DemoGym.ipynb

@@ -0,0 +1,86 @@
+{
+ "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()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

DemoMUD.ipynb

@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e848ca9-c915-4aa2-a7cc-a5654ed06863",
+   "metadata": {},
+   "source": [
+    "# Demonstration of Training and Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9506e99-a947-4f69-8355-a3ce696793fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from main import train, test\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de500d9e-40d1-4b6b-900f-96c2ec69e464",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pickle.load(open(\"data.pkl\", \"rb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39ca7791-c844-4231-9f3b-e8ae80fe8103",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mud_point = train(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d8c70ab-d055-418c-b67e-ba5109d989f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test(mud_point)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

README.md

@@ -45,3 +45,7 @@ Using the following presumptions, we can establish better values for the "data v
 
 > The angular momentum of the pole is the most important thing to stabilize.
 
+# headless mode
+
+Run `./headless.sh` (requires `sudo`) to install virtual displays so you can view results in a Jupyter notebook.
+

headless.sh

@@ -0,0 +1,3 @@
+#!/bin/sh
+sudo apt update && sudo apt install build-essential xvfb swig
+pip install box2d-py pyvirtualdisplay

main.py

@@ -7,6 +7,20 @@ import pandas as pd
 from scipy.stats import gaussian_kde as gkde
 from scipy.stats import norm
 
+import matplotlib.pyplot as plt
+
+try:
+    from pyvirtualdisplay import Display
+    display = Display(visible=0, size=(1400, 900))
+    display.start()
+except ImportError:
+    pass
+
+is_ipython = 'inline' in plt.get_backend()
+if is_ipython:
+    from IPython import display
+
+plt.ion()
 
 def train(data):
     D = pd.DataFrame(data)
@@ -28,11 +42,19 @@ def test(decision=np.array([-0.09, -0.71, -0.43, -0.74]), seed=1992):
     env = gym.make("CartPole-v1")
     observation, info = env.reset(seed=seed, return_info=True)
     score = 0
+    if is_ipython:
+        img = plt.imshow(env.render(mode='rgb_array'))
     for i in range(10000):
         action = 1 if decision.T @ observation < 0 else 0
         observation, reward, done, info = env.step(action)
         score += reward
-        env.render()
+        if not is_ipython:
+            env.render()
+        else:
+            img.set_data(env.render(mode='rgb_array'))
+            plt.axis('off')
+            display.display(plt.gcf())
+            display.clear_output(wait=True)
         if done:
             if score == 500:
                 print("WIN")