Update 'README.md'

needed to install some virtual display software.
`gcc` may be required to add to `./headless.sh`

Co-authored-by: Michael Pilosov <consistentbayes@gmail.com>
Reviewed-on: #1
Co-authored-by: mm <mm@clfx.cc>
Co-committed-by: mm <mm@clfx.cc>

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

@@ -2,6 +2,7 @@
 
 control systems with MUD points
 
+
 # installation
 
 ```bash
@@ -17,6 +18,9 @@ A `data.pkl` file is provided for your convenience with input / output samples.
 python main.py
 ```
 
+You can also instead use the included [jupyter notebook](./DemoMUD.ipynb).
+
+
 # info
 
 The inputs are the parameters to a `1x4` matrix which is multiplied against the observations of the state in order to make a decision for the next action (push left or right). The output of the vector inner-product is binarized by comparing it to zero as a threshold value.
@@ -29,6 +33,7 @@ There is no assumed error in observations; the "data variance" is designed to re
 
 Therefore, since our objective is to stabilize the cart, the target "time series signal" is zero for all four dimensions of the observation space. The presumed "data variance" should actually correspond to the acceptable bands of signal (WIP).
 
+
 # generate data
 
 You can generate your own data with:
@@ -45,3 +50,8 @@ 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 / notebook demos
+
+Run `./headless.sh` (requires `sudo`) to install virtual displays so you can use the included Jupyter notebooks.
+

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")