OxML2020_RL1.ipynb

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\n",
                "text/plain": "<Figure size 432x288 with 1 Axes>"
              },
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": [],
                  "needs_background": "light"
                },
                "image/png": 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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OxML2020/practicals/blob/master/OxML2020_RL1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
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      "metadata": {
        "id": "KMcewmfiGZAM",
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      },
      "source": [
        "---\n",
        "#OxML 2020: Reinforcement Learning Part 1\n",
        "\n",
        "**Introduction to Reinforcement Learning and Bandit Problem**\n",
        "\n",
        "**content creator:** Fatemeh Abdollahi\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1OBA1Tj5cMV",
        "colab_type": "text"
      },
      "source": [
        "#Section 0: Introduction\n",
        "\n",
        "Using training information that evaluates the actions are taken rather than instructs by giving correct actions, is the most critical feature distinguishing reinforcement learning from other types of learning. This creates the need for actively exploring for an explicit search for good behaviour. \n",
        "\n",
        "**Purely evaluative feedback** indicates how good the action taken was, but not whether it was the best or the worst action possible. However, **purely instructive feedback** indicates the correct action to take, independently of the action taken. This kind of feedback is the basis of **supervised learning**. \n",
        "\n",
        "In this notebook, we study the evaluative aspect of reinforcement learning in a simplified setting, which does not involve learning to act in more than one situation. \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlYs4cQke6GQ",
        "colab_type": "text"
      },
      "source": [
        "#Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJaI8XkMe5WS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Imports\n",
        "from __future__ import print_function\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import ipywidgets as widgets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4ALgX174fOm",
        "colab_type": "text"
      },
      "source": [
        "# Section 1: Multi-Armed Bandit\n",
        "\n",
        "Multi-Arm Bandit problems are the classic reinforcement learning problem and some of the simplest ones to solve. We have an agent who repeatedly faced with a choice among k different actions. After each choice, it receives a reward that is returned according to a given underlying probability distribution. The game is played over many episodes, and the goal is to maximize the expected total reward or to find the arm with the highest mean value.\n",
        "\n",
        "Consider a doctor who wants to choose between experimental treatments for a series of seriously ill patients. So each action and reward can be defined as the selection of treatment and the patient's survival or well-being, respectively."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUYkct8H4U1W",
        "colab_type": "text"
      },
      "source": [
        "##Section 1.1: Choosing Action\n",
        "   \n",
        "The strategy for choosing actions based on our expectations is defined as *policy* (often denoted $\\pi$). We could have a random policy -- just pick an arm at random each time -- though this doesn't seem likely to be capable of optimizing our reward. \n",
        "\n",
        "In our *k*-armed bandit problem, each of the *k* actions has an expected or mean reward given that action is selected; it is defined as the value of that arm. We denote the action selected on time step $t$ as $A_{t}$, and the corresponding reward as $R_{t}$. \n",
        "\n",
        "\\begin{align}\n",
        "q_*(a) \\doteq \\mathbb{E} [R_{t} | A_{t} = a]\n",
        "\\end{align}\n",
        " \n",
        "In practice, this is often represented as an array of values, where each action's value is a different element in the array.\n",
        "\n",
        "If you knew the value of each action, then solving the k-armed bandit problem would be easy: you would always select the action with the highest value. We assume that you do not know the action values with certainty, although you may have estimates. We denote the estimated value of action $a$ at time step $t$ as $Q_{t}(a)$. We would like $Q_{t}(a)$ to be close to $q_{*}(a)$.\n",
        "\n",
        "An obvious choice would be to take the action with the highest value w.r.t $Q_{t}$. This is referred to as the *greedy* policy:\n",
        "\n",
        "\\begin{align}\n",
        "A_{t} = \\text{argmax}_{a} \\; Q_{t} (a)\n",
        "\\end{align}\n",
        "\n",
        "where our choice action is the one that maximizes the current value function. While you are following greedy policy, you are *exploiting* your current knowledge of the value of the actions. However, if you select one of the *nongreedy* actions, then you are *exploring*, because you can improve your estimate of the nongreedy action's value as well. \n",
        "\n",
        "So, the greedy policy does have a fatal flaw: it easily gets trapped in local maxima. It never explores to see what it hasn't seen before if one option is already better than the others. This leads us to a fundamental challenge in coming up with effective policies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhNskMDmR8QZ",
        "colab_type": "text"
      },
      "source": [
        "##Section 1.2: The Exploitation-Exploration Dilemma\n",
        "\n",
        "*Exploitation* is the right thing to do to maximize the expected reward on the one step, but *exploration* may produce the greater total reward in the long run, if we always stick to the safe bet, we don't know what we are missing. Sometimes we aren't missing much of anything, and regret not sticking with our preferred choice, yet other times we stumble upon something new that was way better than we thought.\n",
        "\n",
        "If you have many time steps ahead on which to make action selections, then it may be better to explore the nongreedy actions and discover which of them are better than the greedy action. Reward is lower in the short run, during exploration, but higher in the long run because after you have discovered the better actions, you can exploit them many times. \n",
        "\n",
        "Because it is not possible both to explore and to exploit with any single action selection, one often refers to the “conflict” between exploration and exploitation.\n",
        "\n",
        "In any specific case, whether it is better to explore or exploit depends in a complex way on the precise values of the estimates, uncertainties, and the number of remaining steps. There are many sophisticated methods for balancing exploration and exploitation for particular mathematical formulations of the k-armed bandit and related problems.\n",
        "\n",
        "A simple extension to our greedy policy is to add some randomness. For instance, a coin flip -- heads we take the best choice now, tails we pick one at random. This is referred to as the $\\epsilon$-greedy policy:\n",
        "\n",
        "\\begin{align}\n",
        "A \\leftarrow\n",
        "        \\begin{cases}\n",
        "        \\text{argmax}_{a} \\; Q_{t} (a)    & \\quad \\text {with probability  1 - }\\epsilon  \\\\\n",
        "        \\text{a random action}        & \\quad \\text{with probibility } \\epsilon \n",
        "        \\end{cases} \n",
        "\\end{align}\n",
        "\n",
        "which is to say that the probability of selecting the greedy choice is equal to 1 - $\\epsilon$ for $\\epsilon \\in [0,1]$ otherwise we select the action at random.\n",
        "\n",
        "Despite its relative simplicity, the epsilon-greedy policy is quite effective, which leads to its general popularity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyobUtGycgxx",
        "colab_type": "text"
      },
      "source": [
        "### Exercise 1: Implement Epsilon-Greedy\n",
        "\n",
        "In this exercise you will implement the epsilon-greedy algorithm for deciding which action to take from a set of possible actions given their value function and a probability $\\epsilon$ of simply chosing one at random. \n",
        "\n",
        "HINT: You may find [`np.random.random`](https://numpy.org/doc/stable/reference/random/generated/numpy.random.random.html), [`np.random.choice`](https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html), and [`np.argmax`](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) useful here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6VR4L2Tw_jT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def choose_action(qEst, epsilon):\n",
        "  # write a boolean expression that determines if we should take the best action\n",
        "  be_greedy = ...\n",
        "  if be_greedy:\n",
        "    # write an expression for selecting the best action from the action values\n",
        "    action = ...\n",
        "  else:\n",
        "    # write an expression for selecting a random action\n",
        "    action = ...\n",
        "\n",
        "  return action"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1EQJiBXdkw_",
        "colab_type": "text"
      },
      "source": [
        "##Section 1.3: Learning from Reward\n",
        "\n",
        "Now that we have a policy for deciding what to do, how do we learn from our actions?\n",
        "\n",
        "One way to do this is just to keep a record of every result we ever got and use the averages for each action. If we have a potentially very long-running episode, the computational cost of keeping all these values and recomputing the mean over and over again isn't ideal. Instead, we can use a streaming mean calculation, which looks like this:\n",
        "\n",
        "\\begin{align}\n",
        "q_{t+1}(a) \\leftarrow q_{t}(a) + \\frac{1}{n_t(a)} (r_{t} - q_{t}(a))\n",
        "\\end{align}\n",
        "\n",
        "where our action-value function $q_t(a)$ is the mean of the rewards of action $a$ seen so far, $n_t$ is the number of $a$ is taken until time $t$, and $r_t$ is the reward just received for taking action $a$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWaoRnFkgwhR",
        "colab_type": "text"
      },
      "source": [
        "###Exercise 2: Updating Action Value\n",
        "\n",
        "In this exercise, you will implement the action-value update rule above. The function will take in the action-value function represented as an array $q$, the action taken, the true action-value $q_{*} $, and an array that keeps how many times each action has been selected. The function will return the reward, action_count, and updated value for the selected action."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnfyLDoAxx5b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def take_action(action, qEst, qTrue, action_count):\n",
        "  # generate the reward under N(real reward, 1)\n",
        "  reward = np.random.randn() + qTrue[action]\n",
        "  action_count[action] += 1\n",
        "  # update estimation using sample averages\n",
        "  qEst[action] = ...\n",
        "\n",
        "  return reward, action_count, qEst"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aI1KeWakipO0",
        "colab_type": "text"
      },
      "source": [
        "##Exercise 3: Playing with Bandit\n",
        "\n",
        "In this exercise, you should use ```\n",
        "choose_action(...)``` and ```\n",
        "take_action(...)``` to play with your Bandit. The inputs of ```\n",
        "play_Bandit(...)``` are  1)the number of plays, 2)maximum time step that we would like to play 3)epsilon for $\\epsilon-greedy$  policy, and 4) the number of arms that we have in our problem.\n",
        "\n",
        "The outputs are 1)the average of the reward received up to time step $t$ over the number of plays (to have more smooth graph), and 2)the percentage of taking the best action at time $t$ over the number of plays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "px2I3aiy0b35",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def play_Bandit(n_Bandits, max_time_step, epsilon, k):\n",
        "\n",
        "  average_rewards = np.zeros((max_time_step))\n",
        "  bestActionCounts = np.zeros((max_time_step))\n",
        "\n",
        "  for i in range (0, n_Bandits):\n",
        "    #reset the environment (each i is a new experiment)\n",
        "    \n",
        "    #initial the estimated q values and action_count\n",
        "    qEst = ...\n",
        "    action_count = ...\n",
        "\n",
        "    qTrue = []\n",
        "    for i in range(0, k):\n",
        "      # initialize real rewards with N(0,1) distribution\n",
        "      qTrue.append(np.random.randn())\n",
        "\n",
        "    #determine the best action w.r.t. true q values\n",
        "    bestAction = ...\n",
        "\n",
        "    #lets play with k-armed Bandit\n",
        "    for time_step in range(0, max_time_step):\n",
        "      #choose action based on the policy\n",
        "      action = ...\n",
        "      #take the selected action\n",
        "      reward, action_count, qEst = ...\n",
        "      average_rewards[time_step] += reward\n",
        "\n",
        "      if action == bestAction:\n",
        "        bestActionCounts[time_step] += 1\n",
        "  \n",
        "  bestActionCounts /= n_Bandits\n",
        "  average_rewards /= n_Bandits\n",
        "  return bestActionCounts, average_rewards\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDQNPc475RDA",
        "colab_type": "text"
      },
      "source": [
        "So let's see the averaged reward and the percentage of choosing the best action for different epsilons: ``` [0, 0.1, 0.01] ```!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewJ4x1J37nnA",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "a717594b-82ae-4fa1-ce64-98770a19553b"
      },
      "source": [
        "#@title averaged reward and %taking best action \n",
        "figureIndex = 0\n",
        "def epsilonGreedy(nBandits, max_time_step, k):\n",
        "  epsilons = [0, 0.1, 0.01]\n",
        "  bestActionCounts_all =[]\n",
        "  averageRewards_all =[]\n",
        "  for eps in epsilons:\n",
        "    bestActionCounts, averageRewards = play_Bandit(nBandits, max_time_step, eps, k)\n",
        "    bestActionCounts_all.append(bestActionCounts)\n",
        "    averageRewards_all.append(averageRewards)\n",
        "\n",
        "  global figureIndex\n",
        "  plt.figure(figureIndex)\n",
        "  figureIndex += 1\n",
        "  for eps, counts in zip(epsilons, bestActionCounts_all):\n",
        "      plt.plot(counts, label='epsilon = '+str(eps))\n",
        "  plt.xlabel('Steps')\n",
        "  plt.ylabel('% optimal action')\n",
        "  plt.legend()\n",
        "  plt.figure(figureIndex)\n",
        "  figureIndex += 1\n",
        "  for eps, rewards in zip(epsilons, averageRewards_all):\n",
        "      plt.plot(rewards, label='epsilon = '+str(eps))\n",
        "  plt.xlabel('Steps')\n",
        "  plt.ylabel('average reward')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "epsilonGreedy(2000, 1000, 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wz6q8jrs9UNi",
        "colab_type": "text"
      },
      "source": [
        "##Live Show: Number of Arms\n",
        "\n",
        "Let's try different number of arms and see its effect on our graphs!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8Qmy3sW6UI8",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605,
          "referenced_widgets": [
            "32e4385c050341e68b59e55886d62f5b",
            "4d7afd0f871948a1b5dac646d5e89ba2",
            "0cf69a08621f42b78812cd3323a03d5b",
            "fde43f7a24424bb3a79c7b47ed87fc88",
            "ae651da54edb4175ba7b0115615cbbe6",
            "086193d300544786ad5cefa4a877d1b7",
            "c7c9b77b9c34421f94a4feba3fe9aec7",
            "3b16db2ff4224a9aba198780f57d4d91",
            "ccf12688e1824991871c21ee13380a2a",
            "5c1134172e0e4903a861fac39a778bb9"
          ]
        },
        "outputId": "7b9d089e-5f22-4266-aec1-1e87da68a4cd"
      },
      "source": [
        "#@title\n",
        "\n",
        "#@markdown Make sure you execute this cell to enable the widget!\n",
        "\n",
        "@widgets.interact_manual(n_arms=widgets.IntSlider(10, min=2, max=15))\n",
        "def explore_bandit_parameters(k=10):\n",
        "  epsilonGreedy(1000, 500, k)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32e4385c050341e68b59e55886d62f5b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=10, description='k', max=30, min=-10), Button(description='Run Interact'…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edUnB0oU-oQG",
        "colab_type": "text"
      },
      "source": [
        "#References\n",
        "\n",
        "1. Neuromatch [RL Tutorial](https://github.com/NeuromatchAcademy/course-content/tree/master/tutorials/W2D5_ReinforcementLearning)\n",
        "2. [RL an introduction -solutions](https://github.com/JaeDukSeo/reinforcement-learning-an-introduction/blob/master/chapter02/TenArmedTestbed.py)\n",
        "3. [RL an introduction, second edition](http://www.incompleteideas.net/book/the-book.html)"
      ]
    }
  ]
}