learning_cartpole.ipynb

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  "metadata": {
    "colab": {
      "name": "learning_cartpole.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ruperty/colab/blob/master/learning_cartpole.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSMgY0NY06F4"
      },
      "source": [
        "# Cartpole Reorganisation\n",
        "\n",
        "Two previous demos ([Basic Cartpole](https://colab.research.google.com/github/ruperty/colab/blob/master/basic_cartpole.ipynb) , [Moving Cartpole](https://colab.research.google.com/github/ruperty/colab/blob/master/moving_cartpole.ipynb)) showed how the Cartpole scenario could be controlled and moved around with a simple hierarchy of about 4 or 5 levels. Each level has a single weight (gain) value which governs the performance of the control unit.\n",
        "\n",
        "Previously those weights were chosen by hand, so may not necessarily be the best. This current demo shows how those weights can be automatically optimised by a process of reorganisation (learning). In this case, reorganisation means changing the weight values based upon the global error within the system to improve performance. The global error encapsulates all the errors within each control system in the hierarchy. The lower this error then the more stable is the control of the Cartpole. \n",
        "\n",
        "As explained in the Advanced section, in this demo, this is achieved by implementing a Perceptual Control Theory hierarchy within Tensorflow, the Deep Learning framework.\n",
        "\n",
        "However, before going in to details let's first see it running. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZD-BQCTt5bhG"
      },
      "source": [
        "## Imports\n",
        "If you were to run this demo in your own desktop Python environment you would be able to see the Cartpole run live. However, due to the limitations of the online system it is necessary to create a video of the Cartpole environment and run that after the event. \n",
        "\n",
        "So, to support that we need these first two cells, which would not be required in your local environment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fV5L29-q174H"
      },
      "source": [
        "# install of utilities to display openai environment in video\n",
        "!pip install gym pyvirtualdisplay > /dev/null 2>&1\n",
        "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n",
        "!apt-get install x11-utils  > /dev/null 2>&1"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "herCgfq35YpV",
        "outputId": "1adf5089-54fc-4241-a55a-01b245f8013f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from gym.wrappers import Monitor\n",
        "from pyvirtualdisplay import Display\n",
        "display = Display(visible=0, size=(1400, 900))\n",
        "display.start() "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyvirtualdisplay.display.Display at 0x7f358f58d588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQx_8Ook6atl"
      },
      "source": [
        "You would need these next two, to import some standard libraries and my own functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJA-rbm7YSjL",
        "outputId": "6465cf21-e301-4015-ed81-53fb60d0a908",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# install of my own pct library\n",
        "!git clone https://github.com/ruperty/pctx.git"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pctx'...\n",
            "remote: Enumerating objects: 69, done.\u001b[K\n",
            "remote: Counting objects: 100% (69/69), done.\u001b[K\n",
            "remote: Compressing objects: 100% (43/43), done.\u001b[K\n",
            "remote: Total 69 (delta 19), reused 68 (delta 18), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (69/69), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdXkGW5jbPqF"
      },
      "source": [
        "#import plotly.graph_objects as go\n",
        "import pctx.openai.cpplots as cp\n",
        "from pctx.dl.models.cartpole import CartpoleTuning"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3z1WtEmG7SDU"
      },
      "source": [
        "A few settings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOH-pBqIQTIA"
      },
      "source": [
        "training=True\n",
        "batch_size=10\n",
        "num_runs=2000\n",
        "learning_rate=0.0020\n",
        "record_video=True\n",
        "# initial weights/gains\n",
        "weights=[4,       2.0,       2.0,       -0.05]\n",
        "# this trainable variable governs which of the weights we are going to train\n",
        "# for the purposes of this demo we'll just do the top two\n",
        "trainable=[True, True, False, False]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgMiMy-Z7frQ"
      },
      "source": [
        "Create the Cartpole hierarchy. See Advanced section for visualisation of the (Tensorflow) model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YLP7wm121DK",
        "outputId": "4d4bfa66-6d92-4bbf-8980-0fa02b521720",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ct = CartpoleTuning( env_name=\"CartPole-v1\", trainable=trainable, video_wrap=record_video)\n",
        "env = ct.get_env()\n",
        "obs = env.reset()\n",
        "print(\"Initial inputs pole angle, pole velocity, cart position, cart velocity\", obs)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initial inputs pole angle, pole velocity, cart position, cart velocity [-1.35158671e-02 -3.90122535e-02  4.49873197e-02  1.11396564e-05]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2se-uN_75wz"
      },
      "source": [
        "Configure the learning aspects of the model. This uses an optimisation algorithm called RMSProp and a \"loss\" function, which in this case is the root of the sum of the squares of the errors (RSuSE).  That is, squaring each of the four errors adding them up and then taking the square root. The loss value actually used for optimisation is the moving average of the loss. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhnvqxfD_fye"
      },
      "source": [
        "ct.configure(weights=weights, opt_type=\"rmsprop\", learning_rate=learning_rate, loss_type=\"rsuse\",  loss_smooth=0.9, print=100, num_runs=num_runs, training=training)\n",
        "ct.display_configure(render=record_video)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RiXxH7o9uOe"
      },
      "source": [
        "## Training\n",
        "That's all the set up, now run the system and see the training reorganise the weights of the Cartpole model to optimise the control."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqZK17B83El8",
        "outputId": "a8d5ca2b-6bd8-4849-e545-b452e3eb5495",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "out = ct.run( batch_size, verbose=False) "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                   Weights \n",
            "Step  Loss    pole_angle pole_velocity cart_position cart_velocity  \n",
            " 100  0.577      4.005        1.879        2.000        -0.050       \n",
            " 200  0.843      4.027        1.719        2.000        -0.050       \n",
            " 300  0.839      4.091        1.551        2.000        -0.050       \n",
            " 400  0.578      4.147        1.398        2.000        -0.050       \n",
            " 500  0.567      4.198        1.236        2.000        -0.050       \n",
            " 600  0.429      4.251        1.079        2.000        -0.050       \n",
            " 700  0.425      4.292        0.916        2.000        -0.050       \n",
            " 800  0.337      4.330        0.759        2.000        -0.050       \n",
            " 900  0.350      4.346        0.595        2.000        -0.050       \n",
            "1000  0.494      4.361        0.432        2.000        -0.050       \n",
            "Cartpole failed with a poleangle of -12.58 degrees and global error of 1.53.\n",
            "An optimised control system for this case would have residual error of around 0.30 or less\n",
            "Minimum loss 0.23 and weights 4.368      0.533      2.000      -0.050    \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPG6eY8caMFz"
      },
      "source": [
        "## Video\n",
        "Watch the video."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_bc2pyQxhXM",
        "outputId": "d3c34649-f0c0-44a4-f129-8426b1388bf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "if record_video: \n",
        "  ct.show_video()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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type=\"video/mp4\" />\n",
              "             </video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KD0TMHsA-cp"
      },
      "source": [
        "Now plot the global error over the period of the Cartpole run."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgklcWXY5TyI",
        "outputId": "d76ef083-5bc2-4cb5-ca72-80bcc7f97c96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "import numpy as np\n",
        "data = ct.get_error_data()\n",
        "data = [data[0], np.clip(data[1], 0, 1.2)]\n",
        "cp.scatter_plot(data , \"Global Error\", \"Time\", \"Error\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
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              "var gd = document.getElementById('d0d6df04-7193-46e4-a7b3-97fe1f90a54c');\n",
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlMJFrNvBSxC"
      },
      "source": [
        "As you can see the error has reduced somewhat, to improve control. Also, as you probably noticed, the system fails towards the end of the run as the error goes off the scale. \n",
        "\n",
        "The reasons for both of these are explained in the next section when you look at the 3D visualisation of the landscape of the error based on the weight values.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsJzf9sK31CE"
      },
      "source": [
        "# Advanced\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-h3h8HILz3kQ"
      },
      "source": [
        "## Tensorflow\n",
        "\n",
        "Tensroflow (https://www.tensorflow.org/) is Google's very sophisticated Deep Learning platform for imlplementing Neural Networks.\n",
        "\n",
        "It is based on the principle of Graph Computation. This means that the processing is separated into separate nodes which are linked together. The node structure allows for highly-distributed systems, across threads/processors or even remote computers as different nodes can be processed at the same time. This parallelism results in highly-efficient processing which is crucial for the enormous computational demands of big data neural networks.\n",
        "\n",
        "The graph structure is designed so as to fit with the structure of neural networks, that deal with layers of neuron-like processing units. That graph structure is also very well suited to Perceptual Control hierarchies, which, similarly, consists of layers of neuron-like (control) units.\n",
        "\n",
        "However, there is one major difference which is that the whole philosophy of neural networks (and Artificial Intelligence generally) is of an open-loop processing pipeline from inputs, through the layers, to the outputs. \n",
        "\n",
        "Within neural networks there is no concept of continuous feedback dynamically affecting the inputs. \n",
        "\n",
        "The architecture of Tensorflow has been designed and implemented with that philosophy in mind and, therefore, does not readily support dynamic, immediate feedback.\n",
        "\n",
        "Fortunately though, I have managed to hack the standard procedures to construct custom processing and training loops so that the outputs at each step are able to feed back through the scenario environment to dynamically change the inputs that are \"perceived\".\n",
        "\n",
        "The passive, one-way processing of the traditional neural networks neglects a fundamental aspect of living systems, that of dynamic interaction with the world. It is this continuous interaction which enables systems to be highly adaptive and take control of their environment rather than being at the mercy of it. \n",
        "\n",
        "This two-way interaction is at the heart of Perceptual Control Theory and holds open the prospect of being able to develop sophisticated, robust, adaptive artificial systems. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Icjj8cSb6QFh"
      },
      "source": [
        "## The Model\n",
        "\n",
        "Tensrflow includes some very useful visualisation functionality. The next cell, shows one, which is the visualisation of the neural network graph, of layers.\n",
        "\n",
        "This is the above implementation of the PCT Cartpole system. \n",
        "\n",
        "Each node is a layer of \"neurons\" and the links are the connections between the neurons in one layer and another. Typically, each node in a neural network might involve dozens or hundreds of of neurons and connections.\n",
        "\n",
        "In this case, though, each node is just a single neuron and the links are a single connection. So, PCT provides a very parsimonious implementation of a neural network that can control the Cartpole.\n",
        "\n",
        "Each layer computes the difference between the desired and actual input (percpeption) resulting in the layer \"error\". The error along with a weight (gain) defines the desired (reference) input at the next layer.\n",
        "\n",
        "The main output of this network, for training purposes, is each of the errors, in contrast to standard neural networks which is normally the prediction (or label) of what the input should be. These errors are used to adjust (train) the network weights in order to improve performance; reduce the error.\n",
        "\n",
        "A traditional Reinforcement Learning neural network implementation of the Cartpole system is likely to have over 1,000 trainable parameters (weights). This PCT implementation has just **FOUR**!\n",
        "\n",
        "There is one other output of this network and that is the lowest level (weighted) error which varies continuously and upon which the \"action\" is based that is fed back into the Cartpole environment determining the direction in which to move the cart.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NedV8WMNPbbI"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yR12Sr7o28GD",
        "outputId": "908ff34b-1393-41a0-88b5-f85393d24f38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        }
      },
      "source": [
        "ct.plot_model(\"pct-cartpole-model.png\")\n",
        "from IPython.display import Image, display\n",
        "Image(\"pct-cartpole-model.png\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RH3JA24Y7B2F"
      },
      "source": [
        "## Error Landscape\n",
        "\n",
        "The 3-dimensional plot below shows the error landscape based upon the weights of the control units in the PCT Cartpole implementation. \n",
        "\n",
        "The plot is **interactive** so you can rotate the landscape, zoom in and hover to display the values.\n",
        "\n",
        "The x and y axes show, respectively, the two weights at the top of the hierarchy, for pole angle and pole velocity. \n",
        "\n",
        "The z axis is the global error of the system. The areas where z is zero are actually very high error, but are shown as zero to make the landscape easier to see.\n",
        "\n",
        "As you can see, the error slopes down to a minimum where pa=3.5 and pv=0.5, which should agree with those values found through the above tuning. Lower than that though, the error falls off a cliff, or, rather, falls up a cliff. That is, after that point the system becomes unstable and fails.\n",
        "\n",
        "And that is why the training above suddenly fails after a period of improvement. \n",
        "\n",
        "So, for this scenario at least, it is not sufficient just to throw an optimisation algorithm at the problem due to the severe disconuities in the error value as a result of small changes in the weights. \n",
        "\n",
        "To find the optimal values for the weights it is also necessary to understand the error landscape."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fb7cWFCUvyu",
        "outputId": "77cf231b-61f0-4aa6-9850-eceb722a428d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        }
      },
      "source": [
        "cp.load_3d_landscape(\"pctx/data/data-papv-[[00.0][10.0]-[00.0][02.0]]-cp-2.0.npy\", 2.5)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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{\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Loss Landscape papv - cp  2.0\"}, \"width\": 800, \"yaxis\": {\"automargin\": true}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('cc7708f6-2af5-444f-8e64-1f8d9de29c43');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVwEVR8TXoF5"
      },
      "source": [
        "## Optimised Run\n",
        "\n",
        "Ok, so let's take the results of the above tuning and provide the values of the weights to the cartpole and see how it does."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHg67nYg2ITC",
        "outputId": "56454ce0-1e38-43f3-c912-e5877e8ff884",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# install of utilities to display openai environment in video\n",
        "!pip install gym pyvirtualdisplay > /dev/null 2>&1\n",
        "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n",
        "!apt-get install x11-utils  > /dev/null 2>&1\n",
        "\n",
        "from gym.wrappers import Monitor\n",
        "from pyvirtualdisplay import Display\n",
        "display = Display(visible=0, size=(1400, 900))\n",
        "display.start() \n",
        "\n",
        "!git clone https://github.com/ruperty/pctx.git\n",
        "import pctx.openai.cpplots as cp\n",
        "from pctx.dl.models.cartpole import CartpoleTuning\n",
        "\n",
        "training=False\n",
        "batch_size=10\n",
        "num_runs=1000\n",
        "record_video=True\n",
        "# weights/gains\n",
        "weights=[3.5,       0.5,       2.0,       -0.05]\n",
        "\n",
        "ctagain = CartpoleTuning( env_name=\"CartPole-v1\", video_wrap=record_video)\n",
        "env = ctagain.get_env()\n",
        "obs = env.reset()\n",
        "ctagain.configure(weights=weights, loss_type=\"rsuse\",  loss_smooth=0.9, print=100, num_runs=num_runs, training=training)\n",
        "ctagain.display_configure(render=record_video)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pctx'...\n",
            "remote: Enumerating objects: 69, done.\u001b[K\n",
            "remote: Counting objects: 100% (69/69), done.\u001b[K\n",
            "remote: Compressing objects: 100% (43/43), done.\u001b[K\n",
            "remote: Total 69 (delta 19), reused 68 (delta 18), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (69/69), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dP-N7LAvx71",
        "outputId": "b16bf497-1211-4fec-e510-f2d70d1c1f09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "out = ctagain.run( batch_size, verbose=False) "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                   Weights \n",
            "Step  Loss    pole_angle pole_velocity cart_position cart_velocity  \n",
            " 100  0.177      3.500        0.500        2.000        -0.050       \n",
            " 200  0.270      3.500        0.500        2.000        -0.050       \n",
            " 300  0.246      3.500        0.500        2.000        -0.050       \n",
            " 400  0.175      3.500        0.500        2.000        -0.050       \n",
            " 500  0.165      3.500        0.500        2.000        -0.050       \n",
            " 600  0.255      3.500        0.500        2.000        -0.050       \n",
            " 700  0.166      3.500        0.500        2.000        -0.050       \n",
            " 800  0.275      3.500        0.500        2.000        -0.050       \n",
            " 900  0.168      3.500        0.500        2.000        -0.050       \n",
            "1000  0.164      3.500        0.500        2.000        -0.050       \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbAkjYl5vaHr",
        "outputId": "e34cef72-2680-4d77-a383-9e04e257d537",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "if record_video:\n",
        "  ctagain.show_video()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<video alt=\"test\" autoplay                 \n",
              "                 controls style=\"height: 400px;\">\n",
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type=\"video/mp4\" />\n",
              "             </video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8frT2LMYKRn"
      },
      "source": [
        "## Error Plot\n",
        "\n",
        "As you can see from the video and this error plot the control is pretty tight with a low global error throughout.\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GozkHjHKX7Ao",
        "outputId": "f8bac650-35d2-419f-bfaf-9da7bc9163fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "import numpy as np\n",
        "data = ctagain.get_error_data()\n",
        "data = [data[0], np.clip(data[1], 0, 1)]\n",
        "cp.scatter_plot(data , \"Global Error\", \"Time\", \"Error\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "# Comments\n",
        "\n",
        "Strangely, when I first implmented this Cartpole model in Tensorflow I had got the order of the perceptions wrong, but was actually getting better control than above (error of about ~0.15). The order, from the top level, was cp, cv, pa, pv rather than pa, pv, cp, cv.\n",
        "\n",
        "When I noticed this I was, at first, surprised that it was working at all, then  realised that as the steady-state reference at each level was to be zero, it didn't really matter what the order was. If you introduced levels where the reference was non-zero, such as in the moving cartpole, then the order would be important.\n",
        "\n",
        "It would be interesting, though, to explore why the control was better with that configuration. I'll leave that for others! "
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        "So, there we are, the Cartpole Inverted Pendulum problem implemented in Tensorflow in order to tune the control paramters to improve performance.\n",
        "\n",
        "The purpose of this demonstration has been, for me, as an initial proof of concept of combining the principles of Perceptual Control Theory with those of Deep Learning Neural Networks. It shows, in a simple way, how reorganisation takes place in a perceptual control hierarchy.\n",
        "\n",
        "As such this is just a springboard for a whole area of new research and suggests the directions to take to implement far more sophisticated dynamic and adaptive systems."
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