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87 lines
2.0 KiB
87 lines
2.0 KiB
3 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "71383e8d-63f1-462c-bd77-688d8d34a60a",
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"metadata": {},
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"source": [
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"# Demonstration of `gym`: Visualize Interactive Results in Jupyter Notebook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eae51654-4ccf-44ed-aaac-f1d993d7e4a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from pyvirtualdisplay import Display\n",
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"display = Display(visible=0, size=(1400, 900))\n",
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"display.start()\n",
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"\n",
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"is_ipython = 'inline' in plt.get_backend()\n",
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"if is_ipython:\n",
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" from IPython import display\n",
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"\n",
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"plt.ion()\n",
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"\n",
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"# Load the gym environment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "be872e01-e4fd-4940-874e-d46e97fb3519",
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"metadata": {},
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"outputs": [],
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"source": [
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"import gym\n",
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"import random\n",
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"%matplotlib inline\n",
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"\n",
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"env = gym.make('LunarLander-v2')\n",
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"env.seed(23)\n",
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"\n",
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"# Let's watch how an untrained agent moves around\n",
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"\n",
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"state = env.reset()\n",
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"img = plt.imshow(env.render(mode='rgb_array'))\n",
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"for j in range(200):\n",
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"# action = agent.act(state)\n",
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" action = random.choice(range(4))\n",
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" img.set_data(env.render(mode='rgb_array')) \n",
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" plt.axis('off')\n",
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" display.display(plt.gcf())\n",
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" display.clear_output(wait=True)\n",
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" state, reward, done, _ = env.step(action)\n",
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" if done:\n",
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" break\n",
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"\n",
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"env.close()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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