ConnectX-kaggle-competition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ConnectX Kaggle Competition\n",
    "\n",
    "https://www.kaggle.com/c/connectx/overview/environment-rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.7.7'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from platform import python_version\n",
    "python_version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Test Kaggle Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from kaggle_environments import evaluate, make, utils\n",
    "\n",
    "env = make(\"connectx\", debug=True)\n",
    "env.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load my Game Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from gamelearner import RandomPlayer, HumanPlayer, GameController\n",
    "from connectx import Connect4, Connect4BasicPlayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X _ _ _ _ _ _\n",
      "O _ _ _ _ _ _\n",
      "O _ _ _ _ _ _\n",
      "X _ O _ _ _ X\n",
      "O X X O O O O\n",
      "X X O X X O X\n",
      "Winner: 2\n"
     ]
    }
   ],
   "source": [
    "game = Connect4()\n",
    "while not game.game_over:\n",
    "    role = game.turn\n",
    "    moves = game.available_moves()\n",
    "    move = np.random.choice(moves)\n",
    "    game.make_move((role, move))\n",
    "game.show_state()\n",
    "print(f\"Winner: {game.winner}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Competition Submission File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests complete\n"
     ]
    }
   ],
   "source": [
    "def my_agent(observation, configuration, run_tests=False):\n",
    "\n",
    "    import numpy as np\n",
    "\n",
    "    # Game parameters\n",
    "    _connect = 4\n",
    "    _shape = (6, 7)\n",
    "    \n",
    "    # Algorithm parameters\n",
    "    _terminal_values = {'win': 1, 'loss': -1, 'draw':0}\n",
    "\n",
    "    # Data objects for analyzing board\n",
    "    _steps = {\n",
    "        'u': (1, 0),\n",
    "        'd': (-1, 0),\n",
    "        'r': (0, 1),\n",
    "        'l': (0, -1),\n",
    "        'ur': (1, 1),\n",
    "        'dr': (-1, 1),\n",
    "        'ul': (1, -1),\n",
    "        'dl': (-1, -1)\n",
    "    }\n",
    "\n",
    "    # Function used by _check_positions method\n",
    "    _fcum = lambda x1, x2: (x1 + x2)*x2\n",
    "\n",
    "    def empty_board_state():\n",
    "        \"\"\"Initialize board_full and state.\"\"\"\n",
    "        # board_full has a border set to -1\n",
    "        board_full = -np.ones(np.array(_shape) + (2, 2), dtype='int8')\n",
    "        state = state_from_board_full(board_full)\n",
    "        state[:] = 0\n",
    "        return board_full, state\n",
    "\n",
    "    def get_fill_levels(state):\n",
    "        # Note: This assumes proper filling!\n",
    "        return (state > 0).sum(axis=0)\n",
    "\n",
    "    def state_from_board_full(board_full):\n",
    "        return board_full[1:1+_shape[0], 1:1+_shape[1]]\n",
    "\n",
    "    def available_moves(fill_levels):\n",
    "        \"\"\"Returns list of available (empty) moves (slots).\"\"\"\n",
    "        spaces_left = fill_levels < _shape[0]\n",
    "        return np.nonzero(spaces_left)[0]\n",
    "\n",
    "    def chain_in_direction(position, direction, role, board_full):\n",
    "        \"\"\"Finds number of matching discs in one direction.\"\"\"\n",
    "        step = _steps[direction]\n",
    "        for i in range(_connect):\n",
    "            position = (step[0]+position[0], step[1]+position[1])\n",
    "            x = board_full[position]\n",
    "            if x != role:\n",
    "                break\n",
    "        return i\n",
    "\n",
    "    def check_game_state_from_position(position, role, board_full):\n",
    "        results = {}\n",
    "        for direction, step in _steps.items():\n",
    "            n = chain_in_direction(position, direction, role, board_full)\n",
    "            if n == _connect - 1:\n",
    "                return True\n",
    "            results[direction] = n\n",
    "        for d1, d2 in [('u', 'd'), ('l', 'r'), ('ul', 'dr'), ('dl', 'ur')]:\n",
    "            if results[d1] + results[d2] >= _connect-1:\n",
    "                return True\n",
    "        return False\n",
    "\n",
    "    def check_game_state_after_move(move, board_full, state=None):\n",
    "        if state is None:\n",
    "            state = state_from_board_full(board_full)\n",
    "        fill_levels = get_fill_levels(state)\n",
    "        role, column = move\n",
    "        level = fill_levels[column]\n",
    "        assert level < _shape[0]\n",
    "        position = (level+1, column+1)\n",
    "        assert board_full[position] == 0\n",
    "        return check_game_state_from_position(position, role, \n",
    "                                              board_full=board_full)\n",
    "\n",
    "    def wins_from_next_move(role, board_full, state=None, moves=None):\n",
    "        if state is None:\n",
    "            state = state_from_board_full(board_full)\n",
    "        fill_levels = get_fill_levels(state)\n",
    "        if moves is None:\n",
    "            moves = available_moves(fill_levels)\n",
    "        wins = {}\n",
    "        for col in moves:\n",
    "            pos_fb = (fill_levels[col]+1, col+1)\n",
    "            win = check_game_state_from_position(pos_fb, role, \n",
    "                                                 board_full=board_full)\n",
    "            wins[col] = win\n",
    "        return wins\n",
    "\n",
    "    def check_for_obvious_move(role, board_full, state=None, \n",
    "                               fill_levels=None,\n",
    "                               terminal_values=_terminal_values, depth=1):\n",
    "        \"\"\"Analyses the current board state (board_full) from the \n",
    "        perspective of the player role.\n",
    "\n",
    "        Returns\n",
    "            value, positions (float, list): value of the current\n",
    "                state if it is a terminal state (from terminal_values) \n",
    "                else None, and a list of best positions (columns) to \n",
    "                play on next move.\n",
    "        \"\"\"\n",
    "        if state is None:\n",
    "            state = state_from_board_full(board_full)\n",
    "        if fill_levels is None:\n",
    "            fill_levels = get_fill_levels(state)\n",
    "\n",
    "        # 0. Check if board full (draw):\n",
    "        if fill_levels.sum() == _shape[0]*_shape[1]:\n",
    "            print(\"Check_for_obvious_move called with full board\")\n",
    "            breakpoint()\n",
    "            #raise ValueError(\"No available moves\")\n",
    "\n",
    "        opponent = role ^ 3\n",
    "        win_value, loss_value = (terminal_values['win'], \n",
    "                                 terminal_values['loss'])\n",
    "\n",
    "        # 1. Check for a win by role on next move\n",
    "        possible_moves = wins_from_next_move(role, board_full=board_full, \n",
    "                                             state=state)\n",
    "        n_wins = sum(possible_moves.values())\n",
    "        if n_wins > 0:\n",
    "            winning_moves = [col for col, win in possible_moves.items() if win]\n",
    "            return win_value, winning_moves\n",
    "\n",
    "        if len(possible_moves) == 1:\n",
    "            # 2. Check if draw (last move but no win)\n",
    "            if fill_levels.sum() == _shape[0]*_shape[1] - 1:\n",
    "                return terminal_values['draw'], list(possible_moves.keys())\n",
    "\n",
    "        #TODO: Could continue deeper search if only one move possible\n",
    "        if depth > 0:\n",
    "            # 3. Check what opponent could do next for each possible move\n",
    "            bf2 = board_full.copy()  # TODO: Could remove if sure\n",
    "            state = state_from_board_full(bf2)\n",
    "            fill_levels = get_fill_levels(state)\n",
    "            opp_wins = {}\n",
    "            opp_losses = {}\n",
    "            other_moves = []\n",
    "            opp_move_values = {}\n",
    "            for col in possible_moves:\n",
    "                assert state[fill_levels[col], col] == 0  # TODO: delete later\n",
    "                state[fill_levels[col], col] = role  # Next state after move\n",
    "                fill_levels[col] += 1\n",
    "                value, moves = check_for_obvious_move(opponent, board_full=bf2, \n",
    "                                                      state=state, \n",
    "                                                      fill_levels=fill_levels, \n",
    "                                                      depth=depth-1)\n",
    "                opp_move_values[col] = value\n",
    "                if value == win_value:\n",
    "                    opp_wins[col] = len(moves)\n",
    "                elif value == loss_value:\n",
    "                    opp_losses[col] = len(moves)\n",
    "                else:\n",
    "                    other_moves.append(col)\n",
    "                # Reverse move\n",
    "                fill_levels[col] -= 1\n",
    "                state[fill_levels[col], col] = 0\n",
    "\n",
    "            # 4. Take any move where opponent will definitely lose\n",
    "            if len(opp_losses) > 0:\n",
    "                return win_value, [col for col, value in opp_move_values.items()\n",
    "                                   if value == loss_value]\n",
    "\n",
    "            # 5. If opponent will possibly win for all moves, assume defeat\n",
    "            if len(opp_wins) == len(possible_moves):\n",
    "                fewest = [col for col, n_win_moves in opp_wins.items()\n",
    "                          if n_win_moves == min(opp_wins.values())]\n",
    "                return loss_value, fewest\n",
    "\n",
    "            # 6. Avoid any move where opponent will definitely win\n",
    "            if len(opp_wins) > 0:\n",
    "                return None, other_moves\n",
    "\n",
    "        # Otherwise, return no value\n",
    "        return None, list(possible_moves.keys())\n",
    "\n",
    "    def tests():\n",
    "\n",
    "        # Tests\n",
    "        board_full, state = empty_board_state()\n",
    "        assert np.array_equal(state, np.zeros(_shape, dtype='int8'))\n",
    "        fill_levels = get_fill_levels(state)\n",
    "        assert np.all(fill_levels == 0)\n",
    "        assert (available_moves(fill_levels).tolist()\n",
    "                == [0, 1, 2, 3, 4, 5, 6])\n",
    "        role = 1\n",
    "        assert (list(wins_from_next_move(role, board_full=board_full).keys())\n",
    "                == list(range(7)))\n",
    "        assert not any(wins_from_next_move(role, board_full=board_full).values())\n",
    "        assert not any(wins_from_next_move(role, board_full=board_full, \n",
    "                                           state=state).values())\n",
    "        moves = check_for_obvious_move(role, board_full, state=state)\n",
    "        assert moves == (None, [0, 1, 2, 3, 4, 5, 6])\n",
    "        moves = check_for_obvious_move(role, board_full, \n",
    "                                       fill_levels=fill_levels, depth=3)\n",
    "        assert moves == (None, [0, 1, 2, 3, 4, 5, 6])\n",
    "        state[:] = np.array([\n",
    "            [0, 0, 1, 1, 2, 0, 2],\n",
    "            [0, 0, 1, 1, 1, 0, 2],\n",
    "            [0, 0, 2, 1, 0, 0, 2],\n",
    "            [0, 0, 0, 2, 0, 0, 1],\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0]\n",
    "        ])\n",
    "        role = 2\n",
    "        moves = check_for_obvious_move(role, board_full, state=state, depth=0)\n",
    "        assert moves == (None, [0, 1, 2, 3, 4, 5, 6])\n",
    "        moves = check_for_obvious_move(role, board_full, state=state, depth=1)\n",
    "        assert moves == (None, [0, 2, 3, 4, 6])\n",
    "        moves = check_for_obvious_move(role, board_full, state=state, depth=2)\n",
    "        assert moves == (None, [0, 2, 3, 4, 6])\n",
    "        moves = check_for_obvious_move(role, board_full, state=state, depth=3)\n",
    "        assert moves == (-1, [1, 5])\n",
    "        print(\"Tests complete\")\n",
    "    \n",
    "    if run_tests:\n",
    "        tests()\n",
    "\n",
    "    else:\n",
    "        columns = configuration.columns\n",
    "        rows = configuration.rows\n",
    "        inarow = configuration.inarow\n",
    "        role = observation.mark\n",
    "        assert columns == _shape[1], \"columns not valid\"\n",
    "        assert rows == _shape[0], \"rows not valid\"\n",
    "        assert inarow == _connect, \"inarow not valid\"\n",
    "        assert role in (1, 2), \"role not valid\"\n",
    "        board = np.array(observation.board).reshape(_shape)\n",
    "\n",
    "        board_full, state = empty_board_state()\n",
    "        state[:] = np.flip(board, axis=0)\n",
    "        value, cols = check_for_obvious_move(role, board_full, \n",
    "                                             state=state, depth=3)\n",
    "        col = np.random.choice(cols)\n",
    "        assert col in range(columns), \"col not valid\"\n",
    "\n",
    "        return int(col)\n",
    "\n",
    "\n",
    "my_agent(None, None, run_tests=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test on Kaggle Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My Action 3\n",
      "My Action 0\n",
      "My Action 3\n",
      "My Action 0\n",
      "My Action 0\n",
      "My Action 0\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 0 | 0 | 1 | 2 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 0 | 1 | 2 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "env = make(\"connectx\", debug=True)\n",
    "\n",
    "# Play as first position against random agent.\n",
    "trainer = env.train([None, \"random\"])\n",
    "\n",
    "observation = trainer.reset()\n",
    "\n",
    "while not env.done:\n",
    "    my_action = my_agent(observation, env.configuration)\n",
    "    print(\"My Action\", my_action)\n",
    "    observation, reward, done, info = trainer.step(my_action)\n",
    "    # env.render(mode=\"ipython\", width=100, height=90, header=False, controls=False)\n",
    "\n",
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, -1], [1, -1], [1, -1], [1, -1], [1, -1], [1, -1], [1, -1], [1, -1], [1, -1], [1, -1]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate against random agent.\n",
    "rewards = evaluate(\"connectx\", [my_agent, \"random\"], num_episodes=10)\n",
    "print(rewards)\n",
    "np.mean([r[0] for r in rewards])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1, 1], [-1, 1], [-1, 1], [-1, 1], [-1, 1], [-1, 1], [-1, 1], [-1, 1], [-1, 1], [-1, 1]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate against random agent.\n",
    "rewards = evaluate(\"connectx\", [\"random\", my_agent], num_episodes=10)\n",
    "print(rewards)\n",
    "np.mean([r[0] for r in rewards])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, -1], [1, -1], [0, 0], [1, -1], [1, -1], [-1, 1], [1, -1], [1, -1], [1, -1], [1, -1]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate against negamax agent.\n",
    "rewards = evaluate(\"connectx\", [my_agent, \"negamax\"], num_episodes=10)\n",
    "print(rewards)\n",
    "np.mean([r[0] for r in rewards])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Depth 3 (100 episodes):\n",
    "# random: 1.0\n",
    "# negamax: 0.22 - 0.25\n",
    "\n",
    "# Depth 4 (100 episodes):\n",
    "# random: 1.0\n",
    "# negamax: 0.47"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 0 | 1 | 0 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 0 | 2 | 0 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 1 | 0 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 1 | 1 | 1 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 1 | 1 | 1 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 1 | 2 | 1 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 2 | 0 | 0 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 1 | 1 | 1 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 1 | 2 | 2 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 1 | 1 | 2 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 1 | 1 | 2 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 2 | 0 | 0 | 1 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 1 | 1 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 1 | 1 | 2 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 2 | 1 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 2 | 2 | 2 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 2 | 1 | 1 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
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      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 0 | 0 | 0 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 2 | 0 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 1 | 2 | 0 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 2 | 1 | 2 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 1 | 1 | 2 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 1 | 0 | 2 | 0 | 1 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 2 | 0 | 1 | 1 | 2 | 0 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 2 | 0 | 2 | 2 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 2 | 0 | 1 | 1 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 1 | 1 | 2 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 1 | 2 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 1 | 0 | 2 | 0 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 1 | 1 | 1 | 0 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 2 | 2 | 1 | 0 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 2 | 1 | 2 | 2 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 2 | 1 | 1 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 1 | 2 | 2 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 2 | 1 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 1 | 1 | 2 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 2 | 1 | 1 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 1 | 1 | 2 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 1 | 2 | 2 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 0 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 2 | 1 | 0 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 0 | 2 | 2 | 0 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 0 | 1 | 2 | 1 | 2 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 1 | 1 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 2 | 2 | 1 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 1 | 1 | 2 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 1 | 2 | 1 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 1 | 1 | 1 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 2 | 1 | 1 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 2 | 1 | 2 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Play against itself\n",
    "for _ in range(10):\n",
    "    env = make(\"connectx\", debug=True)\n",
    "    env.run([my_agent, my_agent])\n",
    "    env.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log moves for Debugging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 3), (2, 6)]\n",
      "[(1, 4), (2, 2)]\n",
      "[(1, 6), (2, 5)]\n",
      "[(1, 2), (2, 5)]\n",
      "[(1, 6), (2, 5)]\n",
      "[(1, 5), (2, 3)]\n",
      "[(1, 4), (2, 3)]\n",
      "[(1, 4), (2, 4)]\n",
      "[(1, 5), (2, 4)]\n",
      "[(1, 6), (2, 6)]\n",
      "[(1, 1), (2, 5)]\n",
      "[(1, 2), (2, 0)]\n",
      "[(1, 2), (2, 2)]\n",
      "[(1, 1), (2, 6)]\n",
      "[(1, 1), (2, 1)]\n",
      "[(1, 0), (2, 4)]\n",
      "[(1, 0), (2, 0)]\n",
      "[(1, 1), (2, 1)]\n",
      "[(1, 0), (2, 2)]\n",
      "[(1, 0), (2, 3)]\n",
      "[(1, 3)]\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 2 | 2 | 0 | 2 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 2 | 1 | 2 | 1 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 2 | 1 | 2 | 2 | 1 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 1 | 2 | 1 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 1 | 1 | 1 | 2 | 1 | 2 | 1 |\n",
      "+---+---+---+---+---+---+---+\n",
      "| 2 | 1 | 2 | 1 | 1 | 2 | 2 |\n",
      "+---+---+---+---+---+---+---+\n",
      "\n",
      "Winner 1\n"
     ]
    }
   ],
   "source": [
    "env = make(\"connectx\", debug=True)\n",
    "\n",
    "# Play as first position against other agent.\n",
    "trainer = env.train([None, \"negamax\"])\n",
    "_shape = (env.configuration.rows, env.configuration.columns)\n",
    "\n",
    "def get_board_as_flipped_array(observation):\n",
    "    return np.flip(np.array(observation.board).reshape(_shape), axis=0)\n",
    "\n",
    "my_role = observation.mark\n",
    "opp_role = observation.mark ^ 3\n",
    "observation = trainer.reset()\n",
    "prev_state = get_board_as_flipped_array(observation)\n",
    "moves = []\n",
    "while not env.done:\n",
    "    my_action = my_agent(observation, env.configuration)\n",
    "    observation, reward, done, info = trainer.step(my_action)\n",
    "    new_state = get_board_as_flipped_array(observation)\n",
    "    changes = new_state - prev_state\n",
    "    new_moves = []\n",
    "    for role in (1, 2):\n",
    "        if role in changes:\n",
    "            rows, cols = np.nonzero(changes == role)\n",
    "            assert len(rows) == 1\n",
    "            new_moves.append((role, cols[0]))\n",
    "    print(new_moves)\n",
    "    moves = moves + new_moves\n",
    "    prev_state = new_state\n",
    "\n",
    "assert env.done\n",
    "env.render()\n",
    "state = np.array(env.state[0]['observation']['board']).reshape((6,7))\n",
    "if reward == 0:\n",
    "    print(\"Draw\")\n",
    "else:\n",
    "    winner = my_role if reward == 1 else opp_role\n",
    "    print(\"Winner\", winner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(moves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X O O _ O O O\n",
      "X X O X O X O\n",
      "O O X O O X X\n",
      "X X X O X O X\n",
      "X X X O X O X\n",
      "O X O X X O O\n",
      "\n",
      "Winner: 1\n"
     ]
    }
   ],
   "source": [
    "game = Connect4(moves=moves)\n",
    "game.show_state()\n",
    "if game.game_over:\n",
    "    if game.winner:\n",
    "        print(\"\\nWinner:\", game.winner)\n",
    "    else:\n",
    "        print(\"\\nDraw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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