{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Policy Gradient\n",
    "\n",
    "Lecture 16 | CMU ANLP Spring 2026 | Instructor: Sean Welleck\n",
    "\n",
    "This notebook contains a simple policy gradient/REINFORCE example on the CartPole environment.\n",
    "\n",
    "*Acknowledgements: code originally based on the example from the [pytorch repository](https://github.com/pytorch/examples/blob/main/reinforcement_learning/reinforce.py)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install \"gymnasium[classic-control]\"\n",
    "import gymnasium as gym\n",
    "import numpy as np\n",
    "from collections import deque\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.distributions import Categorical\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1134a2830>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the CartPole environment\n",
    "env = gym.make('CartPole-v1', render_mode=\"rgb_array\")\n",
    "env.reset(seed=42)\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation space: Box([-4.8               -inf -0.41887903        -inf], [4.8               inf 0.41887903        inf], (4,), float32)\n",
      "Action space: Discrete(2)\n",
      "\n",
      "State is a vector of 4 numbers:\n",
      "  [cart position, cart velocity, pole angle, pole angular velocity]\n"
     ]
    }
   ],
   "source": [
    "# State and action spaces\n",
    "print(\"Observation space:\", env.observation_space)\n",
    "print(\"Action space:\", env.action_space)\n",
    "print(\"\\nState is a vector of 4 numbers:\")\n",
    "print(\"  [cart position, cart velocity, pole angle, pole angular velocity]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial state: [-0.04058227  0.04756223  0.02611397  0.02860643]\n",
      "\n",
      "Step 1:\n",
      "  Action: 1 (RIGHT)\n",
      "  Reward: 1.0\n",
      "  Next state: [-0.03963102  0.24230015  0.0266861  -0.25572422]\n",
      "  Done: False\n",
      "\n",
      "Step 2:\n",
      "  Action: 0 (LEFT)\n",
      "  Reward: 1.0\n",
      "  Next state: [-0.03478502  0.04680755  0.02157161  0.04525506]\n",
      "  Done: False\n",
      "\n",
      "Step 3:\n",
      "  Action: 0 (LEFT)\n",
      "  Reward: 1.0\n",
      "  Next state: [-0.03384887 -0.14861698  0.02247672  0.34466523]\n",
      "  Done: False\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See a few transitions with visualization\n",
    "state, _ = env.reset()\n",
    "print(\"Initial state:\", state)\n",
    "\n",
    "# Create a figure to show the transitions\n",
    "fig, axes = plt.subplots(1, 4, figsize=(16, 4))\n",
    "\n",
    "# Show initial frame\n",
    "axes[0].imshow(env.render())\n",
    "axes[0].set_title('Initial state')\n",
    "axes[0].axis('off')\n",
    "\n",
    "for step in range(3):\n",
    "    action = env.action_space.sample()  # Random action (0=left, 1=right)\n",
    "    next_state, reward, terminated, truncated, _ = env.step(action)\n",
    "    \n",
    "    # Show frame after action\n",
    "    axes[step + 1].imshow(env.render())\n",
    "    axes[step + 1].set_title(f'Step {step+1}: {\"LEFT\" if action == 0 else \"RIGHT\"}')\n",
    "    axes[step + 1].axis('off')\n",
    "    \n",
    "    print(f\"\\nStep {step+1}:\")\n",
    "    print(f\"  Action: {action} ({'LEFT' if action == 0 else 'RIGHT'})\")\n",
    "    print(f\"  Reward: {reward}\")\n",
    "    print(f\"  Next state: {next_state}\")\n",
    "    print(f\"  Done: {terminated or truncated}\")\n",
    "    \n",
    "    state = next_state\n",
    "    if terminated or truncated:\n",
    "        break\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that a positive reward is given at each timestep.\n",
    "\n",
    "Hence maximizing reward means keeping the cartpole upright for a longer time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Policy Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Policy(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Policy, self).__init__()\n",
    "        self.affine1 = nn.Linear(4, 128)\n",
    "        self.affine2 = nn.Linear(128, 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.affine1(x)\n",
    "        x = F.relu(x)\n",
    "        action_scores = self.affine2(x)\n",
    "        return F.softmax(action_scores, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State: [-0.03718864 -0.00496141 -0.0129202   0.0426765 ]\n",
      "Action probabilities: [0.45581493 0.54418504]\n",
      "  P(LEFT) = 0.456\n",
      "  P(RIGHT) = 0.544\n"
     ]
    }
   ],
   "source": [
    "# See the policy give action probabilities for a sample state\n",
    "policy = Policy()\n",
    "state, _ = env.reset()\n",
    "state_tensor = torch.from_numpy(state).float().unsqueeze(0)\n",
    "probs = policy(state_tensor)\n",
    "print(\"State:\", state)\n",
    "print(\"Action probabilities:\", probs.detach().numpy()[0])\n",
    "print(f\"  P(LEFT) = {probs[0, 0].item():.3f}\")\n",
    "print(f\"  P(RIGHT) = {probs[0, 1].item():.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 0\tLast reward: 38.00\tAverage reward: 11.40\n",
      "Episode 10\tLast reward: 25.00\tAverage reward: 20.90\n",
      "Episode 20\tLast reward: 88.00\tAverage reward: 28.01\n",
      "Episode 30\tLast reward: 38.00\tAverage reward: 32.49\n",
      "Episode 40\tLast reward: 37.00\tAverage reward: 33.95\n",
      "Episode 50\tLast reward: 50.00\tAverage reward: 39.78\n",
      "Episode 60\tLast reward: 51.00\tAverage reward: 37.84\n",
      "Episode 70\tLast reward: 109.00\tAverage reward: 44.12\n",
      "Episode 80\tLast reward: 40.00\tAverage reward: 56.91\n",
      "Episode 90\tLast reward: 62.00\tAverage reward: 61.30\n",
      "Episode 100\tLast reward: 74.00\tAverage reward: 56.74\n",
      "Episode 110\tLast reward: 85.00\tAverage reward: 58.00\n",
      "Episode 120\tLast reward: 91.00\tAverage reward: 60.80\n",
      "Episode 130\tLast reward: 22.00\tAverage reward: 63.29\n",
      "Episode 140\tLast reward: 62.00\tAverage reward: 66.19\n",
      "Episode 150\tLast reward: 41.00\tAverage reward: 62.61\n",
      "Episode 160\tLast reward: 99.00\tAverage reward: 56.90\n",
      "Episode 170\tLast reward: 140.00\tAverage reward: 77.05\n",
      "Episode 180\tLast reward: 13.00\tAverage reward: 84.40\n",
      "Episode 190\tLast reward: 112.00\tAverage reward: 100.78\n",
      "Episode 200\tLast reward: 96.00\tAverage reward: 93.33\n",
      "Episode 210\tLast reward: 30.00\tAverage reward: 111.62\n",
      "Episode 220\tLast reward: 30.00\tAverage reward: 117.65\n",
      "Episode 230\tLast reward: 135.00\tAverage reward: 115.73\n",
      "Episode 240\tLast reward: 130.00\tAverage reward: 126.93\n",
      "Episode 250\tLast reward: 123.00\tAverage reward: 135.17\n",
      "Episode 260\tLast reward: 28.00\tAverage reward: 113.06\n",
      "Episode 270\tLast reward: 92.00\tAverage reward: 118.97\n",
      "Episode 280\tLast reward: 87.00\tAverage reward: 136.60\n",
      "Episode 290\tLast reward: 60.00\tAverage reward: 155.64\n",
      "Episode 300\tLast reward: 153.00\tAverage reward: 154.30\n",
      "Episode 310\tLast reward: 209.00\tAverage reward: 165.78\n",
      "Episode 320\tLast reward: 113.00\tAverage reward: 187.68\n",
      "Episode 330\tLast reward: 161.00\tAverage reward: 180.81\n",
      "Episode 340\tLast reward: 187.00\tAverage reward: 191.28\n",
      "Episode 350\tLast reward: 167.00\tAverage reward: 181.70\n",
      "Episode 360\tLast reward: 157.00\tAverage reward: 212.88\n",
      "Episode 370\tLast reward: 112.00\tAverage reward: 199.18\n",
      "Episode 380\tLast reward: 121.00\tAverage reward: 200.80\n",
      "Episode 390\tLast reward: 100.00\tAverage reward: 193.65\n",
      "Episode 400\tLast reward: 199.00\tAverage reward: 231.76\n",
      "Episode 410\tLast reward: 129.00\tAverage reward: 269.26\n",
      "Episode 420\tLast reward: 369.00\tAverage reward: 293.28\n",
      "Episode 430\tLast reward: 198.00\tAverage reward: 249.87\n",
      "Episode 440\tLast reward: 206.00\tAverage reward: 211.03\n",
      "Episode 450\tLast reward: 189.00\tAverage reward: 203.38\n",
      "Episode 460\tLast reward: 339.00\tAverage reward: 227.34\n",
      "Episode 470\tLast reward: 215.00\tAverage reward: 238.23\n",
      "Episode 480\tLast reward: 411.00\tAverage reward: 274.05\n",
      "Episode 490\tLast reward: 295.00\tAverage reward: 288.11\n",
      "Episode 500\tLast reward: 357.00\tAverage reward: 337.01\n",
      "Episode 510\tLast reward: 172.00\tAverage reward: 272.27\n",
      "Episode 520\tLast reward: 170.00\tAverage reward: 227.11\n",
      "Episode 530\tLast reward: 182.00\tAverage reward: 201.28\n",
      "Episode 540\tLast reward: 231.00\tAverage reward: 195.65\n",
      "Episode 550\tLast reward: 223.00\tAverage reward: 219.76\n",
      "Episode 560\tLast reward: 384.00\tAverage reward: 284.02\n",
      "Episode 570\tLast reward: 500.00\tAverage reward: 334.55\n",
      "Episode 580\tLast reward: 500.00\tAverage reward: 393.80\n",
      "Episode 590\tLast reward: 281.00\tAverage reward: 401.60\n",
      "Episode 600\tLast reward: 300.00\tAverage reward: 348.52\n",
      "Episode 610\tLast reward: 245.00\tAverage reward: 302.59\n",
      "Episode 620\tLast reward: 285.00\tAverage reward: 288.97\n",
      "Episode 630\tLast reward: 348.00\tAverage reward: 316.04\n",
      "Episode 640\tLast reward: 322.00\tAverage reward: 366.06\n",
      "Episode 650\tLast reward: 500.00\tAverage reward: 395.27\n",
      "Episode 660\tLast reward: 500.00\tAverage reward: 378.62\n",
      "Episode 670\tLast reward: 500.00\tAverage reward: 384.64\n",
      "Episode 680\tLast reward: 228.00\tAverage reward: 399.61\n",
      "Episode 690\tLast reward: 500.00\tAverage reward: 401.93\n",
      "Episode 700\tLast reward: 467.00\tAverage reward: 408.80\n",
      "Episode 710\tLast reward: 252.00\tAverage reward: 369.11\n",
      "Episode 720\tLast reward: 295.00\tAverage reward: 329.86\n",
      "Episode 730\tLast reward: 296.00\tAverage reward: 346.16\n",
      "Episode 740\tLast reward: 500.00\tAverage reward: 392.77\n",
      "Episode 750\tLast reward: 500.00\tAverage reward: 410.89\n",
      "Episode 760\tLast reward: 412.00\tAverage reward: 417.61\n",
      "Episode 770\tLast reward: 260.00\tAverage reward: 375.77\n",
      "Episode 780\tLast reward: 500.00\tAverage reward: 387.47\n",
      "Episode 790\tLast reward: 500.00\tAverage reward: 388.76\n",
      "Episode 800\tLast reward: 373.00\tAverage reward: 393.68\n",
      "Episode 810\tLast reward: 500.00\tAverage reward: 411.42\n",
      "Episode 820\tLast reward: 484.00\tAverage reward: 423.33\n",
      "Episode 830\tLast reward: 500.00\tAverage reward: 428.01\n",
      "Episode 840\tLast reward: 500.00\tAverage reward: 432.04\n",
      "Episode 850\tLast reward: 500.00\tAverage reward: 452.13\n",
      "Episode 860\tLast reward: 185.00\tAverage reward: 388.99\n",
      "Episode 870\tLast reward: 332.00\tAverage reward: 326.53\n",
      "Episode 880\tLast reward: 239.00\tAverage reward: 264.11\n",
      "Episode 890\tLast reward: 134.00\tAverage reward: 229.41\n",
      "Episode 900\tLast reward: 169.00\tAverage reward: 203.34\n",
      "Episode 910\tLast reward: 158.00\tAverage reward: 183.30\n",
      "Episode 920\tLast reward: 157.00\tAverage reward: 170.89\n",
      "Episode 930\tLast reward: 145.00\tAverage reward: 168.52\n",
      "Episode 940\tLast reward: 214.00\tAverage reward: 164.27\n",
      "Episode 950\tLast reward: 235.00\tAverage reward: 183.74\n",
      "Episode 960\tLast reward: 373.00\tAverage reward: 236.89\n",
      "Episode 970\tLast reward: 500.00\tAverage reward: 337.88\n",
      "Episode 980\tLast reward: 360.00\tAverage reward: 384.07\n",
      "Episode 990\tLast reward: 500.00\tAverage reward: 398.13\n",
      "Episode 1000\tLast reward: 427.00\tAverage reward: 428.43\n",
      "Episode 1010\tLast reward: 242.00\tAverage reward: 351.37\n",
      "Episode 1020\tLast reward: 225.00\tAverage reward: 293.91\n",
      "Episode 1030\tLast reward: 202.00\tAverage reward: 261.33\n",
      "Episode 1040\tLast reward: 203.00\tAverage reward: 240.96\n",
      "Episode 1050\tLast reward: 180.00\tAverage reward: 235.60\n",
      "Episode 1060\tLast reward: 356.00\tAverage reward: 250.94\n",
      "Episode 1070\tLast reward: 500.00\tAverage reward: 322.80\n",
      "Episode 1080\tLast reward: 500.00\tAverage reward: 380.72\n",
      "Episode 1090\tLast reward: 500.00\tAverage reward: 416.96\n",
      "Episode 1100\tLast reward: 500.00\tAverage reward: 438.33\n",
      "Episode 1110\tLast reward: 500.00\tAverage reward: 461.28\n",
      "Episode 1120\tLast reward: 500.00\tAverage reward: 470.64\n",
      "Episode 1130\tLast reward: 500.00\tAverage reward: 467.45\n",
      "Episode 1140\tLast reward: 500.00\tAverage reward: 475.14\n",
      "Solved! Running reward is now 475.13811859993376 and the last episode runs to 500 time steps!\n"
     ]
    }
   ],
   "source": [
    "policy = Policy()\n",
    "optimizer = optim.Adam(policy.parameters(), lr=1e-3)\n",
    "\n",
    "running_reward = 10\n",
    "num_episodes = 10000\n",
    "max_steps_per_episode = 10000\n",
    "log_interval = 10\n",
    "running_reward = 10\n",
    "discount_gamma = 0.99\n",
    "EPS = np.finfo(np.float32).eps.item()\n",
    "\n",
    "reward_history = []\n",
    "\n",
    "for i_episode in range(num_episodes):\n",
    "    state, _ = env.reset()\n",
    "    ep_reward = 0\n",
    "\n",
    "    # 1. Collect an episode trajectory\n",
    "    log_probs, rewards = [], []\n",
    "    for t in range(1, max_steps_per_episode):\n",
    "        state = torch.tensor(state).unsqueeze(0)\n",
    "        probs = policy(state)\n",
    "        p = Categorical(probs)\n",
    "        action = p.sample()\n",
    "        log_prob = p.log_prob(action)\n",
    "\n",
    "        state, reward, terminated, truncated, _ = env.step(action.item())\n",
    "\n",
    "        log_probs.append(log_prob)\n",
    "        rewards.append(reward)\n",
    "        ep_reward += reward\n",
    "        if terminated or truncated:\n",
    "            break\n",
    "\n",
    "    # 2. Compute returns\n",
    "    returns = deque()\n",
    "    R = 0\n",
    "    for r in rewards[::-1]:\n",
    "        R = r + discount_gamma * R\n",
    "        returns.appendleft(R)\n",
    "    returns = torch.tensor(returns)\n",
    "\n",
    "    # 3. Compute loss\n",
    "    log_probs = torch.cat(log_probs)\n",
    "    policy_loss = -(log_probs*returns).sum()\n",
    "\n",
    "    # 4. Update policy\n",
    "    optimizer.zero_grad()\n",
    "    policy_loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    running_reward = 0.05 * ep_reward + (1 - 0.05) * running_reward\n",
    "    reward_history.append(running_reward)\n",
    "    \n",
    "    if i_episode % log_interval == 0:\n",
    "        print(f'Episode {i_episode}\\tLast reward: {ep_reward:.2f}\\tAverage reward: {running_reward:.2f}')\n",
    "    if running_reward > env.spec.reward_threshold:\n",
    "        print(f\"Solved! Running reward is now {running_reward} and the last episode runs to {t} time steps!\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training progress\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(reward_history)\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Running Reward')\n",
    "plt.title('Training Progress')\n",
    "plt.axhline(y=env.spec.reward_threshold, color='r', linestyle='--', label='Solved threshold')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Visualizing the Trained Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_animation(num_steps=200, use_greedy=True, show_actions=True):\n",
    "    import base64\n",
    "    from io import BytesIO\n",
    "    from PIL import Image, ImageDraw, ImageFont\n",
    "    import random\n",
    "    \n",
    "    state, _ = env.reset()\n",
    "    frames = []\n",
    "    actions_taken = []\n",
    "    total_reward = 0\n",
    "    \n",
    "    for t in range(num_steps):\n",
    "        with torch.no_grad():\n",
    "            state_tensor = torch.from_numpy(state).float().unsqueeze(0)\n",
    "            probs = policy(state_tensor)\n",
    "            if use_greedy:\n",
    "                action = probs.argmax().item()\n",
    "            else:\n",
    "                m = Categorical(probs)\n",
    "                action = m.sample().item()\n",
    "        frames.append(env.render())\n",
    "        actions_taken.append(action)\n",
    "        state, reward, terminated, truncated, _ = env.step(action)\n",
    "        total_reward += reward\n",
    "        if terminated or truncated:\n",
    "            break\n",
    "    print(f\"Episode: {len(frames)} steps, total reward: {total_reward:.1f}\")\n",
    "    \n",
    "    # Convert frames to base64 encoded images with action overlay\n",
    "    encoded_frames = []\n",
    "    action_names = [\"LEFT\", \"RIGHT\"]\n",
    "    \n",
    "    for frame, action in zip(frames, actions_taken):\n",
    "        img = Image.fromarray(frame)\n",
    "        if show_actions:\n",
    "            draw = ImageDraw.Draw(img)\n",
    "            font = ImageFont.load_default(size=20)\n",
    "            action_text = f\"Action: {action_names[action]}\"\n",
    "            \n",
    "            # Get text bounding box\n",
    "            bbox = draw.textbbox((0, 0), action_text, font=font)\n",
    "            text_width = bbox[2] - bbox[0]\n",
    "            text_height = bbox[3] - bbox[1]\n",
    "            \n",
    "            # Position in top right corner with padding\n",
    "            padding = 10\n",
    "            x = img.width - text_width - padding\n",
    "            y = padding\n",
    "            \n",
    "            # Draw background rectangle\n",
    "            bg_color = (50, 150, 50) if action == 1 else (150, 50, 50) \n",
    "            draw.rectangle(\n",
    "                [x - 5, y - 5, x + text_width + 5, y + text_height + 5],\n",
    "                fill=bg_color\n",
    "            )\n",
    "            draw.text((x, y), action_text, fill=(255, 255, 255), font=font)\n",
    "        \n",
    "        # Convert to base64\n",
    "        buffer = BytesIO()\n",
    "        img.save(buffer, format='PNG')\n",
    "        encoded = base64.b64encode(buffer.getvalue()).decode()\n",
    "        encoded_frames.append(encoded)\n",
    "    \n",
    "    # Generate unique ID for this animation\n",
    "    unique_id = f\"anim_{random.randint(100000, 999999)}\"\n",
    "    \n",
    "    # Create HTML with JavaScript animation\n",
    "    html = f\"\"\"\n",
    "    <div id=\"{unique_id}_container\" style=\"text-align: center;\">\n",
    "        <h3>CartPole Episode (Reward: {total_reward:.1f}, Steps: {len(frames)})</h3>\n",
    "        <img id=\"{unique_id}_img\" style=\"width: 500px; border: 2px solid #333; border-radius: 5px;\"/>\n",
    "        <br><br>\n",
    "        <button id=\"{unique_id}_play_btn\" style=\"padding: 10px 20px; font-size: 14px; margin: 5px;\">▶️ Play/Pause</button>\n",
    "        <button id=\"{unique_id}_reset_btn\" style=\"padding: 10px 20px; font-size: 14px; margin: 5px;\">↺ Reset</button>\n",
    "        <span id=\"{unique_id}_info\" style=\"margin-left: 20px; font-size: 16px;\">Frame: 0/{len(frames)}</span>\n",
    "        <br><br>\n",
    "        <label for=\"{unique_id}_speed\">Speed: </label>\n",
    "        <input type=\"range\" id=\"{unique_id}_speed\" min=\"20\" max=\"200\" value=\"50\" style=\"width: 300px;\">\n",
    "        <span id=\"{unique_id}_speed_val\">50ms</span>\n",
    "    </div>\n",
    "    \n",
    "    <script>\n",
    "    (function() {{\n",
    "        var frames = {encoded_frames};\n",
    "        var currentFrame = 0;\n",
    "        var playing = false;\n",
    "        var interval = null;\n",
    "        \n",
    "        var img = document.getElementById('{unique_id}_img');\n",
    "        var playBtn = document.getElementById('{unique_id}_play_btn');\n",
    "        var resetBtn = document.getElementById('{unique_id}_reset_btn');\n",
    "        var info = document.getElementById('{unique_id}_info');\n",
    "        var speedSlider = document.getElementById('{unique_id}_speed');\n",
    "        var speedVal = document.getElementById('{unique_id}_speed_val');\n",
    "        \n",
    "        function showFrame(n) {{\n",
    "            currentFrame = n % frames.length;\n",
    "            img.src = 'data:image/png;base64,' + frames[currentFrame];\n",
    "            info.innerText = 'Frame: ' + currentFrame + '/{len(frames)}';\n",
    "        }}\n",
    "        \n",
    "        function togglePlay() {{\n",
    "            playing = !playing;\n",
    "            if (playing) {{\n",
    "                var speed = speedSlider.value;\n",
    "                interval = setInterval(function() {{\n",
    "                    currentFrame = (currentFrame + 1) % frames.length;\n",
    "                    showFrame(currentFrame);\n",
    "                }}, speed);\n",
    "            }} else {{\n",
    "                clearInterval(interval);\n",
    "            }}\n",
    "        }}\n",
    "        \n",
    "        function reset() {{\n",
    "            playing = false;\n",
    "            clearInterval(interval);\n",
    "            currentFrame = 0;\n",
    "            showFrame(0);\n",
    "        }}\n",
    "        \n",
    "        // Event listeners\n",
    "        playBtn.addEventListener('click', togglePlay);\n",
    "        resetBtn.addEventListener('click', reset);\n",
    "        \n",
    "        speedSlider.addEventListener('input', function() {{\n",
    "            speedVal.innerText = this.value + 'ms';\n",
    "            if (playing) {{\n",
    "                clearInterval(interval);\n",
    "                var speed = this.value;\n",
    "                interval = setInterval(function() {{\n",
    "                    currentFrame = (currentFrame + 1) % frames.length;\n",
    "                    showFrame(currentFrame);\n",
    "                }}, speed);\n",
    "            }}\n",
    "        }});\n",
    "        \n",
    "        // Show first frame\n",
    "        showFrame(0);\n",
    "        \n",
    "        // Auto-play on load\n",
    "        setTimeout(togglePlay, 500);\n",
    "    }})();\n",
    "    </script>\n",
    "    \"\"\"\n",
    "    \n",
    "    return HTML(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode: 200 steps, total reward: 200.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div id=\"anim_248296_container\" style=\"text-align: center;\">\n",
       "        <h3>CartPole Episode (Reward: 200.0, Steps: 200)</h3>\n",
       "        <img id=\"anim_248296_img\" style=\"width: 500px; border: 2px solid #333; border-radius: 5px;\"/>\n",
       "        <br><br>\n",
       "        <button id=\"anim_248296_play_btn\" style=\"padding: 10px 20px; font-size: 14px; margin: 5px;\">▶️ Play/Pause</button>\n",
       "        <button id=\"anim_248296_reset_btn\" style=\"padding: 10px 20px; font-size: 14px; margin: 5px;\">↺ Reset</button>\n",
       "        <span id=\"anim_248296_info\" style=\"margin-left: 20px; font-size: 16px;\">Frame: 0/200</span>\n",
       "        <br><br>\n",
       "        <label for=\"anim_248296_speed\">Speed: </label>\n",
       "        <input type=\"range\" id=\"anim_248296_speed\" min=\"20\" max=\"200\" value=\"50\" style=\"width: 300px;\">\n",
       "        <span id=\"anim_248296_speed_val\">50ms</span>\n",
       "    </div>\n",
       "    \n",
       "    <script>\n",
       "    (function() {\n",
       "        var frames = 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CEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhAAUkf0/AUKkrw9mi6kAAAAASUVORK5CYII=', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQBLZ/wLkdZWkeUuX+QAAAABJRU5ErkJggg==', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQBLZ/wLkdZWkeUuX+QAAAABJRU5ErkJggg==', 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CE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAssj+Dwc3pK9Be7b5AAAAAElFTkSuQmCC', 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', 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AaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEAR2f8BVmnSr7WehOAAAAAASUVORK5CYII=', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQBHZ/wGFB9Kv0KJRVQAAAABJRU5ErkJggg==', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQBHZ/wGFB9Kv0KJRVQAAAABJRU5ErkJggg==', 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', 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QhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCAIrL/A1BkpK+wRXjSAAAAAElFTkSuQmCC', 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QmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQApBE9j8NdZWkXtU7eAAAAABJRU5ErkJggg==', 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aEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACUET2v+7+pK9bvrePAAAAAElFTkSuQmCC', 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CCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAEdn/Axzg0q/PRZVaAAAAAElFTkSuQmCC', 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', 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aEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACUET2v+7+pK9bvrePAAAAAElFTkSuQmCC', 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CE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAJLI/geh5pWkcDKNvAAAAABJRU5ErkJggg==', 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', 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', 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AQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgCSyP4HNmaVpEAWDmcAAAAASUVORK5CYII=', 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mhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhAAUkf0vmZGkryqINIoAAAAASUVORK5CYII=', 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AQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgCSyP4HNmaVpEAWDmcAAAAASUVORK5CYII=', 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CEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAJQRPZ/ODqkr/tMLIsAAAAASUVORK5CYII=', 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CEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAJQRPZ/ODqkr/tMLIsAAAAASUVORK5CYII=', 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IQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAEtn/AMrXlaQb8FJEAAAAAElFTkSuQmCC', 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', 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CAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgCyyP4HPi+krx+dFxgAAAAASUVORK5CYII=', 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TQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAssj+D9zJpK8wOlDJAAAAAElFTkSuQmCC', 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', 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EIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgiez/AF9XlaT24KezAAAAAElFTkSuQmCC', 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DBXHtQRJMjUJRhBDSIIRQECGEJBgRQlGEEBIhhFAMIYQkVFgsAwVx7UEaTI1CQYQQ0iCEUBAhhCSYGoWiuPYgDUaEUBAhhCS4fQKKIoSQCCGEYgghJMFnhFAU1x6kwdQoFEQIIQ1CCAURQkiCxTJQFCGEpqr4BP7um3fr1v0f9+bAIQghJKG+vugzgKiEEJJQp4RQkGZF/WKI6eXt527cc8zuurYtK3d0arHm1HYLS9uFEIoihJCf2e9Nani8u67t2l191+7qO7Lj1L9OjQohFMPUKBRQwcbb6+qEEIohhFBkBRv2GhBCUYQQkuAzQiiKEEISfEYIRRFCSIIRIRRFCCEJFstAUYQQ8lC6R+IQew0IoShCCAW3sLTd1CgUxQ31kJ+RHad+2DfLAEWpsFYNmijZ//PgKgYA4GMyIoSmMiKEf0oWywAQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJp/wwRAaEaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAZJH9H1gERbMpBNsnAAAAAElFTkSuQmCC', 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QgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIAssv8B4r6kr8+FDaUAAAAASUVORK5CYII=', 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AQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgCSyP4H88iVpHefPHgAAAAASUVORK5CYII=', 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NCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBKCL7fyYFpK8cq8s2AAAAAElFTkSuQmCC', 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zWAYyzPUJJWdqFLJMCKH0hBAyTAih5IwIIcuEEMpACCG7hBBKLmexDGSY6xNKz9QoZJgQQukJIWSYEELJmRqFLHN9QukZEUKGCSGUnNsnIMuEEMpACCG7hBBKzmeEkGWuTyg9U6OQYUIIpSeEkGFCCCVnsQxkmRBCS+U+qZ49e5XuxVUW/kFCCCWXz6d9BsDhCSGUXIMSQoa1SvsEIFYIX9l1/pZ9x+1t6NC2YnfXNutP77gk1VMDhBBKL/9/IZz3/tTGjXsbOmzYM2DDngFjusxI79QAU6NQeg0N+SYVLHa47UB5CCGUXD7/d2qnhZAiIYSSs1gGskwIoXyfEQIZJIRQckaEkGVCCGVaLANkkxBCyeXzyUffI+EOCkiREEKZpkYPVzsVhHS5oR7KZ0yXGb5ZBrImZz0btFBm/8+DqxgAgE/IiBBayogQPpMslgEgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDT/hgmA0IwIAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBSCL7H5s8RbNJYs6DAAAAAElFTkSuQmCC', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQBLZ/wCISJWkYlRy2AAAAABJRU5ErkJggg==', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQBLZ/wCISJWkYlRy2AAAAABJRU5ErkJggg==', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQBLZ/wCISJWkYlRy2AAAAABJRU5ErkJggg==', 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', 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hBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIInsfwEcyJWklhiO7AAAAABJRU5ErkJggg==', 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hBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIInsfwEcyJWklhiO7AAAAABJRU5ErkJggg==', 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hAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAFlk/w9vMqSv6sOR3QAAAABJRU5ErkJggg==', 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aEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQApBE9r+xOZWk+wOSgAAAAABJRU5ErkJggg==', 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', 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BCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAUgi+391J6SvMYaLoAAAAABJRU5ErkJggg==', 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ITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAFIIvs/RbmVpGbvsKQAAAAASUVORK5CYII=', 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ITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAFIIvs/RbmVpGbvsKQAAAAASUVORK5CYII=', 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', 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IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBSCL7fxnFpK+IYTBaAAAAAElFTkSuQmCC', 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hAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAFlk/w+4X6Svz3or8gAAAABJRU5ErkJggg==', 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', 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QAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQApBE9r/aKpWkt/9ZmgAAAABJRU5ErkJggg==', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAEtn/Ab5UpK/gqctMAAAAAElFTkSuQmCC', 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QBiGEzBBCSIERIWSHEEIqhBCyQgghDRbLQGa4GiEFpkYhO4QQ0iCEkBlCCCkwIoTsEEJIhRBCVgghpKDCYhnIDFcjpMHUKGSGEEIahBAyQwghBaZGITtcjZAGI0LIDCGEFLh9ArJDCCEVQghZIYSQAp8RQna4GiENpkYhM4QQ0iCEkBlCCCmwWAayQwjhwCr+AZ/45r169S7fmwOfihBCChoa0j4D4P8IIaSgXgkhM1qlfQLwWfbK9nM27j5qV33HtpU7urV55+ROiwrbhRCyQwihXOZ+MLXx8a76jmt3Dly7c+CYrvf/bWpUCCErTI1C2SvYdHt9vRBCVgghtFwFG/caEEJ2CCGkwGeEkB1CCCnwGSFkhxBCCowIITuEEFJgsQxkhxBC6RXukTjIXgNCyA4hhBZtYWG7qVHIDjfUQ7mM6Xr/x32zDJAdFVavwQFl9v88uGYBACgZI0I4MCNCCMJiGQBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQvNvmAAIzYgQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCAJLL/BTOaRbPHVC0DAAAAAElFTkSuQmCC', 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', 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oQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAEtn/Ab5UpK/gqctMAAAAAElFTkSuQmCC', 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CAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAJLI/g8dyNKvg3qadAAAAABJRU5ErkJggg==', 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mhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhABkkf0/AZukr3/m9BAAAAAASUVORK5CYII=', 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QmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAKQRPY/AyqVpKwATLEAAAAASUVORK5CYII=', 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', 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QgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCAJLL/BweQpK/Z8tIGAAAAAElFTkSuQmCC', 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', 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QhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIAksv8Bl5uVpIdJliYAAAAASUVORK5CYII=', 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QhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIAksv8Bl5uVpIdJliYAAAAASUVORK5CYII=', 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A0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCAJLL/ASwblaQI0k8EAAAAAElFTkSuQmCC', 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IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAUgi+3/vV6Sv4wV1YQAAAABJRU5ErkJggg==', 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', 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IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAUgi+3/vV6Sv4wV1YQAAAABJRU5ErkJggg==', 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ITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAFIIvs/wIyVpEKzyYkAAAAASUVORK5CYII=', 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', 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EIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgiez/AfVMpK/PoJ5qAAAAAElFTkSuQmCC', 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gNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgiex/AFUMlaTCYClrAAAAAElFTkSuQmCC', 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AaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAEtn/ATiTpK/1boouAAAAAElFTkSuQmCC', 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', 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AaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAaEIIQGhCCEBoQghAEtn/ATiTpK/1boouAAAAAElFTkSuQmCC', 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QhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIAksv8F6X2VpMQIO+MAAAAASUVORK5CYII=', 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', 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0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIIns/wDdIqSvAWT8kgAAAABJRU5ErkJggg==', 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', 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IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAUgi+3/jF6SvecmSswAAAABJRU5ErkJggg==', 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NCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAUgi+18SfZWk4n7hOQAAAABJRU5ErkJggg==', 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NCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAUgi+18SfZWk4n7hOQAAAABJRU5ErkJggg==', 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NCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAUgi+18SfZWk4n7hOQAAAABJRU5ErkJggg==', 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', 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TQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgCSyP4fJl6kr2SyLqUAAAAASUVORK5CYII=', 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EAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAJJH9L6bulaT/cNZDAAAAAElFTkSuQmCC', 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', 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gNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCA0IQQgNCEEIDQhBCCJ7P8BLFOkr6MNP1sAAAAASUVORK5CYII=', 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QAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQAhCaEAIQmhACEJoQApBE9v/K7aSvtd9zAgAAAABJRU5ErkJggg==', 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', 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CAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAksj+FztulaQkTIGzAAAAAElFTkSuQmCC', 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CAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAQhNCAEITQgBCE0IAksj+FztulaQkTIGzAAAAAElFTkSuQmCC', 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', 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JIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAKEJIQChCSEAoQkhAElk/wtkX5WkR2+edQAAAABJRU5ErkJggg==', 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'];\n",
       "        var currentFrame = 0;\n",
       "        var playing = false;\n",
       "        var interval = null;\n",
       "        \n",
       "        var img = document.getElementById('anim_248296_img');\n",
       "        var playBtn = document.getElementById('anim_248296_play_btn');\n",
       "        var resetBtn = document.getElementById('anim_248296_reset_btn');\n",
       "        var info = document.getElementById('anim_248296_info');\n",
       "        var speedSlider = document.getElementById('anim_248296_speed');\n",
       "        var speedVal = document.getElementById('anim_248296_speed_val');\n",
       "        \n",
       "        function showFrame(n) {\n",
       "            currentFrame = n % frames.length;\n",
       "            img.src = 'data:image/png;base64,' + frames[currentFrame];\n",
       "            info.innerText = 'Frame: ' + currentFrame + '/200';\n",
       "        }\n",
       "        \n",
       "        function togglePlay() {\n",
       "            playing = !playing;\n",
       "            if (playing) {\n",
       "                var speed = speedSlider.value;\n",
       "                interval = setInterval(function() {\n",
       "                    currentFrame = (currentFrame + 1) % frames.length;\n",
       "                    showFrame(currentFrame);\n",
       "                }, speed);\n",
       "            } else {\n",
       "                clearInterval(interval);\n",
       "            }\n",
       "        }\n",
       "        \n",
       "        function reset() {\n",
       "            playing = false;\n",
       "            clearInterval(interval);\n",
       "            currentFrame = 0;\n",
       "            showFrame(0);\n",
       "        }\n",
       "        \n",
       "        // Event listeners\n",
       "        playBtn.addEventListener('click', togglePlay);\n",
       "        resetBtn.addEventListener('click', reset);\n",
       "        \n",
       "        speedSlider.addEventListener('input', function() {\n",
       "            speedVal.innerText = this.value + 'ms';\n",
       "            if (playing) {\n",
       "                clearInterval(interval);\n",
       "                var speed = this.value;\n",
       "                interval = setInterval(function() {\n",
       "                    currentFrame = (currentFrame + 1) % frames.length;\n",
       "                    showFrame(currentFrame);\n",
       "                }, speed);\n",
       "            }\n",
       "        });\n",
       "        \n",
       "        // Show first frame\n",
       "        showFrame(0);\n",
       "        \n",
       "        // Auto-play on load\n",
       "        setTimeout(togglePlay, 500);\n",
       "    })();\n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Animate the trained agent playing an episode\n",
    "create_animation(num_steps=200, use_greedy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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