Lessons in the Research-to-Production Pipeline: From Data Science to Software Engineering
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{
"cells": [
{
"cell_type": "markdown",
"id": "71383e8d-63f1-462c-bd77-688d8d34a60a",
"metadata": {},
"source": [
"# Demonstration of `gym`: Visualize Interactive Results in Jupyter Notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eae51654-4ccf-44ed-aaac-f1d993d7e4a1",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from pyvirtualdisplay import Display\n",
"display = Display(visible=0, size=(1400, 900))\n",
"display.start()\n",
"\n",
"is_ipython = 'inline' in plt.get_backend()\n",
"if is_ipython:\n",
" from IPython import display\n",
"\n",
"plt.ion()\n",
"\n",
"# Load the gym environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "be872e01-e4fd-4940-874e-d46e97fb3519",
"metadata": {},
"outputs": [],
"source": [
"import gym\n",
"import random\n",
"%matplotlib inline\n",
"\n",
"env = gym.make('LunarLander-v2')\n",
"env.seed(23)\n",
"\n",
"# Let's watch how an untrained agent moves around\n",
"\n",
"state = env.reset()\n",
"img = plt.imshow(env.render(mode='rgb_array'))\n",
"for j in range(200):\n",
"# action = agent.act(state)\n",
" action = random.choice(range(4))\n",
" img.set_data(env.render(mode='rgb_array')) \n",
" plt.axis('off')\n",
" display.display(plt.gcf())\n",
" display.clear_output(wait=True)\n",
" state, reward, done, _ = env.step(action)\n",
" if done:\n",
" break\n",
"\n",
"env.close()"
]
}
],
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