5ovs1B5q186M9+uPbz8c0X7K/DyQ39oPr6Kf0w7nJPkgldFR64dwMYT+kBvopvXBehriwH1zI4ILUvyoWEsqTmaHcy4eGLqAW7snikZQdup8nM7ZDrKHJr6vu+6bq/PVhl1ZzoFl1/npdueB6NTT5Y5ov2d8Hkhv7AXAN5yb7AHAV52YI+wFwD+dliAv7wYUMLuge/woklLcwW72uGS5Pr8ywbu/plale1wyP20XX3t+/XRfeda0qq/dJUqefP275fWX1Pl1417X64MCOuMyX7O8DyY39ALiGc5N9ALiKczOE/QC4h/MyxIX94EIG28L7lyPteQuzlfeVkWoqr5W/7JCCH7Zfnc7o55OvtHfobYhxXrX94MAOnfOzS3X1WdM1Z/IsnTH01Ha32bh7ix5cuUh/fPv5hPxXLdv7QHJjPwCu4dxkHwCu4twMYT8A7uG8DHFhP7iQwSYWpRA2T2aGsscVKntcoQIHGhSobpQag1J2hryF2Qm/8n9Dk1+L1zytxWueVungcRo1cITycnJV11CvbfsqVLZ7c0LnS/b3geTGfgBcw7nJPgBcxbkZwn4A3MN5GeLCfnAhgy0sSiEq3v45Vk+Mst2brT9xsL0PJDf2A+Aazk32AeAqzs0Q9gPgHs7LEBf2gwsZkql7ve8LAAAAAAAAKYFFKQAAAAAAACQdi1IAAAAAAABIOo8xxiRzYG1trQoKCkLDc5N/SStzpFkykjySp6edS2qRgQwuZbA9X5JMfbMkqaamRvn5+VYySPb7SXLk8eCYJAMZ2mZwoKPoJzK4Mp8MjmWgnyQ58liQgQyOzHcmQ5j9ZPVC5y0h7Qy3PJ8MZHAtg+35jrG+L1x4PGxnsD2fDGRwlPX94MJjQQb788ngVgZHWN8PLjwWZCCDK/NdydAFq4tSvFOKDGSwn8H2fMnNouS/9NnLYHs+GcjQLoNjHUU/pXcG2/PJ4FgG+kmSI48FGcjgyHxnMoTZT/YWpXp6lT97VNLH1j66Taa+WZ6emVbmk4EMrmWwPV+SahaWS0cCVmZ3yFI/SW48HrYz2J5PBjIcz6mOop/SPoPt+WRwKwP9FOLCY0EGMrgy35UM4fYTFzoHAAAAAABA0rEoBcTIk9zvCgAAAAAAIOGS8VrX6jWlgFQ0/kCDZm6u1rmVR1VyyK/soNSYIW3t7dOq4h56fFyhNvTPsR0TAAAAAICw2Xity6IUEKYR1Y1asLxKk6qOqskjZY0/Q5o2VsrLU3ZdnU7fskVjN6zXnI3VeqOoh26aWqSKwmzbsQGkodLB4zR64Aj1ysnV4YZ6le+rUNnuzbZjAYAkOgqAu9K1n45/rbvJSEsl1UnKC0pjP/Lr6wf9CXmty6IUEIZr3qvRfcv3ypvtk2bPVtYtt0hnntnudlnvvCPdc48mPrFEq5dU6MapRVo6Jt9CYgDpJifLpxkTLtGcybM0fsgp7f5+w6539eDKRVq67jk1NPktJASQzugoAK5K935qea3bGDBaKOkeI63v4HZnGOkWSddUHY3ra12uKQV04Zr3avTgS1XKGjlKme9ulh55RCot7fjGpaXSI48o893Nyhpxsn77UqWuea8muYEBpJ2TBwzXm3f8RQtm3qnTBpV0eJvTBpVowcw79eYdf9HI/sOSnBBAOqOjALgq3fup5bVuRcDodEnfkFTWyW3LPv770yVtD5i4vdZlUQo4gZHVjbpv+V5p9GhlrF4tDRokeTyS19vxHbze0N8PGqSMNWukUaN03/K9GlHdmNzgANLGyQOG6+Vbl6i4cKA8Ho+8GR33kzfDK4/Ho+LCgXrlB090uydVANxERwFwVbr3U8tr3fcknSdp98e/D3Zy+5bf75Z0rqRyKS6vdSNelNqzZ49mzZqlvn37qkePHjr99NO1du3amEIArrp3eZUysn3K+Otfpbw8KSsrvDtmZUl5ecp46SV5s31asLwqsUEhiX5C+snJ8mnZ3IeU58tVpje8T+RnejOV58vV0zc9rJwsX4IT4lh0FNINHZU66CekG/op9FrXHzC6WFKtpECY9wt8fPvPS2oMmJhf60a0KHXo0CFNmjRJWVlZev755/Xuu+/ql7/8pXr37h1TCMBF4/c3aFLVUWVd+0/SsGHhL0i1yMqShg9X5peu1aSqoxp/oCExQSGJfkJ6mjHhEg3pUxz2k6kWmd5MDe07SFefNT1ByXA8OgrpiI5KDfQT0lG691PLa92lknYo/AWpFoGP77dUivm1bkSPwH/+539qyJAhWrhwYevvRowYEfVwwGUzt1SHvmXvllukYLDzj+ydSCAg3Xyzmn73mGZurtaG/ifFPygk0U9IT3Mmz1LQBOX1RN5PgWBAcybP0uI1T8c/GNqho5CO6KjUQD8hHaV7P7W81r3HhN6p1NlH9k4kQ9I9kmZ6FNNr3YjeKfXss8/qU5/6lL74xS9qwIABOvPMM/Xb3/42qsGA686tPKqs8WeEvmUvmgUpKXS/CROUNf4MfbrqaFzzoS36CemmdPA4jR9ySqfXP+iKN8OrM4aeqtLB4+KcDB2ho5Bu6KjUQT8h3dBPode6mz7+lr1oFqT08f3ekbTJKKbXuhEtSn3wwQe6//77NXr0aL344ov6zne+o1tuuUWPPfZYp/fx+/2qra1t8wOkgpJDfmns2DhtrERjD3a/rw91Cf2EdDN6YHz+K/aoOG0HJxZpR9FPSHV0VOqgn5Bu6KfQa90tcdrWVimm17oRfXwvGAzqU5/6lH7+859Lks4880xt2rRJ//u//6vrrruuw/vMnz9fP/nJT6IOCNjgMUbZQYUubh4P+fnKDoa2azye+GwTbdBPSDe9cnLjsp28OG0HJxZpR9FPSHV0VOqgn5Bu0r2fWl7r1sVpe7VSTK91I3qnVFFRkU455ZQ2vxs3bpx27tzZ6X3mzZunmpqa1p9du3ZFHBJINuPxqDFDUl2cTtXaWjVmiAWpBKKfkG4ON9THZTt1cdoOTizSjqKfkOroqNRBPyHdpHs/tbzWjdPbL5QvxfRaN6J3Sk2aNElbt25t87v33ntPw4YN6/Q+Pp9PPl/qf10i0s/W3j6dviVOb2rcskVb+nAeJBL9hHRTvq8iPtvZ+0FctoMTi7Sj6CekOjoqddBPSDf0U+i17tiP4nN5mbFSTK91I3qn1Pe//32tXr1aP//5z7Vt2zYtXrxYDz74oObOnRt1AMBVq4p7qGnDeumdd0LfoheNQEBat05NZRu0uqhHXPOhLfoJ6aZs92Zt2PWuAsHo+ikQDGj9zn9o4554XVEAJ0JHId3QUamDfkK6oZ9Cr3VP80hnKMJFoWNkSDpT0qkexfRaN6L5Z599tpYtW6bf//73Ou200/Qf//EfuvvuuzVz5syoAwCuenxcobKMpHvukTKiPFW9Xunee5VlQttD4tBPSEcPrlykDE90/eTN8OrBlYvinAidoaOQjuio1EA/IR2lez+1vNa9RbF9+94tUsyvdSP6+J4kfeELX9AXvvCFqAcCqWJD/xy9UdRDE59Yoswf/UgaNEjKygp/A01N0p4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GnyFNGyvl5Sm7rk6nb9misRvWa87Gar1R1EM3TS1SRWG27dhII/QTWnAswDUck2jBsQDXcEziWIEDDQocapSaglJWhry9s+VN0BsOWJRC2HKyfJox4RLNmTxL44ec0u7vN+x6Vw+uXKSl655TQ5PfQkIk2jXv1ei+5XvlzfZJs2cr65ZbpDPPbHe7rHfeke65RxOfWKLVSyp049QiLR2TbyEx0gX9hBYcC3ANxyRacCzANRyTOJZpDqqpvFb+skMKftj+8c7o55OvtLeyRufLkxm/K0FxTSmE5eQBw/XmHX/Rgpl36rRBJR3e5rRBJVow8069ecdfNLL/sCQnRKJd816NHnypSlkjRynz3c3SI49IpaUd37i0VHrkEWW+u1lZI07Wb1+q1DXv1SQ3MNIG/YQWHAtwDcckWnAswDUckzhWoLpRdYs/0NHleztckJKk4Id+HV2+V3WLP1CgujFus1mUQpdOHjBcL9+6RMWFA+XxeOTN8HZ4O2+GVx6PR8WFA/XKD56guLqRkdWNum/5Xmn0aGWsXi0NGiR5PJK342NBXm/o7wcNUsaaNdKoUbpv+V6NiGN5ARL9hE9wLMA1HJNowbEA13BM4liB6kYdfmq7zOHmsG5vDjfr8FPb47YwxaIUTigny6dlcx9Sni9Xmd7wPu2Z6c1Uni9XT9/0sHKyfAlOiGS4d3mVMrJ9yvjrX6W8PCkrK7w7ZmVJeXnKeOklebN9WrC8KrFBkVboJ7TgWIBrOCbRgmMBruGYxLFMc1D1z+6UGoNSuF+mbiQ1hu5nmoMxZ4hoUWr+/Pk6++yzlZeXpwEDBujKK6/U1q1bYw4Bd82YcImG9CkOu7BaZHozNbTvIF191vQEJUOyjN/foElVR5V17T9Jw4aFvyDVIitLGj5cmV+6VpOqjmr8gYaE5KSf0g/9hBapcCzQUeklFY5JJEcqHAv0U3pJhWMSydNUXitT1xz+glQLI5m6ZjWV18acIaJFqZUrV2ru3LlavXq1XnrpJTU1Nemiiy5SfX19zEHgpjmTZyloolv9DAQDmjN5VpwTIdlmbqlWk0fSLbdIwShXwgMB6eab1eSRZm6ujme8VvRT+qGf0CIVjgU6Kr2kwjGJ5EiFY4F+Si+pcEwiefxlh6zeX4rw2/deeOGFNn9+9NFHNWDAAK1bt07nn39+zGHgltLB4zr8FoZweTO8OmPoqSodPI6vE01h51YeVdb4Mzr8lr2web3ShAnKGn+GPp2gY4F+Si/0E1qkyrFAR6WPVDkmkXipcizQT+kjVY5JJEfgQEOnFzUPV/BDvwIHGuTtnxP1NmK6plRNTejbtPr06dPpbfx+v2pra9v8IDWMHjgiLtsZFaftwI6SQ35p7Ng4baxEYw8m5+tk6afujX5Ci1Q9FrrqKPopdaXqMYn4S9VjgX7qvlL1mERiBA7F50LlsV7wPOpFqWAwqO9973uaNGmSTjvttE5vN3/+fBUUFLT+DBkyJNqRSLJeOblx2U5enLaD5PMYo+ygQhc3j4f8fGUHQ9tNJPqp+6Of0CIVj4VwOop+Sl2peEwiMVLxWKCfurdUPCaRQE2xX6RcUugi6TGIelFq7ty52rRpk5YsWXLC282bN081NTWtP7t27Yp2JJLscEN8PkdeF6ftIPmMx6PGDEl1dfHZYG2tGjNC200k+qn7o5/QIhWPhXA6in5KXal4TCIxUvFYoJ+6t1Q8JpFAWTF9cO4T2bFtJ7JL7n/spptu0p///Ge99tprGjx48Alv6/P55PPxtZGpqHxfRXy2s/eDuGwHdmzt7dPpW7bEZ2NbtmhLn8T2Af2UHugntEi1YyHcjqKfUleqHZNInFQ7Fuin7i/Vjkkklrd3dny2UxjbdiJa0jLG6KabbtKyZcu0fPlyjRjBZ0m7s7Ldm7Vh17sKBANR3T8QDGj9zn9o4544LWjAilXFPdS0Yb30zjuhb9GLRiAgrVunprINWl3UI675WtBP6YV+QotUORboqPSRKsckEi9VjgX6KX2kyjGJ5PD2z1FGv9gWmDP6+WK6yLkU4aLU3LlztWjRIi1evFh5eXnau3ev9u7dq6NHj8YUAu56cOUiZXiiezueN8OrB1cuinMiJNvj4wqVZSTdc4+UEeVbM71e6d57lWVC20sE+in90E9okQrHAh2VXlLhmERypMKxQD+ll1Q4JpE8vtLeVu8vRbgodf/996umpkYXXHCBioqKWn+eeOKJmIPATUvXPaddByvVHGiO6H7NgWbt/GiP/vj28wlKhmTZ0D9HbxT1UPMTS6QdO6Smpsg20NQkbd+u5ief0BtFPbQhxpX0ztBP6Yd+QotUOBboqPSSCsckkiMVjgX6Kb2kwjGJ5MkanS9PXqYU6SV/PZInL1NZo/NjzhDxx/c6+pk9e3bMQeCmhia/rrrvm6rz14ddXM2BZtX563XlguvV0ORPcEIkw01TixRo9Ct40UWhi56HuzDV1CTV1Sk4bZoCjX7dNLUoYRnpp/RDP6FFKhwLdFR6SYVjEsmRCscC/ZReUuGYRPJ4MjOUe/nQ0MXKw12Y8kjK8CJETgABAABJREFUDt3Pkxn7xdLjdLl1dGfv79+uC++6VpXV+ySp088gt/y+snqfLrzrWn1wYEfSMiKxKgqzdePUIqm8XMGJE6U9e0J/0dk1plp+v2dP6PbbtunGqUWqiPEieMDx6Ce04FiAazgm0YJjAa7hmMSxvIXZ6nXNcHl6hfc9eJ5emep1zfCYL3DeIqpv30P6+eDADp3zs0t19VnTNWfyLJ0x9NR2t9m4e4seXLlIf3z7eVbQu6GlY/LlkdF9y9+X95RxyvzStdLNN0sTJrS/8fr10r33qvnJJxRo9OvGacVaOib2t3YCHaGf0IJjAa7hmEQLjgW4hmMSx/IWZivvKyPVVF4rf9khBT9s/3hn9PPJV9o79JG/OLxDqgWLUghbQ5Nfi9c8rcVrnlbp4HEaNXCE8nJyVddQr237KlS2e7PtiEiwp8YUaN2AHlqwvEqTHntMTb97TFnjz5BKSqT8fKm2Vtq6VU0b1ivLSKuLe+jmKSN4hxQSjn5CC44FuIZjEi04FuAajkkcy5OZoexxhcoeV6jAgQYFqhulxqCUnSFvYXbM37LXGRalEJWy3ZspqTRVUZitS68epvEHGjRzc7U+vXuzxpatV3ZQasyQtvTxafVphXp8XGHCLmoOnAj9hBYcC3ANxyRacCzANRyTOJa3f07CFqGOx6IUgKhs6J+jDf1Pav2zxxgZT6Rf2wAAAAAASFdc6BxAXLAgBQAAAACIBItSAAAAAAAASDqPMcYkc2Btba0KCgpCw3OT/+lBc6RZMpI8kqennU8vkoEMLmWwPV+STH2zJKmmpkb5+fa+pc92P0mOPB4ck2QgQ9sMDnQU/UQGV+aTwbEM9JMkRx4LMpDBkfnOZAizn6xeU6olpJ3hlueTgQyuZbA93zHW94ULj4ftDLbnk4EMjrK+H1x4LMhgfz4Z3MrgCOv7wYXHggxkcGW+Kxm6YHVRindKkYEM9jPYni+5WZT8lz57GWzPJwMZ2mVwrKPop/TOYHs+GRzLQD9JcuSxIAMZHJnvTIYw+8neolRPr/Jnj0r62NpHt8nUN8vTM9PKfDKQwbUMtudLUs3CculIwMrsDlnqJ8mNx8N2BtvzyUCG4znVUfRT2mewPZ8MbmWgn0JceCzIQAZX5ruSIdx+4kLnAAAAAAAASDoWpQAAQLfmSe53ugAAACBMVq8pBQAAEG/jDzRo5uZqnVt5VCWH/MoOSo0Z0tbePq0q7qHHxxVqQ/8c2zEBAADSHotSKah08DiNHjhCvXJydbihXuX7KlS2e3NSMwQONChwqFFqCkpZGfL2zpY3iU/wbc93JYMLxwJwLBeOSRfOTdsZbM+3lWFEdaMWLK/SpKqjavJIm4y0VFKdpLygNPYjv75+0K85G6v1RlEP3TS1SBWF2QnNBHfQT2RwKYPt+XAL/USGdM7AolSKyMnyacaESzRn8iyNH3JKu7/fsOtdPbhykZaue04NTf6EZDDNQTWV18pfdkjBD9vPyOjnk6+0t7JG58uTGf9Phtqe70oGF44F4FguHJMunJu2M9iebzvDNe/V6L7le9UYMFoo6R4jre/gdmcY6RZJ11Qd1eolFbpxapGWjsmPaxa4g34ig0sZbM+HW+gnMpAhhEWpFHDygOFaNvchDelTrKAJdnib0waVaMHMO3Xb9Lm6csH1+uDAjrhmCFQ3qv7ZnTJ1nX+tY/BDv44u36uGtz5U7uVD5Y3jf322Pd+VDC4cC8CxXDgmXTg3bWewPd92hmveq9GDL1XpPUkXS9qhzi+aWSbpG5J+IunFgNFvX6qUR0ZPjSmISxa4g34ig0sZbM+HW+gnMpDhEyzBO+7kAcP18q1LVFw4UB6PR94Mb4e382Z45fF4VFw4UK/84AmN7D8sbhkC1Y06/NR2mcOdH6DHMoebdfip7QpUN3aL+a5kcOFYAI7lwjHpwrlpO4Pt+bYzjKxu1H3L9+o9SedJ2v3x7zt+iv/J73dLOldSuaT7lu/ViDjuD9hHP5HBpQy258Mt9BMZyNAWi1IOy8nyadnch5Tny1WmN7w3tWV6M5Xny9XTNz2snCxfzBlMc1D1z+6UGoNSuF9eZCQ1hu5nmjt7WZAa813J4MKxABzLhWPShXPTdgbb813IcO/yKvkDRhdLqpUUCPN+gY9v/3lJjQGjBcurYsoBd9BPZHApg+35cAv9RAYytBfRotT999+v0tJS5efnKz8/X+eee66ef/75mEOgYzMmXKIhfYrDLqwWmd5MDe07SFefNT3mDE3ltaG38EX6bdpGMnXNaiqvTen5rmRw4VhwHf2UXC4cky6cm7Yz2J5vO8P4/Q2aVHVUSxX6yF64C1ItAh/fb6mkSVVHNf5AQ9RZukJHJQ/9RAaXMtieHw76KXnoJzKQob2IFqUGDx6sX/ziF1q3bp3Wrl2rqVOn6oorrtA//vGPmIOgvTmTZ3X6GeOuBIIBzZk8K+YM/rJDaX1/VzK4cCy4jn5KLheOSRfOTdsZbN/fdoaZW6rV5JHuUfRv/c5Q6P5NHmnm5uqos3SFjkoe+okMLmWwff9w0E/JQz+RgQztRfQc7rLLLtMll1yi0aNHa8yYMbrzzjvVq1cvrV69OuYgaKt08DiNH3JKp58x7oo3w6szhp6q0sHjos4QONDQ4VX3IxH80K9AlP/l2fZ8VzK4cCykAvopeVw4Jl04N21nsD3fhQznVh7Vpo+/ZS/aN48HJb0jaZORPl11NMqtdI2OSg76iQwuZbA9P1z0U3LQT2QgQ8eivqZUIBDQkiVLVF9fr3PPPbfT2/n9ftXW1rb5QddGDxwRl+2MimE7gUPxu7hjKs53JYMLx0KqoZ8Sy4Vj0oVz03YG2/NdyFByyK8tcUkgbZU09mBivnL7eOF0FP0UHfqJDC5lsD0/qln0U8LQT2QgQ8ciXpTauHGjevXqJZ/Pp29/+9tatmyZTjnllE5vP3/+fBUUFLT+DBkyJKbA6aJXTm5ctpMXy3aa4nRhxcYot2N7viMZnDgWUgT9lBxOHJMOnJvWM9iebzmDxxhlB6W6+CRQraTsYGi7iRJJR9FP0aGfyOBUBtvzI0A/JR79RAYydCziRamSkhKtX79ea9as0Xe+8x1dd911evfddzu9/bx581RTU9P6s2vXrpgCp4vDDfVx2U5dLNvJitOXM2ZHuR3b8x3J4MSxkCLop+Rw4ph04Ny0nsH2fMsZjMejxgwpLz4JlC+pMSO03USJpKPop+jQT2RwKoPt+RGgnxKPfiIDGToW2WX/JWVnZ2vUqFGSpAkTJuitt97Sr3/9az3wwAMd3t7n88nn4+voI1W+ryI+29n7QdT39fbOjksGb2F027E935UMLhwLqYJ+Sg4XjkkXzk3bGWzPdyHD1t4+jf0oPh+5GytpS5/E9kEkHUU/RYd+IoNLGWzPjwT9lHj0ExnI0LGYl8aCwaD8/uRcgyGdlO3erA273lUgGOkXXIcEggGt3/kPbdwT/dU2vP1zlNEvtv/Dyejnk7d/TkrOdyWDC8dCqqKfEsOFY9KFc9N2BtvzXciwqriHTvNIZyi2b987U9KpHml1UY8otxIdOir+6CcyuJTB9vxY0E/xRz+RgQydbCOSG8+bN0+vvfaatm/fro0bN2revHl69dVXNXPmzJhCoGMPrlykDE90T7O9GV49uHJRzBl8pb3T+v6uZHDhWHAd/ZRcLhyTLpybtjPYvr/tDI+PK1SWkW5RbN++d4ukLBPaXqLQUclDP5HBpQy27x8O+il56CcykKG9iM6I/fv362tf+5pKSkp04YUX6q233tKLL76oadOmxRwE7S1d95x2HaxUc6A5ovs1B5q186M9+uPbz8ecIWt0vjx5mVKkl9jwSJ68TGWNzk/p+a5kcOFYcB39lFwuHJMunJu2M9iebzvDhv45eqOoh66RNExSpF+y7f34fjMkvVHUQxsS+G4EOip56CcyuJTB9vxw0E/JQz+RgQztRbQo9fDDD2v79u3y+/3av3+/Xn75ZcoqgRqa/Lrqvm+qzl8fdnE1B5pV56/XlQuuV0NT7G+59WRmKPfyoaGLl4V7oHokZYfu58mM7ROitue7ksGFY8F19FNyuXBMunBu2s5ge74LGW6aWqRsr0cvKHSx8nAXprwf3/5FSdlej26aWhRTjq7QUclDP5HBpQy254eDfkoe+okMZGgv8S2HmLy/f7suvOtaVVbvk6ROP4Pc8vvK6n268K5r9cGBHXHL4C3MVq9rhsvTK7zr4nt6ZarXNcPjdlFG2/NdyeDCsQAcy4Vj0oVz03YG2/NtZ6gozNaNU4s0RtIqSYM//n1nT3Bafj/449uPlnTj1CJVJOFCwkge+okMLmWwPR9uoZ/IQIa2Iv72PSTfBwd26JyfXaqrz5quOZNn6Yyhp7a7zcbdW/TgykX649vPJ+RdMd7CbOV9ZaSaymvlLzuk4IftZ2T088lX2jv0FsA4/1cd2/NdyeDCsQAcy4Vj0oVz03YG2/NtZ1g6Jl8eGd23fK82BoyWSrpH0jsd3Ha8QteQmqHQO6RumFqkpWMS//EYJB/9RAaXMtieD7fQT2QgwydYlEoRDU1+LV7ztBaveVqlg8dp1MARysvJVV1Dvbbtq1DZ7s0Jz+DJzFD2uEJljytU4ECDAtWNUmNQys6QtzA74d8MYnu+KxlcOBaAY7lwTLpwbtrOYHu+7QxPjSnQugE9tGB5lWZXHdVMj7TJSFsl1Sr0Ub0SSad5Qhc1f724h26ewjukujv6iQwuZbA9H26hn8hAhhAWpVJQ2e7N1hcevP1zrP4fp+35rmRw4VgAjuXCMenCuWk7g+35tjJUFGbr0quHafyBBs3cXK1PVx3V1Qf9yg5KjRnSlj4+LSzqocfHFSb0ouZwE/1EBpcy2J4Pt9BPZEjnDCxKAQCAbmVD/xxt6H9S6589xsh4Iv1aGQAAACQaH1YGAADdGgtSAAAAbmJRCgAAAAAAAEnnMcaYZA6sra1VQUFBaHhu8j89aI40S0aSR/L0tPPpRTKQwaUMtudLkqlvliTV1NQoP9/et2DZ7ifJkceDY5IMZGibwYGOop/I4Mp8MjiWgX6S5MhjQQYyODLfmQxh9pPVa0q1hLQz3PJ8MpDBtQy25zvG+r5w4fGwncH2fDKQwVHW94MLjwUZ7M8ng1sZHGF9P7jwWJCBDK7MdyVDF6wuSvFOKTKQwX4G2/MlN4uS/9JnL4Pt+WQgQ7sMjnUU/ZTeGWzPJ4NjGegnSY48FmQggyPznckQZj/ZW5Tq6VX+7FFJH1v76DaZ+mZ5emZamU8GMriWwfZ8SapZWC4dCViZ3SFL/SS58XjYzmB7PhnIcDynOop+SvsMtueTwa0M9FOIC48FGcjgynxXMoTbT1zoHAAAAAAAAEnHohQAAAAAAACSjkUpAAAAAAAAJB2LUgAAAAAAAEg6q9++h9RVOnicRg8coV45uTrcUK/yfRUq2705afMDBxoUONQoNQWlrAx5e2fL2z8nafMl+/sAQMdcODdtd5QL+wBAey6cm7b7SXJjPwBoy4Xzkn5KTyxKIWw5WT7NmHCJ5kyepfFDTmn39xt2vasHVy7S0nXPqaHJH/f5pjmopvJa+csOKfhh++1n9PPJV9pbWaPz5clMzJsAbe8DAB1z4dy03VEu7AMA7blwbtruJ8mN/QCgLRfOS/oJLEohLCcPGK5lcx/SkD7FCppgh7c5bVCJFsy8U7dNn6srF1yvDw7siNv8QHWj6p/dKVPX3Oltgh/6dXT5XjW89aFyLx8qb2F23OZL9vcBgI65cG7a7igX9gGA9lw4N233k+TGfgDQlgvnJf0EiWtKIQwnDxiul29douLCgfJ4PPJmeDu8nTfDK4/Ho+LCgXrlB09oZP9hcZkfqG7U4ae2yxzuvKyOZQ436/BT2xWobozLfMn+PgDQMRfOTdsd5cI+ANCeC+em7X6S3NgPANpy4bykn9AipkWpX/ziF/J4PPre974XpzhwTU6WT8vmPqQ8X64yveG9sS7Tm6k8X66evulh5WT5YppvmoOqf3an1BiUTLh3ktQYup9p7ni1OxK29wGiQz91fy6cm7Y7yoV9gMjRT92fC+em7X6S3NgPiBwd1b25cF7STzhW1ItSb731lh544AGVlpbGMw8cM2PCJRrSpzjsE7VFpjdTQ/sO0tVnTY9pflN5bejtnOGWVQsjmbpmNZXXxjRfsr8PEDn6KT24cG7a7igX9gEiQz+lBxfOTdv9JLmxHxAZOqr7c+G8pJ9wrKgWpQ4fPqyZM2fqt7/9rXr37h3vTHDInMmzOv1sbVcCwYDmTJ4V03x/2SGr95fs7wNEhn5KHy6cm7Y7yoV9gPDRT+nDhXPTdj9JbuwHhI+OSg8unJf0E44V1aLU3Llzdemll+pzn/tcvPPAIaWDx2n8kFM6/WxtV7wZXp0x9FSVDh4X1f0DBxo6/AaGSAQ/9CtwoCHq+9veB4gc/ZQeXDg3bXeUC/sAkaGf0oML56btfpLc2A+IDB3V/blwXtJPOF7E3763ZMkSvf3223rrrbfCur3f75ff/8lBV1sb+1vtkByjB46Iy3ZGDRyhst2bI75f4FB8LmIXqG6Ut39OVPe1vQ8QGfopfbhwbtruKBf2AcJHP6UPF85N2/0kubEfEL5IOop+Sl0unJf0E44X0Tuldu3ape9+97t6/PHHlZMT3gEwf/58FRQUtP4MGTIkqqBIvl45uXHZTl6022mK/QJ2kkIX0IuS9X2AsNFP6cWJc9NyRzmxDxAW+im9OHFu8hwKEYi0o+in1OXEeUk/4TgRLUqtW7dO+/fv11lnnaXMzExlZmZq5cqVuueee5SZmalAINDuPvPmzVNNTU3rz65du+IWHol1uKE+Ltupi3Y7WTF9OeQnsqPfjvV9gLDRT+nFiXPTckc5sQ8QFvopvThxbvIcChGItKPop9TlxHlJP+E4EX1878ILL9TGjRvb/O7rX/+6xo4dq9tuu01eb/vPZPp8Pvl8fF1iKirfVxGf7ez9IKr7eXtnx2W+tzD67djeBwgf/ZReXDg3bXeUC/sA4aGf0osL56btfpLc2A8IT6QdRT+lLhfOS/oJx4toUSovL0+nnXZam9/l5uaqb9++7X6P1Fe2e7M27HpXpw0qieoicIFgQBt3b9HGPVuimu/tn6OMfr6YLoSX0c8X9WeNJfv7AOGjn9KLC+em7Y5yYR8gPPRTenHh3LTdT5Ib+wHhoaPShwvnJf2E48XpvXPorh5cuUgZnugOE2+GVw+uXBTTfF9pbF9HG+v9Jfv7AEDHXDg3bXeUC/sAQHsunJu2+0lyYz8AaMuF85J+wrFiXpR69dVXdffdd8chCly0dN1z2nWwUs2B5oju1xxo1s6P9uiPbz8f0/ys0fny5GVKngjv6JE8eZnKGp0f03zJ/j5A9Oin7s2Fc9N2R7mwDxAd+ql7c+HctN1Pkhv7AdGho7ovF85L+gnH4p1SOKGGJr+uuu+bqvPXh33CNgeaVeev15ULrldDU/Rvy5QkT2aGci8fGrqQXbil5ZGUHbqfJzP2Q9z2PgDQMRfOTdsd5cI+ANCeC+em7X6S3NgPANpy4bykn3AsFqXQpff3b9eFd12ryup9kkKfoe1Iy+8rq/fpwruu1QcHdsRlvrcwW72uGS5Pr/Augebplale1wyP6eJ3x7O9DwB0zIVz03ZHubAPALTnwrlpu58kN/YDgLZcOC/pJ7SI6ELnSF8fHNihc352qa4+a7rmTJ6lM4ae2u42G3dv0YMrF+mPbz8f95Vjb2G28r4yUk3ltfKXHerwwngZ/XzylfYOvR00Dqvnx7O9DwB0zIVz03ZHubAPALTnwrlpu58kN/YDgLZcOC/pJ0gsSiECDU1+LV7ztBaveVqlg8dp1MARysvJVV1Dvbbtq1DZ7s0Jne/JzFD2uEJljytU4ECDAtWNUmNQys6QtzA7pm9gCJftfQCgYy6cm7Y7yoV9AKA9F85N2/0kubEfALTlwnlJP4FFKUSlbPdmqyent39OUgrqRGzvAwAdc+HctN1RLuwDAO25cG7a7ifJjf0AoC0Xzkv6KT1xTSkAAAAAAAAkHYtSAAAAAAAASDoWpQAAAAAAAJB0HmOMSebA2tpaFRQUhIbnJv+SVuZIs2QkeSRPTzuX1CIDGVzKYHu+JJn6ZklSTU2N8vPzrWSQ7PeT5MjjwTFJBjK0zeBAR9FPZHBlPhkcy0A/SXLksSADGRyZ70yGMPvJ6oXOW0LaGW55PhnI4FoG2/MdY31fuPB42M5gez4ZyOAo6/vBhceCDPbnk8GtDI6wvh9ceCzIQAZX5ruSoQtWF6V4pxQZyGA/g+35kptFyX/ps5fB9nwykKFdBsc6in5K7wy255PBsQz0kyRHHgsykMGR+c5kCLOf7C1K9fQqf/aopI+tfXSbTH2zPD0zrcwnAxlcy2B7viTVLCyXjgSszO6QpX6S3Hg8bGewPZ8MZDieUx1FP6V9BtvzyeBWBvopxIXHggxkcGW+KxnC7ScudA4AAAAAAICkY1EKAAAAAAAASceiFAAAAAAAAJLO6oXOkboCBxoUONQoNQWlrAx5e2fL2z8nafNLB4/T6IEj1CsnV4cb6lW+r0Jluzcnbb5kfx+4kgFwjQvnhe2OcmEfuJABcI3tbnAlgwv94EIGwCUudIMLGVzoBhcyJBOLUgibaQ6qqbxW/rJDCn7ob/f3Gf188pX2VtbofHky4/8mvJwsn2ZMuERzJs/S+CGntPv7Dbve1YMrF2npuufU0NQ+XzzY3geuZABc48J5YbujXNgHLmQAXGO7G1zJ4EI/uJABcIkL3eBCBhe6wYUMtrAohbAEqhtV/+xOmbrOv9Yx+KFfR5fvVcNbHyr38qHyFmbHbf7JA4Zr2dyHNKRPsYIm2OFtThtUogUz79Rt0+fqygXX64MDO+I2X7K/D1zJALjGhfPCdke5sA9cyAC4xnY3uJLBhX5wIQPgEhe6wYUMLnSDCxls6l5LbEiIQHWjDj+1XeZw5yfJsczhZh1+arsC1Y1xmX/ygOF6+dYlKi4cKI/HI2+Gt8PbeTO88ng8Ki4cqFd+8IRG9h8Wl/mS/X3gSgbANS6cF7Y7yoV94EIGwDW2u8GVDC70gwsZAJe40A0uZHChG1zIYFtEi1I//vGP5fF42vyMHTs2UdngANMcVP2zO6XGoGTCvZOkxtD9THPHK97hysnyadnch5Tny1WmN7w39mV6M5Xny9XTNz2snCxfTPMl+/vAlQyuo5/Sjwvnhe2OcmEfuJAhFdBR6cV2N7iSwYV+cCGD6+in9OJCN7iQwYVucCGDCyJ+p9Spp56qqqqq1p/XX389EbngiKby2tDbCMM9SVoYydQ1q6m8Nqb5MyZcoiF9isMuqxaZ3kwN7TtIV581Pab5kv194EqGVEA/pRcXzgvbHeXCPnAhQ6qgo9KH7W5wJYML/eBChlRAP6UPF7rBhQwudIMLGVwQ8aJUZmamTjrppNaffv36JSIXHOEvO2T1/nMmz+r088VdCQQDmjN5VkzzJfv7wJUMqYB+Si8unBe2O8qFfeBChlRBR6UP293gSgYX+sGFDKmAfkofLnSDCxlc6AYXMrgg4kWp8vJyFRcXa+TIkZo5c6Z27tyZiFxwQOBAQ4dX/o9E8EO/Agcaorpv6eBxGj/klE4/X9wVb4ZXZww9VaWDx0V1f8n+PnAlQ6qgn9KHC+eF7Y5yYR+4kCGV0FHpwXY3uJLBhX5wIUOqoJ/Sgwvd4EIGF7rBhQyuiGhRauLEiXr00Uf1wgsv6P7771dFRYU++9nPqq6urtP7+P1+1dbWtvlBaggcis/F06K9CNvogSPiMn9UDNuxvQ9cyZAK6Kf04sJ5YbujXNgHLmRIFZF2FP2Uumx3gysZXOgHFzKkAvopfbjQDS5kcKEbXMjgiog+xDl9+ief3SwtLdXEiRM1bNgwPfnkk7r++us7vM/8+fP1k5/8JLaUsKMpThdOa4xuO71ycuMyPi+W7VjeB85kSAH0U5px4Lyw3lEO7AMnMqSISDuKfkpd1rvBkQxO9IMLGVIA/ZQ+XOgGFzI40Q0uZHBExB/fO1ZhYaHGjBmjbdu2dXqbefPmqaampvVn165dsYxEMmXFdHh8Iju67RxuqI/L+LpYtmN5HziTIQXRT92cA+eF9Y5yYB84kSFFddVR9FPqst4NjmRwoh9cyJCC6Kfuy4VucCGDE93gQgZHxPQvOHz4sN5//30VFRV1ehufz6f8/Pw2P0gN3t7Z8dlOYXTbKd9XEZf55Xs/iPq+tveBKxlSEf3UvblwXtjuKBf2gQsZUlVXHUU/pS7b3eBKBhf6wYUMqYh+6r5c6AYXMrjQDS5kcEVEi1K33nqrVq5cqe3bt+vvf/+7rrrqKnm9Xn35y19OVD5Y5O2fo4x+vpi2kdHPJ2//nKjuW7Z7szbseleBYCCq+weCAa3f+Q9t3LMlqvtL9veBKxlSAf2UXlw4L2x3lAv7wIUMqYKOSh+2u8GVDC70gwsZUgH9lD5c6AYXMrjQDS5kcEVEi1K7d+/Wl7/8ZZWUlOhLX/qS+vbtq9WrV6t///6JygfLfKW9rd7/wZWLlOGJ7g193gyvHly5KKb5kv194EoG19FP6ceF88J2R7mwD1zIkAroqPRiuxtcyeBCP7iQwXX0U3pxoRtcyOBCN7iQwQURHQlLlixRZWWl/H6/du/erSVLlujkk09OVDY4IGt0vjx5mZInwjt6JE9eprJGx/Z23qXrntOug5VqDjRHdL/mQLN2frRHf3z7+ZjmS/b3gSsZXEc/pR8XzgvbHeXCPnAhQyqgo9KL7W5wJYML/eBCBtfRT+nFhW5wIYML3eBCBhek/lWxkFCezAzlXj40dAG1cE8Wj6Ts0P08mbEdYg3/n707j4+yOvQ//n0yk0wgK0tYwo5BQBRQ3OkVwaIibhWVWrDiWq+4tP3VWu6111tbS3u9rVaxtiqiFq1YFUu9olVZtK0iIhBUQJB9FYSskEky8/z+GBMJ2WaSmeecZD7v1yuvV53MzPlyHs63M4dnnqkK6luP3KDSYHnUpVUdqlZpsFyXzrpeFVXBVo0vmZ8DWzIAtrFhXZjuKBvmwIYMgG1Md4MtGWzoBxsyADaxoRtsyGBDN9iQwQbt40+BhPLlpinz8v5yMv1R3d/J9Cvz8v5xu+ja519s0Tn3T9auor2S1Ojnj2tu31W0V+fcP1mb9m2Ny/iS+TmwJQNgGxvWhemOsmEObMgA2MZ0N9iSwYZ+sCEDYBMbusGGDDZ0gw0ZTIvuT46k58tNU9Z3BqpqQ4mChQcV3l9/dzqla0CB4Z0ipyHGedd2076tOvUXE3XZSRN005ipGtl3WL37rNmxTo8tnauXP1oYl93zo5meA1syALaxYV2Y7igb5sCGDIBtTHeDLRls6AcbMgA2saEbbMhgQzfYkMEkNqUQNceforShuUobmqvQvgqFiiqlyrCUliJfblrCr/xfURXUc8te0XPLXtHw3kNV0H2AstIzVFpRro17N6twx9qEji+ZnwNbMgC2sWFdmO4oG+bAhgyAbUx3gy0ZbOgHGzIANrGhG2zIYEM32JDBFDal0CK+vHSjC6Nwx1pPCqoppufAlgyAbWxYF6Y7yoY5sCEDYBvT3WBLBhv6wYYMgE1s6AYbMtjQDTZk8FL7Ou8LAAAAAAAAbQKbUgAAAAAAAPAcm1IAAAAAAADwnOO6ruvlgCUlJcrJyYkMnuH9Ja3cQ9WSK8mRnI5mLqlFBjLYlMH0+JLklldLkoqLi5WdnW0kg2S+nyRLjgd/J8lAhroZLOgo+okMtoxPBssy0E+SLDkWZCCDJeNbkyHKfjJ6ofOakGYGNzw+GchgWwbT41vG+FzYcDxMZzA9PhnIYCnj82DDsSCD+fHJYFcGSxifBxuOBRnIYMv4tmRohtFNKc6UIgMZzGcwPb5kZ1HyL33mMpgenwxkqJfBso6in5I7g+nxyWBZBvpJkiXHggxksGR8azJE2U/mNqU6+pQ9rcDzYUue2ii3vFpOR7+R8clABtsymB5fkornbJAOhYyM3SBD/STZcTxMZzA9PhnIcDSrOop+SvoMpscng10Z6KcIG44FGchgy/i2ZIi2n7jQOQAAAAAAADzHphQAAADgIcfb7xkCAMBaRq8pBQAAALR3I/ZVaMraIp2x67AGHwwqLSxVpkjrOwX0Xn4HPTs0V6vz0k3HBADAc2xKAS00vPdQDeo+QJnpGSqrKNeGvZtVuGOt6VieC+2rUOhgpVQVllJT5OuUJh8vrAGj6KcI+gmmDSiq1KxFuzV692FVOVLqiJHS+CFSVpbSSkt1wrp1GrJ6lW5aU6R/9uygW8f11ObcNNOxE46Oop9gH9ZlBPMQ4WVHsSkFxCA9NaBJoy7QTWOmakSf4+r9fvX2T/XY0rl6acVrqqgKGkjoDbc6rKoNJQoWHlR4f/0/Z0r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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def simulate_game(\n", " nr_of_games: int,\n", " policies: tuple[GamePolicy, GamePolicy],\n", " tqdm_on: bool = False,\n", ") -> tuple[np.ndarray, np.ndarray]:\n", " \"\"\"Simulates a stack of games.\n", "\n", " Args:\n", " nr_of_games: The number of games that should be simulated.\n", " policies: The policies that should be used to simulate the game.\n", " tqdm_on: Switches tqdm on.\n", "\n", " Returns:\n", " A stack of board histories and actions.\n", " \"\"\"\n", " board_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 8, 8), dtype=np.int8)\n", " action_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 2), dtype=np.int8)\n", " current_boards = get_new_games(nr_of_games)\n", " for turn_index in tqdm(range(SIMULATE_TURNS)) if tqdm_on else range(SIMULATE_TURNS):\n", " policy_index = (turn_index + 1) % 2\n", " policy = policies[policy_index]\n", " board_history_stack[turn_index, :, :, :] = current_boards\n", " if policy_index == 0:\n", " current_boards *= -1\n", " current_boards, action_taken = single_turn(current_boards, policy)\n", " action_history_stack[turn_index, :] = action_taken\n", "\n", " if policy_index == 0:\n", " current_boards *= -1\n", "\n", " return board_history_stack, action_history_stack\n", "\n", "\n", "simulation_results = simulate_game(1, (RandomPolicy(1), RandomPolicy(1)))\n", "_unique_bords, _unique_actions = drop_duplicate_boards(\n", " simulation_results[0].reshape(-1, 8, 8), simulation_results[1].reshape(-1, 2)\n", ")\n", "plot_othello_boards(_unique_bords, actions=_unique_actions)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(70, 8, 8)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.reshape(simulation_results[0], (-1, 8, 8)).shape" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(70, 2)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simulation_results[1].reshape(-1, 2).shape" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak memory: 341.93 MiB, increment: 0.08 MiB\n", "15 s ± 776 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%memit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n", "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "KeyboardInterrupt\n", "\n" ] } ], "source": [ "board, action = simulate_game(1, (GreedyPolicy(0), GreedyPolicy(0)))\n", "\n", "plot_othello_boards(\n", " *drop_duplicate_boards(board.reshape(-1, 8, 8), action.reshape(-1, 2))\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistical examination of the natural action space and result\n", "As for many project some evaluation of the project is in order.\n", "\n", "1. What is the expected distribution of scores\n", "2. What is the expected distribution of possible actions\n", "\n", " a. over time\n", " \n", " b. ober space\n", "\n", "The easiest and robustest way to analyse this is when analyzing randomly played games." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this purpose we played a sample of 10k games and saved them for later analysis." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n", " simulation_results = simulate_game(\n", " 10_000, (RandomPolicy(1), RandomPolicy(1)), tqdm_on=True\n", " )\n", " _board_history, _action_history = simulation_results\n", " np.save(\"rnd_history.npy\", np.astpye.astype(np.int8))\n", " np.save(\"rnd_action.npy\", _action_history.astype(np.int8))\n", "else:\n", " _board_history = np.load(\"rnd_history.npy\")\n", " _action_history = np.load(\"rnd_action.npy\")\n", "_board_history.shape, _action_history.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For those 10k games the possible actions where evaluated and saved for each and every turn in the game." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "if not os.path.exists(\"turn_possible.npy\"):\n", " __board_history = _board_history.copy()\n", " __board_history[1::2] = __board_history[1::2] * -1\n", "\n", " _poss_turns = get_possible_turns(\n", " __board_history.reshape((-1, 8, 8)), tqdm_on=True\n", " ).reshape((SIMULATE_TURNS, -1, 8, 8))\n", " np.save(\"turn_possible.npy\", _poss_turns)\n", " del __board_history\n", "_poss_turns = np.load(\"turn_possible.npy\")\n", "_poss_turns.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those possible turms then where counted for all games in the history stack." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The action space size can be drawn into a histogram by turn and a curve over the mean action space size.\n", "This can be used to analyse in which area of the game that cant be solved absolutely." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "count_poss_turns = np.sum(_poss_turns, axis=(2, 3))\n", "mean_possibility_count = np.mean(count_poss_turns, axis=1)\n", "std_possibility_count = np.std(count_poss_turns, axis=1)\n", "cum_prod = count_poss_turns\n", "\n", "\n", "@interact(turn=(0, 69))\n", "def poss_turn_count(turn):\n", " fig, axes = plt.subplots(2, 2, figsize=(15, 8))\n", " ax1, ax2, ax3, ax4 = axes.flatten()\n", " _mean_possibility_count = mean_possibility_count.copy()\n", " _std_possibility_count = std_possibility_count.copy()\n", " _mean_possibility_count[_mean_possibility_count <= 1] = 1\n", " _std_possibility_count[_std_possibility_count <= 1] = 1\n", " # np.cumprod(_mean_possibility_count[::-1], axis=0)[::-1]\n", " # todo what happens here=\n", " fig.suptitle(\n", " f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(_mean_possibility_count):.4g}\"\n", " )\n", " ax1.hist(count_poss_turns[turn], density=True)\n", " ax1.set_title(f\"Histogram of the action space size for turn {turn}\")\n", " ax1.set_xlabel(\"Action space size\")\n", " ax1.set_ylabel(\"Action space size probability\")\n", " ax2.set_title(f\"Mean size of the action space per turn\")\n", " ax2.set_xlabel(\"Turn\")\n", " ax2.set_ylabel(\"Average possible moves\")\n", "\n", " ax2.errorbar(\n", " range(70),\n", " mean_possibility_count,\n", " yerr=std_possibility_count,\n", " label=\"Mean action space size with error bars\",\n", " )\n", " ax2.scatter(turn, mean_possibility_count[turn], marker=\"x\")\n", " ax2.legend()\n", "\n", " ax4.plot(\n", " range(70),\n", " np.cumprod(_mean_possibility_count[::-1], axis=0)[::-1],\n", " # yerr=np.cumprod(_std_possibility_count[::-1], axis=0)[::-1],\n", " )\n", " ax4.scatter(\n", " turn,\n", " np.cumprod(_mean_possibility_count[::-1], axis=0)[::-1][turn],\n", " marker=\"x\",\n", " )\n", " ax4.set_yscale(\"log\", base=10)\n", " ax4.set_xlabel(\"Turn\")\n", " ax4.set_ylabel(\"Mean remaining total action space size\")\n", " fig.delaxes(ax3)\n", " fig.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is interesting to see that the action space for the first player (white) is much smaller than for the second player." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "white = mean_possibility_count[::2]\n", "black = mean_possibility_count[1::2]\n", "df = pd.DataFrame(\n", " [\n", " {\n", " \"white\": np.prod(np.extract(white, white)),\n", " \"black\": np.prod(np.extract(black, black)),\n", " }\n", " ],\n", " index=[\"Total mean action-space\"],\n", ").T\n", "del white, black\n", "df" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Hash branching" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "_board_history.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "def calculate_board_branching(board_history) -> pd.Series:\n", " assert len(board_history.shape) == 4\n", " assert board_history.shape[-2:] == (8, 8)\n", " assert board_history.shape[0] == SIMULATE_TURNS\n", " return pd.Series(\n", " [count_unique_baords(board_history[turn]) for turn in range(SIMULATE_TURNS)]\n", " )\n", "\n", "\n", "_ = calculate_board_branching(_board_history).plot(\n", " title=f\"Exploration history over {_board_history.shape[0]} games\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "_poss_turns.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mean_poss_turn = np.mean(_poss_turns, axis=1)\n", "del _poss_turns\n", "\n", "\n", "@interact(turn=(0, 69))\n", "def turn_distribution_heatmap(turn):\n", " turn_possibility_on_field = mean_poss_turn[turn]\n", "\n", " sns.heatmap(\n", " turn_possibility_on_field,\n", " linewidth=0.5,\n", " square=True,\n", " annot=True,\n", " xticklabels=\"ABCDEFGH\",\n", " yticklabels=list(range(1, 9)),\n", " )\n", " plt.title(f\"Headmap of where stones can be placed on turn {turn}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def calculate_direct_score(board_history: np.ndarray) -> np.ndarray:\n", " boards_evaluated = np.reshape(\n", " evaluate_boards(np.reshape(board_history, (-1, 8, 8))), (SIMULATE_TURNS, -1)\n", " )\n", " direct_score = boards_evaluated - np.roll(boards_evaluated, shift=-1, axis=0)\n", " direct_score[-1] = 0\n", " return direct_score / 64\n", "\n", "\n", "print(np.max(np.abs(calculate_direct_score(_board_history))))\n", "assert len(calculate_direct_score(_board_history).shape) == 2\n", "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "score_history = calculate_direct_score(_board_history) * 64\n", "score_history[1::2] = score_history[1::2] * -1\n", "\n", "\n", "@interact(turn=(0, 59))\n", "def hist_direct_score(turn):\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", " fig.suptitle(\n", " f\"Action space size analysis / total size estimate {np.prod(np.extract(mean_possibility_count, mean_possibility_count)):.4g}\"\n", " )\n", "\n", " ax1.set_title(\n", " f\"Histogram of scores on turn {turn} by {'white' if turn % 2 == 0 else 'black'}\"\n", " )\n", "\n", " ax1.hist(score_history[turn], density=True)\n", " ax1.set_xlabel(\"Points made\")\n", " ax1.set_ylabel(\"Score probability\")\n", " ax2.set_title(f\"Points scored at turn\")\n", " ax2.set_xlabel(\"Turn\")\n", " ax2.set_ylabel(\"Average points scored\")\n", "\n", " ax2.errorbar(\n", " range(60),\n", " np.mean(score_history, axis=1)[:60],\n", " yerr=np.std(score_history, axis=1)[:60],\n", " label=\"Mean score at turn\",\n", " )\n", " ax2.scatter(turn, np.mean(score_history, axis=1)[turn], marker=\"x\", color=\"red\")\n", " ax2.legend()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n", " final_evaluation = final_boards_evaluation(board_history[-1])\n", " return final_evaluation / 64\n", "\n", "\n", "print(np.max(np.abs(calculate_final_evaluation_for_history(_board_history))))\n", "assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n", "_final_eval = calculate_final_evaluation_for_history(_board_history)\n", "plt.title(\"Histogram over the score distribution\")\n", "plt.hist((_final_eval * 64), density=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n", " who_won = evaluate_who_won(board_history[-1])\n", " return who_won\n", "\n", "\n", "plt.title(\"Win distribution\")\n", "plt.bar(\n", " [\"black\", \"draw\", \"white\"],\n", " pd.Series(calculate_who_won(_board_history)).value_counts().sort_index() / 10000,\n", ")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def history_changed(board_history: np.ndarray) -> np.ndarray:\n", " return ~np.all(\n", " np.roll(board_history, shift=1, axis=0) == board_history, axis=(2, 3)\n", " )\n", "\n", "\n", "plt.title(\"Share of turns skipped\")\n", "plt.plot(1 - np.mean(history_changed(_board_history), axis=1))\n", "plt.xlabel(\"Turn\")\n", "plt.ylabel(\"Factor of skipped turns\")\n", "plt.yscale(\"log\", base=10)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_gamma_table(board_history, gamma_value: float):\n", " unchanged = history_changed(board_history)\n", " gamma_values = np.ones_like(unchanged, dtype=float)\n", " gamma_values[unchanged] = gamma_value\n", " return gamma_values\n", "\n", "\n", "get_gamma_table(_board_history, 0.8).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def calculate_q_reword(\n", " board_history: np.ndarray,\n", " who_won_fraction: float = 0.2,\n", " final_score_fraction=0.2,\n", " gamma=0.8,\n", ") -> np.ndarray:\n", " assert who_won_fraction + final_score_fraction <= 1\n", " assert final_score_fraction >= 0\n", " assert who_won_fraction >= 0\n", "\n", " gama_table = get_gamma_table(board_history, gamma)\n", " combined_score = np.zeros_like(gama_table)\n", " combined_score += calculate_direct_score(board_history) * (\n", " 1 - (who_won_fraction + final_score_fraction)\n", " )\n", " combined_score[-1] += (\n", " calculate_final_evaluation_for_history(board_history)\n", " * final_score_fraction\n", " / 0.7\n", " )\n", " combined_score[-1] += calculate_who_won(board_history) * who_won_fraction\n", " for turn in range(SIMULATE_TURNS - 1, 0, -1):\n", " values = gama_table[turn] * combined_score[turn]\n", " combined_score[turn - 1] += values\n", "\n", " return combined_score\n", "\n", "\n", "calculate_q_reword(\n", " _board_history, gamma=0.7, who_won_fraction=0, final_score_fraction=1\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "calculate_q_reword(\n", " _board_history, gamma=0.8, who_won_fraction=1, final_score_fraction=0\n", ")[:, 0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "calculate_q_reword(\n", " _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=0\n", ")[:, 0] * 64" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "def weights_init_normal(m):\n", " \"\"\"Takes in a module and initializes all linear layers with weight\n", " values taken from a normal distribution.\n", " Source: https://stackoverflow.com/a/55546528/11003343\n", " \"\"\"\n", "\n", " classname = m.__class__.__name__\n", " # for every Linear layer in a model\n", " if classname.find(\"Linear\") != -1:\n", " y = m.in_features\n", " # m.weight.data should be taken from a normal distribution\n", " m.weight.data.normal_(0.0, 1 / np.sqrt(y))\n", " # m.bias.data should be 0\n", " m.bias.data.fill_(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "BATCH_SIZE = 1000\n", "\n", "\n", "class DQLNet(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.fc1 = nn.Linear(8 * 8 * 2, 128 * 2)\n", " self.fc2 = nn.Linear(128 * 2, 128 * 3)\n", " self.fc3 = nn.Linear(128 * 3, 128 * 2)\n", " self.fc4 = nn.Linear(128 * 2, 1)\n", "\n", " def forward(self, x):\n", " if isinstance(x, np.ndarray):\n", " x = torch.from_numpy(x).float()\n", " x = torch.flatten(x, 1)\n", " x = self.fc1(x)\n", " x = F.relu(x)\n", " x = self.fc2(x)\n", " x = F.relu(x)\n", " x = self.fc3(x)\n", " x = F.relu(x)\n", " x = self.fc4(x)\n", " x = torch.tanh(x)\n", " return x\n", "\n", "\n", "class DQL_Simple(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.fc1 = nn.Linear(8 * 8 * 2, 64 * 3)\n", " self.fc2 = nn.Linear(64 * 3, 128 * 2)\n", " self.fc3 = nn.Linear(128 * 2, 1)\n", "\n", " def forward(self, x):\n", " if isinstance(x, np.ndarray):\n", " x = torch.from_numpy(x).float()\n", " x = torch.flatten(x, 1)\n", " x = self.fc1(x)\n", " x = F.relu(x)\n", " x = self.fc2(x)\n", " x = F.relu(x)\n", " x = self.fc3(x)\n", " x = torch.tanh(x)\n", " return x\n", "\n", "\n", "DQLNet().forward(np.zeros((5, 2, 8, 8)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "class SymmetryMode(Enum):\n", " MULTIPLY = \"MULTIPLY\"\n", " BREAK_SEQUENCE = \"BREAK_SEQUENCE\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "_board_history, _action_history = simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n", "_board_history.shape, _action_history.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "def action_to_q_learning_format(\n", " board_history: np.ndarray, action_history: np.ndarray\n", ") -> np.ndarray:\n", " q_learning_format = np.zeros(\n", " (SIMULATE_TURNS, board_history.shape[1], 2, 8, 8), dtype=float\n", " )\n", " q_learning_format[:, :, 0, :, :] = board_history\n", " q_learning_format[:, :, 1, :, :] = -1\n", "\n", " game_index = list(range(board_history.shape[1]))\n", " for turn_index in range(SIMULATE_TURNS):\n", " q_learning_format[\n", " turn_index,\n", " game_index,\n", " 1,\n", " action_history[turn_index, game_index, 0],\n", " action_history[turn_index, game_index, 1],\n", " ] = 1\n", " return q_learning_format\n", "\n", "\n", "# %timeit action_to_q_learning_format(_board_history, _action_history)\n", "# %memit action_to_q_learning_format(_board_history, _action_history)\n", "print(_board_history.shape)\n", "print(_action_history.shape)\n", "print(action_to_q_learning_format(_board_history, _action_history).shape)\n", "plot_othello_boards(\n", " action_to_q_learning_format(_board_history, _action_history)[:8, 0, 0]\n", ")\n", "plot_othello_boards(\n", " action_to_q_learning_format(_board_history, _action_history)[:8, 0, 1]\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "def build_symetry_action(\n", " board_history: np.ndarray, action_history: np.ndarray\n", ") -> np.ndarray:\n", " board_history = board_history.copy()\n", " board_history[1::2] *= -1\n", " q_learning_format = np.zeros(\n", " (2, 2, 2, SIMULATE_TURNS, board_history.shape[1], 2, 8, 8)\n", " )\n", " q_learning_format[0, 0, 0, :, :, :, :, :] = action_to_q_learning_format(\n", " board_history, action_history\n", " )\n", " q_learning_format[1, 0, 0, :, :, :, :, :] = np.transpose(\n", " q_learning_format[0, 0, 0, :, :, :, :, :], [0, 1, 2, 4, 3]\n", " )\n", " q_learning_format[:, 1, 0, :, :, :, :, :] = q_learning_format[\n", " :, 0, 0, :, :, :, ::-1, :\n", " ]\n", " q_learning_format[:, :, 1, :, :, :, :, :] = q_learning_format[\n", " :, :, 0, :, :, :, :, ::-1\n", " ]\n", " return q_learning_format\n", "\n", "\n", "%timeit build_symetry_action(_board_history, _action_history)\n", "%memit build_symetry_action(_board_history, _action_history)\n", "build_symetry_action(_board_history, _action_history).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "def live_history(training_history: pd.DataFrame, max_epochs: int | None):\n", " clear_output(wait=True)\n", " # plt.ylim(0, 100)\n", " _ = training_history[\n", " [c for c in training_history.columns if c[1] == \"final_score\"]\n", " ].plot()\n", " plt.xlim(0, max_epochs)\n", "\n", " plt.title(\"Training history\")\n", " plt.xlabel(\"epochs\")\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class QLPolicy(GamePolicy):\n", " def __init__(\n", " self,\n", " epsilon: float,\n", " neural_network: DQLNet,\n", " symmetry_mode: SymmetryMode,\n", " gamma: float = 0.8,\n", " who_won_fraction: float = 0,\n", " final_score_fraction: float = 0,\n", " optimizer: torch.optim.Optimizer | None = None,\n", " loss: nn.modules.loss._Loss | None = None,\n", " ):\n", " super().__init__(epsilon)\n", " assert 0 <= gamma <= 1\n", " self.gamma: float = gamma\n", " del gamma\n", " self.symmetry_mode: SymmetryMode = symmetry_mode\n", " del symmetry_mode\n", " self.neural_network: DQLNet = neural_network\n", " del neural_network\n", " self.who_won_fraction: float = who_won_fraction\n", " del who_won_fraction\n", " self.final_score_fraction: float = final_score_fraction\n", " del final_score_fraction\n", "\n", " if optimizer is None:\n", " self.optimizer = torch.optim.Adam(self.neural_network.parameters(), lr=5e-3)\n", " else:\n", " self.optimizer = optimizer\n", " if loss is None:\n", " self.loss = nn.MSELoss()\n", " else:\n", " self.loss = loss\n", " self.training_results: list[dict[tuple[str, str], float]] = []\n", "\n", " @property\n", " def policy_name(self) -> str:\n", " symmetry_name = {SymmetryMode.MULTIPLY: \"M\", SymmetryMode.BREAK_SEQUENCE: \"B\"}\n", " g = f\"{self.gamma:.1f}\".replace(\".\", \"\")\n", " ww = f\"{self.who_won_fraction:.1f}\".replace(\".\", \"\")\n", " fsf = f\"{self.final_score_fraction:.1f}\".replace(\".\", \"\")\n", " return f\"QL-{symmetry_name[self.symmetry_mode]}-G{g}-WW{ww}-FSF{fsf}-{ql_policy.neural_network.__class__.__name__}-{self.loss.__class__.__name__}\"\n", "\n", " def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n", " results = np.zeros_like(boards, dtype=float)\n", " results = torch.from_numpy(results).float()\n", " q_learning_boards = np.zeros((boards.shape[0], 2, 8, 8))\n", " q_learning_boards[:, 0, :, :] = boards\n", " poss_turns = boards == 0 # checks where fields are empty.\n", " poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n", " turn_possible = np.any(poss_turns, axis=0)\n", " for action_x, action_y in itertools.product(range(8), range(8)):\n", " if not turn_possible[action_x, action_y]:\n", " continue\n", " _q_learning_board = q_learning_boards[\n", " poss_turns[range(boards.shape[0]), action_x, action_y]\n", " ].copy()\n", " _q_learning_board[\n", " range(_q_learning_board.shape[0]), 1, action_x, action_y\n", " ] = 1\n", "\n", " ql_result = self.neural_network.forward(_q_learning_board)\n", " results[poss_turns[:, action_x, action_y], action_x, action_y] = (\n", " ql_result.reshape(-1) + 0.1\n", " )\n", " return results.cpu().detach().numpy()\n", "\n", " def generate_trainings_data(\n", " self, generate_data_size: int\n", " ) -> tuple[torch.Tensor, torch.Tensor]:\n", " train_boards, train_actions = simulate_game(generate_data_size, (self, self))\n", " action_possible = ~np.all(train_actions[:, :] == -1, axis=2)\n", " q_leaning_formatted_action = build_symetry_action(train_boards, train_actions)\n", " q_rewords = calculate_q_reword(\n", " board_history=train_boards,\n", " who_won_fraction=self.who_won_fraction,\n", " final_score_fraction=self.final_score_fraction,\n", " )\n", " q_rewords[1::2, :] *= -1\n", " if self.symmetry_mode == SymmetryMode.MULTIPLY:\n", " new_q_rewords = np.zeros((2, 2, 2) + q_rewords.shape)\n", " for i, k, j in itertools.product((0, 1), (0, 1), (0, 1)):\n", " new_q_rewords[i, k, j] = q_rewords\n", " q_rewords = new_q_rewords\n", " action_possible = np.array([action_possible] * 8).reshape(-1)\n", "\n", " elif self.symmetry_mode == SymmetryMode.BREAK_SEQUENCE:\n", " axis1 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n", " axis2 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n", " axis3 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n", " q_leaning_formatted_action = q_leaning_formatted_action[\n", " axis1, axis2, axis3, range(SIMULATE_TURNS)\n", " ]\n", " action_possible = action_possible.reshape(-1)\n", "\n", " return (\n", " torch.from_numpy(\n", " q_leaning_formatted_action.reshape(-1, 2, BOARD_SIZE, BOARD_SIZE)[\n", " action_possible\n", " ]\n", " ).float(),\n", " torch.from_numpy(q_rewords.reshape(-1, 1)[action_possible]).float(),\n", " )\n", "\n", " def train_batch(self, nr_of_games: int):\n", " x_train, y_train = self.generate_trainings_data(nr_of_games)\n", " y_pred = self.neural_network.forward(x_train)\n", " loss_score = self.loss(y_pred, y_train)\n", " self.optimizer.zero_grad()\n", "\n", " loss_score.backward()\n", " # Update the parameters\n", " self.optimizer.step()\n", " # generate trainings data\n", "\n", " def evaluate_model(self, compare_models: list[GamePolicy], nr_of_games: int):\n", " result_dict: dict[tuple[str, str], float] = {}\n", " eval_copy = copy.copy(self)\n", " eval_copy._epsilon = 1\n", " for model in compare_models:\n", " boards_white, _ = simulate_game(nr_of_games, (eval_copy, model))\n", " boards_black, _ = simulate_game(nr_of_games, (model, eval_copy))\n", " win_eval_white = evaluate_who_won(boards_white[-1])\n", " win_eval_black = evaluate_who_won(boards_black[-1])\n", " result_dict[(model.policy_name, \"final_score\")] = float(\n", " np.mean(\n", " final_boards_evaluation(boards_white[-1])\n", " + final_boards_evaluation(boards_black[-1]) * -1\n", " )\n", " )\n", " result_dict[(model.policy_name, \"white_win\")] = (\n", " np.sum(win_eval_white == 1) / nr_of_games\n", " )\n", " result_dict[(model.policy_name, \"white_lose\")] = (\n", " np.sum(win_eval_white == -1) / nr_of_games\n", " )\n", " result_dict[(model.policy_name, \"black_win\")] = (\n", " np.sum(win_eval_black == 1) / nr_of_games\n", " )\n", " result_dict[(model.policy_name, \"black_lose\")] = (\n", " np.sum(win_eval_black == -1) / nr_of_games\n", " )\n", " result_dict[(\"base\", \"base\")] = nr_of_games\n", " return result_dict\n", "\n", " def save(self):\n", " filename: str = f\"{self.policy_name}-{len(self.training_results)}\"\n", " with open(TRAINING_RESULT_PATH / Path(f\"{filename}.pickle\"), \"wb\") as f:\n", " pickle.dump(self.training_results, f)\n", " torch.save(\n", " self.neural_network.state_dict(),\n", " TRAINING_RESULT_PATH / Path(f\"{filename}.torch\"),\n", " )\n", "\n", " def load(self):\n", " pickle_files = glob.glob(f\"{TRAINING_RESULT_PATH}/{self.policy_name}-*.pickle\")\n", " torch_files = glob.glob(f\"{TRAINING_RESULT_PATH}/{self.policy_name}-*.torch\")\n", "\n", " assert len(pickle_files) == len(torch_files)\n", " if not pickle_files:\n", " return\n", "\n", " pickle_dict = {\n", " int(file.split(\"-\")[-1].split(\".\")[0]): file for file in pickle_files\n", " }\n", " torch_dict = {\n", " int(file.split(\"-\")[-1].split(\".\")[0]): file for file in torch_files\n", " }\n", " pickle_file = pickle_dict[max(pickle_dict.keys())]\n", " torch_file = torch_dict[max(torch_dict.keys())]\n", "\n", " with open(pickle_file, \"rb\") as f:\n", " self.training_results = pickle.load(f)\n", "\n", " self.neural_network.load_state_dict(torch.load(Path(torch_file)))\n", "\n", " def train(\n", " self,\n", " epochs: int,\n", " batches: int,\n", " batch_size: int,\n", " eval_batch_size: int,\n", " compare_with: list[GamePolicy],\n", " save_every_epoch: bool = True,\n", " live_plot: bool = True,\n", " ) -> pd.DataFrame:\n", " max_epochs = epochs + len(self.training_results)\n", " assert epochs > 0\n", " for _ in tqdm(range(epochs)):\n", " for _ in tqdm(range(batches)):\n", " self.train_batch(batch_size)\n", " self.training_results.append(\n", " self.evaluate_model(compare_with, eval_batch_size)\n", " )\n", " if save_every_epoch:\n", " self.save()\n", " if live_plot:\n", " self.plot_history(max_epochs)\n", " return self.history\n", "\n", " def plot_history(self, max_epochs: int | None):\n", " return live_history(self.history, max_epochs)\n", "\n", " @property\n", " def history(self) -> pd.DataFrame:\n", " pandas_result = pd.DataFrame(self.training_results)\n", " pandas_result.columns = pd.MultiIndex.from_tuples(pandas_result.columns)\n", " return pandas_result\n", "\n", "\n", "ql_policy1 = QLPolicy(\n", " 0.95,\n", " neural_network=DQLNet(),\n", " symmetry_mode=SymmetryMode.MULTIPLY,\n", " gamma=0.8,\n", " who_won_fraction=1,\n", " final_score_fraction=0,\n", ")\n", "t1, t2 = ql_policy1._internal_policy(get_new_games(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Symmetry debug" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "train_boards, train_actions = simulate_game(10, (RandomPolicy(0), RandomPolicy(0)))\n", "action_possible = ~np.all(train_actions[:, :] == -1, axis=2)\n", "q_leaning_formatted_action = action_to_q_learning_format(train_boards, train_actions)\n", "train_boards.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "plot_othello_boards(train_boards[:8, 0])\n", "plot_othello_boards(q_leaning_formatted_action[0:9, 0, 1])" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "ql_policy = QLPolicy(\n", " 0.95,\n", " neural_network=DQLNet(),\n", " symmetry_mode=SymmetryMode.MULTIPLY,\n", " gamma=0.8,\n", " who_won_fraction=0,\n", " final_score_fraction=0,\n", ")\n", "_batch_size = 100\n", "%timeit ql_policy.train_batch(_batch_size)\n", "%memit ql_policy.train_batch(_batch_size)\n", "%timeit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)\n", "%memit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "ql_policy = QLPolicy(\n", " 0.95,\n", " neural_network=DQLNet(),\n", " symmetry_mode=SymmetryMode.MULTIPLY,\n", " gamma=0.8,\n", " who_won_fraction=1,\n", " final_score_fraction=0,\n", ")\n", "ql_policy.policy_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "ql_policy = QLPolicy(\n", " 0.95,\n", " neural_network=DQL_Simple(),\n", " symmetry_mode=SymmetryMode.MULTIPLY,\n", " gamma=0.8,\n", " who_won_fraction=0,\n", " final_score_fraction=1,\n", ")\n", "ql_policy.policy_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "# pd.Series.plot?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "probes: int = 1000\n", "_ = (\n", " calculate_board_branching(simulate_game(probes, (ql_policy, ql_policy))[0]) / probes\n", ").plot(\n", " ylim=(0, 1),\n", " title=f\"Branching rate for a QL policy with epsilon={ql_policy.epsilon}\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "ql_policy.load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "ql_policy.plot_history(None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "ql_policy.train(200, 10, 1000, 200, [RandomPolicy(0), GreedyPolicy(0)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "raise NotImplementedError" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "boards_and_actions, score = ql_policy.generate_trainings_data(1)\n", "print(boards_and_actions.shape)\n", "print(score.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "boards_and_actions.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "plot_othello_boards(boards_and_actions[:8, 0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "score[:8, 0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "plot_othello_boards(boards1[:60, 0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train a model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sources\n", "\n", "* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n", "* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n", "* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n", "* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "\n", "def sizeof_fmt(num, suffix=\"B\"):\n", " \"\"\"by Fred Cirera, https://stackoverflow.com/a/1094933/1870254, modified\"\"\"\n", " for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n", " if abs(num) < 1024.0:\n", " return \"%3.1f %s%s\" % (num, unit, suffix)\n", " num /= 1024.0\n", " return \"%.1f %s%s\" % (num, \"Yi\", suffix)\n", "\n", "\n", "for name, size in sorted(\n", " ((name, sys.getsizeof(value)) for name, value in list(locals().items())),\n", " key=lambda x: -x[1],\n", ")[:20]:\n", " print(\"{:>30}: {:>8}\".format(name, sizeof_fmt(size)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.8" }, "toc-autonumbering": true, "toc-showcode": false }, "nbformat": 4, "nbformat_minor": 4 }