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path: root/two_revised_snake_q_network.ipynb
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73c6d255-0c32-4895-9a22-e95eadb25103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pygame 2.5.1 (SDL 2.28.2, Python 3.11.5)\n",
      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import namedtuple\n",
    "from IPython.core.debugger import Pdb\n",
    "from IPython.display import display, clear_output\n",
    "\n",
    "from QNetwork import neuralnetwork_regression as nn\n",
    "from GameEngine import multiplayer\n",
    "from QTable import qtsnake\n",
    "\n",
    "Point = namedtuple('Point', 'x, y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3aab739-e016-4700-89c9-41f3c2f536cf",
   "metadata": {},
   "source": [
    "### Representing Q-function using Neural Networks\n",
    "\n",
    "In the last notebook, I represented my Q-function in a simple lookup table. This notebook offers a difference approach by using a neural network. The function we want the neural network to learn is, of course, the snake's Q-function, which maps a state-action pair onto an expected reward.\n",
    "\n",
    "#### Benefits of a Q-network\n",
    "\n",
    "The distinction between a Q-table and Q-network is that a Q-network contains and updates a set of parameters (weights) which summarize previously seen data. A Q-table cannot do this, and thus is completely clueless in situations where it recieves an input it has either not seen, or has not been trained on. In theory, this allows a neural network to not only represent environments with many more states, but also the ability to make guesses about in 'gaps' in its learning.\n",
    "\n",
    "This notebook will go over training of a simple q-network, which maps a total of 32 different combinations of states and actions onto rewards, much like the previous q-table implementation from ***one_revised_snake_q_table.ipynb***.\n",
    "\n",
    "First, I will set up the game environment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "682a7036-4f0d-4f3d-b147-6355c0a2f93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defines game window size and block size, in pixels\n",
    "WINDOW_WIDTH = 480\n",
    "WINDOW_HEIGHT = 320\n",
    "GAME_UNITS = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41cfbec9-e14e-4c58-95dd-2e3fb1788e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
    "                                    window_height=WINDOW_HEIGHT,\n",
    "                                    units=GAME_UNITS,\n",
    "                                    g_speed=35,\n",
    "                                    s_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "804a13dc-7dd4-43f0-bc47-e781bc022075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game starting with 1 players.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = game_engine.add_player()\n",
    "game_engine.start_game()\n",
    "p1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34efdb66-7a8e-4b48-a015-d1eb8a029915",
   "metadata": {},
   "source": [
    "Training thousands of steps is a little bit slow with the graphics on. It makes only a small difference here, but it provides little information anyways. So, I introduced a function to turn the drawing off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acabac69-a92d-4415-b4ef-251fd1e965f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Draw is now False.\n"
     ]
    }
   ],
   "source": [
    "game_engine.toggle_draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cefedf-e005-4910-9b4c-953697aa3f26",
   "metadata": {},
   "source": [
    "### State-sensing methods, defining reinforcement and greedy-action selector\n",
    "\n",
    "I have also imported the aforementioned q_table implementation as qtsnake. It will come back in the end of the notebook when I pair the q_table and q_network against each other, but to make the game fair, I'll use the exact same state-sensing method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71c97804-74d3-4248-bdb7-5519aa02b556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function QTable.qtsnake.sense_goal(head, goal)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtsnake.sense_goal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e065f223-9e19-4f21-ba75-8d44fc62d353",
   "metadata": {},
   "source": [
    "Even though I plan to only call it when selecting a greedy_action, I'll wrap it in a neat 'query_state' function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26b8f8bf-ad08-40f8-847f-88351e262c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_state(id):\n",
    "    '''\n",
    "    given a player's id,\n",
    "    returns their state\n",
    "    '''\n",
    "    heads, _, goal = game_engine.get_heads_tails_and_goal()\n",
    "    return np.array(qtsnake.sense_goal(heads[id], goal))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d61e508-0661-4893-a720-f0a511c52809",
   "metadata": {},
   "source": [
    "And now the reinforcement function. Because I took the requirement to sense danger away, we only need two outputs from the reinforcement function. In almost every case, the snake is not allowed to choose an action that would collide with its own tail.\n",
    "\n",
    "The output of this function was chosen due to being the best-performing. In actuality, the reinforcement for non-goals will never be used. I prefer the simplicity of using the discount factor to force agents to the goal quickly. This is because, with larger discount, the snake prioritizes actions that result in more immediate rewards. An alternative approach which a tried is to punish the agent for each unneccessary step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0af0a115-83b9-498a-8228-dc79580131f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reinforcement(outcome):\n",
    "    '''\n",
    "    given an outcome of an action,\n",
    "    returns associated reward\n",
    "    '''\n",
    "    if outcome == multiplayer.CollisionType.GOAL:\n",
    "        return -3\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e6040c-9aae-4f9e-8ef6-cf23b4043622",
   "metadata": {},
   "source": [
    "For this version of the epsilon greedy function, I wanted an interface similar to the ***one_revised_snake_q_table.ipynb*** notebook. The function operates in the same way, by accumulating the expected reward for each action taken in a state into a list, and then returning the argmin of those actions. I return the expected reward for this action in addition, because it is needed later for learning with discounted rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a76fd63a-478a-43ad-91ce-df1dff03e565",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pick_greedy_action(q_net, id, epsilon):\n",
    "    '''\n",
    "    given a q network, the id of the player\n",
    "    taking action, and a randomization factor,\n",
    "    returns the most rewarding non-lethal action\n",
    "    or a non-lethal random action and expected reward\n",
    "    '''\n",
    "    viable_actions = game_engine.get_viable_actions(id)\n",
    "    state = query_state(id)\n",
    "\n",
    "    if viable_actions.size < 1:\n",
    "        best_action = 0\n",
    "    elif np.random.uniform() < epsilon:\n",
    "        best_action = np.random.choice(viable_actions)\n",
    "    else:\n",
    "        qs = [q_net.use(np.hstack(\n",
    "            (state, action)).reshape((1, -1))) for action in viable_actions]\n",
    "        best_action = viable_actions[np.argmin(qs)]\n",
    "\n",
    "    X = np.hstack((state, best_action))\n",
    "    q = q_net.use(X.reshape((1, -1)))\n",
    "\n",
    "    return X, q"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c87558b-e6ce-4db2-a0cb-7bdb5dd70c75",
   "metadata": {},
   "source": [
    "### Q-Learning with Temporal Difference, One Sample at a Time\n",
    "\n",
    "Unlike the marble implementation, I have created a similar training loop to what was observed in the q-table, without the use of a make samples function. This means I adjust each weight for a single sample at a time (batch size 1), assigning the output of each intermediate step to the discounted rewards of future steps, 'bootstrapping' the learning process similar to the temporal difference equation. Remember, the nature of this method is somewhat recursive, as it updates Q to agree with max(Q'), which in turn is updated to agree with max(Q'')..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06cd085e-77f4-4a22-9b1f-ec364b7737c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_q(q, old_X, new_X, new_q, outcome, n_epochs, discount=0.9, lr=0.2):\n",
    "    '''\n",
    "    given a q network, the previous state/action pair,\n",
    "    the new state/action pair, the expected next reward,\n",
    "    the outcome of the last action, the number of epochs,\n",
    "    a discount factor (gamma), and the learning rate\n",
    "    updates q with discounted rewards.\n",
    "    '''\n",
    "    reward = reinforcement(outcome)\n",
    "    if outcome == multiplayer.CollisionType.GOAL:\n",
    "        q.train(np.array([new_X]),\n",
    "                np.array([reward]) + np.array([[reward]]),\n",
    "                n_epochs, lr, method='sgd', verbose=False)\n",
    "    else:\n",
    "        q.train(np.array([old_X]),\n",
    "                discount * np.array([new_q]), n_epochs,\n",
    "                lr, method='sgd', verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e8aa26-d334-49d3-8640-ede35ba6f1ae",
   "metadata": {},
   "source": [
    "#### Training\n",
    "\n",
    "In this case, I already know our game world is limited to 32 inputs. In this minimal case, I don't neccessarily care if the network is generalizable, so there is no real need for a test set, and no real downside of overfitting. My learning process will simply run the experiment for a set amount of steps.\n",
    "\n",
    "Through use of my exploration strategy, as well as a randomly initialized set of weights, the data passed into the neural network should thouroughly account for all possible inputs.\n",
    "\n",
    "To start, I initialize a few hyperparameters, discovered largely through trial-and-error, and create a new Q-network object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f51c3238-c918-40a5-bf38-1456f4ed4ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 0.9\n",
    "n_epochs = 10\n",
    "learning_rate = 0.015\n",
    "\n",
    "hidden_layers = [10]\n",
    "q = nn.NeuralNetwork(2, hidden_layers, 1)\n",
    "q.setup_standardization([5, 3.5], [4, np.sqrt(5.25)], [-.1], [0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9cf658-ec13-4810-9443-757b71663bbb",
   "metadata": {},
   "source": [
    "Reminder that gamma is the discount factor, and learning rate controls how quickly the weights are adjusted, much like I used it for the temporal difference equation.\n",
    "\n",
    "In general, the number of epochs corresponds to the amount of weight updates occur per batch of samples. Often, large numbers result in poor generalizability, which, as mentioned, is not a priority due to the size of the Q-input pool.\n",
    "\n",
    "Similarly to before, I'll set up epsilon to decay exponentially over a 10,000 step training loop..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "072ef9b7-86ec-4cbf-a315-dd6b4019fce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 10000\n",
    "epsilon = 1\n",
    "final_epsilon = 0.05\n",
    "epsilon_decay =  np.exp(np.log(final_epsilon) / (n_steps))\n",
    "epsilon_trace = np.zeros(n_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f54fd3-4899-4d83-bd6b-2b50a66a6b26",
   "metadata": {},
   "source": [
    "And create a few classes and methods to plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "720a04aa-b53f-42d7-adf8-7c1a0958ff04",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Scoreboard():\n",
    "    ''' tracks game statistics '''\n",
    "    def __init__(self):\n",
    "        self.all_goals = 0\n",
    "        self._deaths = 0\n",
    "        self._goals = 0\n",
    "        self._max_goals = 0\n",
    "\n",
    "        self.goals = []\n",
    "        self.deaths = []\n",
    "        self.max_goals = []\n",
    "\n",
    "    def track_outcome(self, outcome):\n",
    "        if outcome == multiplayer.CollisionType.GOAL:\n",
    "            self._goals += 1\n",
    "            self.all_goals += 1\n",
    "            if self._goals > self._max_goals:\n",
    "                self._max_goals = self._goals\n",
    "        elif outcome == multiplayer.CollisionType.DEATH:\n",
    "            self._deaths += 1\n",
    "            self._goals = 0\n",
    "\n",
    "    def flush(self):\n",
    "        self.goals.append(self._goals)\n",
    "        self.deaths.append(self._deaths)\n",
    "        self.max_goals.append(self._max_goals)\n",
    "\n",
    "        self._reset()\n",
    "\n",
    "    def _reset(self):\n",
    "        self._deaths = 0\n",
    "        self._goals = 0\n",
    "        self._max_goals = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c86cea77-c3b9-44fa-becd-2d04d49b92cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_status(q, step, epsilon_trace, r_trace=None):\n",
    "    \n",
    "    plt.subplot(4, 3, 1)\n",
    "    plt.plot(epsilon_trace[:step + 1])\n",
    "    plt.ylabel('Random Action Probability ($\\epsilon$)')\n",
    "    plt.ylim(0, 1)\n",
    "\n",
    "    plt.subplot(4, 3, 2)\n",
    "    plt.plot(scoreboard.deaths)\n",
    "    plt.ylabel('Deaths')\n",
    "\n",
    "    plt.subplot(4, 3, 3)\n",
    "    plt.plot(scoreboard.goals)\n",
    "    plt.ylabel('Goals')\n",
    "\n",
    "    plt.subplot(4, 3, 4)\n",
    "    plt.plot(scoreboard.max_goals)\n",
    "    plt.ylabel('Max Score')\n",
    "\n",
    "    '''\n",
    "    plt.subplot(4, 3, 5)\n",
    "    plt.plot(r_trace[:step + 1], alpha=0.5)\n",
    "    binSize = 20\n",
    "    if step+1 > binSize:\n",
    "        # Calculate mean of every bin of binSize reinforcement values\n",
    "        smoothed = np.mean(r_trace[:int(step / binSize) * binSize].reshape((int(step / binSize), binSize)), axis=1)\n",
    "        plt.plot(np.arange(1, 1 + int(step / binSize)) * binSize, smoothed)\n",
    "    plt.ylabel('Mean reinforcement')\n",
    "    '''\n",
    "\n",
    "    plt.subplot(4, 3, 6)\n",
    "    q.draw(['$o$', '$a$'], ['q'])\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ad6521-1907-4fb3-8a33-a0e470e0a361",
   "metadata": {},
   "source": [
    "The logic behind this the training loop is the same as the q-table implementation with added calls to the scoreboard and plotting functions, because I took the time to make each function interface the same. If exported this code to a file, I may utilize higher-order functions to allow easy selection of either Q-function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00ca3585-8a11-4fd5-93d7-8e73bfc31e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "old_X, old_q = pick_greedy_action(q, p1, epsilon)\n",
    "game_engine.player_advance([old_X[1]])\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "scoreboard = Scoreboard()\n",
    "plot_spacing = 1000\n",
    "plotted_steps = 0\n",
    "\n",
    "# R = np.zeros((plot_spacing, 1))\n",
    "# r_trace = np.zeros(n_steps // plot_spacing)\n",
    "\n",
    "for step in range(n_steps):\n",
    "    new_X, new_q = pick_greedy_action(q, p1, epsilon)\n",
    "    outcomes = game_engine.player_advance([new_X[1]])\n",
    "    scoreboard.track_outcome(outcomes[p1])\n",
    "\n",
    "    update_q(q, old_X, new_X, new_q, outcomes[p1], n_epochs, lr=learning_rate)\n",
    "\n",
    "    epsilon *= epsilon_decay\n",
    "    epsilon_trace[step] = epsilon\n",
    "    # R[step % plot_spacing, 0] = reinforcement(outcomes[p1])\n",
    "    old_X = new_X\n",
    "    old_q = new_q\n",
    "\n",
    "    if step >= plotted_steps:\n",
    "        # r_trace[plotted_steps // plot_spacing] = np.mean(R)\n",
    "        plotted_steps += plot_spacing\n",
    "        scoreboard.flush()\n",
    "        fig.clf()\n",
    "        plot_status(q, step, epsilon_trace)\n",
    "        scoreboard.all_goals = 0\n",
    "        clear_output(wait=True)\n",
    "        display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4caa7801-017f-429c-ba14-7331fab1a68b",
   "metadata": {},
   "source": [
    "Like the q-table, the training process occasionally produces a bad agent. This is always due to the agent's exploration finding relatively few goals. A slower decay of epsilon would likely improve this, though this is generally not an issue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc3c79e-2162-4f3a-9e73-a1d789cc2bb0",
   "metadata": {},
   "source": [
    "#### Viewing the Results: Multiplayer Snake\n",
    "\n",
    "Now, I'll test the performance of the Q-network first by itself, in a simple get action advance loop. I'll turn on the draw feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "269ac824-1568-49aa-a020-9a57ee59ae49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Draw is now True.\n"
     ]
    }
   ],
   "source": [
    "game_engine.toggle_draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36a2d897-15a8-47a4-953b-a159af0ad881",
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon = 0\n",
    "for step in range(500):\n",
    "    new_X, _ = pick_greedy_action(q, p1, epsilon)\n",
    "    game_engine.player_advance([new_X[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b208136a-90c0-40e9-ac73-e2536e903ae3",
   "metadata": {},
   "source": [
    "If the agent gets stuck, you may want to retrain the agent by running the last few cells.\n",
    "\n",
    "Now for the fun part: Facing the agents off against each other..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b77b2db1-e928-4cd8-ae98-7f8ac9b1326f",
   "metadata": {},
   "outputs": [],
   "source": [
    "inferior_table = qtsnake.load_q('inferior_qt.npy')\n",
    "superior_table = qtsnake.load_q('superior_qt.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1022bbdf-c68d-4e02-89e0-9d71470d9b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon = 0\n",
    "n_steps = 1500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4897a058-a527-4d3c-bd08-7f8eac8a6e58",
   "metadata": {},
   "source": [
    "Let's make the game really large, to allow the snakes their own space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d67ba96c-9b42-47d2-a88f-a94335bd6967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game starting with 3 players.\n"
     ]
    }
   ],
   "source": [
    "game_engine = multiplayer.Playfield(window_width=WINDOW_WIDTH,\n",
    "                                    window_height=WINDOW_HEIGHT,\n",
    "                                    units=10,\n",
    "                                    g_speed=100,\n",
    "                                    s_size=1)\n",
    "t1 = game_engine.add_player()\n",
    "t2 = game_engine.add_player()\n",
    "n1 = game_engine.add_player()\n",
    "game_engine.start_game()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482f45f9-6964-49e5-90a5-3f189239fe8b",
   "metadata": {},
   "source": [
    "And initialize a new q-table object with the table. This object is quite nice, because it is not tied to any one q-table, it simply reads and writes a q-table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5be5beb-e92c-42ad-9076-c28394560122",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_table = qtsnake.QSnake(game_engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "314d0836-5c99-4de3-91c8-e563fed61e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for step in range(n_steps):\n",
    "    # table 1 (YELLOW)\n",
    "    _, t1_action = q_table.pick_greedy_action(inferior_table, t1, epsilon)\n",
    "\n",
    "    # table 2 (RED)\n",
    "    _, t2_action = q_table.pick_greedy_action(superior_table, t2, epsilon)\n",
    "\n",
    "    # network 1 (PURPLE)\n",
    "    n1_state_action, _ = pick_greedy_action(q, n1, epsilon)\n",
    "    game_engine.player_advance([t1_action,\n",
    "                                t2_action,\n",
    "                                n1_state_action[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe78a0a-a65f-4c9c-9e08-2f99e0fb54b2",
   "metadata": {},
   "source": [
    "Often in my games, the q-network finishes second behind the superior, manually set q-table. Sometimes, it is the other way around, dependent on how successful each agent trained, and the luck of goal spawns during the trial. I have also seen it finish first on many occasions. All in all, it seems both the Q-network and Q-table are relatively equally matched in this game representation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d369045-bc0a-4567-99bb-bb04e75f294c",
   "metadata": {},
   "source": [
    "![Q-Network finishes first!](./extras/2023-12-13_17-35.png)\n",
    "\n",
    "Q-Network finishes first!"
   ]
  }
 ],
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