reinforcement_learning/q.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys ###q5header\n",
    "sys.path.append('../scripts/')\n",
    "from dp_policy_agent import *\n",
    "import random, copy #copy追加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StateInfo:\n",
    "    def __init__(self, action_num, epsilon=0.3):\n",
    "        self.q = np.zeros(action_num)\n",
    "        self.epsilon = epsilon\n",
    "        \n",
    "    def greedy(self):\n",
    "        return np.argmax(self.q)\n",
    "    \n",
    "    def epsilon_greedy(self, epsilon): \n",
    "        if random.random() < epsilon:\n",
    "            return random.choice(range(len(self.q)))\n",
    "        else:\n",
    "            return self.greedy()\n",
    "    \n",
    "    def pi(self):\n",
    "        return self.epsilon_greedy(self.epsilon) \n",
    "    \n",
    "    def max_q(self): \n",
    "        return max(self.q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QAgent(DpPolicyAgent): \n",
    "    def __init__(self, time_interval, estimator, puddle_coef=100,  alpha=0.5, widths=np.array([0.2, 0.2, math.pi/18]).T, \\\n",
    "                 lowerleft=np.array([-4, -4]).T, upperright=np.array([4, 4]).T, dev_borders=[0.1,0.2,0.4,0.8]): #alpha追加\n",
    "        super().__init__(time_interval, estimator, None, puddle_coef, widths, lowerleft, upperright)\n",
    "        \n",
    "        nx, ny, nt = self.index_nums\n",
    "        self.indexes = list(itertools.product(range(nx), range(ny), range(nt)))\n",
    "        self.actions = list(set([tuple(self.policy_data[i]) for i in self.indexes]))\n",
    "        self.ss = self.set_action_value_function()\n",
    "        \n",
    "        ###強化学習用変数### #追加\n",
    "        self.alpha = alpha\n",
    "        self.s, self.a = None, None\n",
    "        self.update_end = False\n",
    "\n",
    "    def set_action_value_function(self): #状態価値関数を読み込んで行動価値関数を初期化\n",
    "        ss = {} #state spaceという意味\n",
    "        for line in open(\"../data/puddle_ignore_values.txt\", \"r\"): #価値のファイルを読み込む\n",
    "            d = line.split()\n",
    "            index, value = (int(d[0]), int(d[1]), int(d[2])), float(d[3]) #インデックスをタプル、値を数字に\n",
    "            ss[index] = StateInfo(len(self.actions)) #StateInfoオブジェクトを割り当てて初期化\n",
    "            \n",
    "            for i, a in enumerate(self.actions): #方策の行動価値を価値のファイルに書いてある値に。そうでない場合はちょっと引く\n",
    "                ss[index].q[i] = value if tuple(self.policy_data[index]) == a else value - 0.1\n",
    "                \n",
    "        return ss\n",
    "    \n",
    "    def policy(self, pose, goal=None): #q4policy\n",
    "        index = self.to_index(pose, self.pose_min, self.index_nums, self.widths)               \n",
    "        s = tuple(index)\n",
    "        a = self.ss[s].pi()\n",
    "        return s, a                 #状態をタプルにしたものと、行動のインデックスで返すように変更\n",
    "    \n",
    "    def decision(self, observation=None):###q4decision\n",
    "        ###終了処理###\n",
    "        if self.update_end:  return 0.0, 0.0\n",
    "        if self.in_goal:          self.update_end = True #ゴールに入った後も一回だけ更新があるので即終了はしない\n",
    "        \n",
    "        ###カルマンフィルタの実行###\n",
    "        self.estimator.motion_update(self.prev_nu, self.prev_omega, self.time_interval)\n",
    "        self.estimator.observation_update(observation)\n",
    "        \n",
    "        ###行動決定と報酬の処理###\n",
    "        s_, a_ = self.policy(self.estimator.pose) #KFの結果から前回の状態遷移後の状態s'と次の行動a_（max_a'のa'ではないことに注意）を得る\n",
    "        r = self.time_interval*self.reward_per_sec() #状態遷移の報酬\n",
    "        self.total_reward += r\n",
    "        \n",
    "        ###Q学習と現在の状態と行動の保存###\n",
    "        self.q_update(self.s, self.a, r, s_)      #self.s, self.aがQ値更新対象の状態行動対。報酬rと次の状態s_を使って更新。\n",
    "        self.s, self.a = s_, a_\n",
    "\n",
    "        ###出力###\n",
    "        self.prev_nu, self.prev_omega = self.actions[a_]\n",
    "        return self.actions[a_]\n",
    "    \n",
    "    def q_update(self, s, a, r, s_):###q4update\n",
    "        if s == None:  return\n",
    "        \n",
    "        q = self.ss[s].q[a]\n",
    "        q_ = self.final_value if self.in_goal else self.ss[s_].max_q()\n",
    "        self.ss[s].q[a] = (1.0 - self.alpha)*q + self.alpha*(r + q_)\n",
    "\n",
    "#        ###ログをとる（あとから削除）###\n",
    "#        with open(\"log.txt\", \"a\") as f:\n",
    "#            f.write(\"{} {} {} prev_q:{:.2f}, next_step_max_q:{:.2f}, new_q:{:.2f}\\n\".format(s, r, s_, q, q_, self.ss[s].q[a]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WarpRobot(Robot): ###q5warprobot\n",
    "    def __init__(self, *args, **kwargs): \n",
    "        super().__init__(*args, **kwargs)\n",
    "        \n",
    "        self.init_agent = copy.deepcopy(self.agent) #エージェントのコピーを残しておく\n",
    "        \n",
    "    def choose_pose(self):  #初期位置をランダムに決めるメソッド\n",
    "        xy = random.random()*6-2\n",
    "        t = random.random()*2*math.pi\n",
    "        return np.array([3, xy, t]).T if random.random() > 0.5 else np.array([xy, 3, t]).T\n",
    "        \n",
    "    def reset(self):\n",
    "        #ssだけ残してエージェントを初期化\n",
    "        tmp = self.agent.ss\n",
    "        self.agent = copy.deepcopy(self.init_agent)\n",
    "        self.agent.ss  = tmp\n",
    "        \n",
    "        #初期位置をセット（ロボット、カルマンフィルタ）\n",
    "        self.pose = self.choose_pose()\n",
    "        self.agent.estimator.belief = multivariate_normal(mean=self.pose, cov=np.diag([1e-10, 1e-10, 1e-10]))\n",
    "        \n",
    "        #軌跡の黒い線が残らないように消す\n",
    "        self.poses = []\n",
    "    \n",
    "    def one_step(self, time_interval):\n",
    "        if self.agent.update_end:\n",
    "            with open(\"log.txt\", \"a\") as f:\n",
    "                f.write(\"{}\\n\".format(self.agent.total_reward + self.agent.final_value))\n",
    "            self.reset()\n",
    "            return\n",
    "        \n",
    "        super().one_step(time_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"400\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def trial(): \n",
    "    time_interval = 0.1\n",
    "    world = PuddleWorld(400000, time_interval, debug=False)  #長時間アニメーション時間をとる\n",
    "\n",
    "    ## 地図を生成して3つランドマークを追加 ##\n",
    "    m = Map()\n",
    "    for ln in [(-4,2), (2,-3), (4,4), (-4,-4)]: m.append_landmark(Landmark(*ln))\n",
    "    world.append(m)   \n",
    "\n",
    "    ##ゴールの追加##\n",
    "    goal = Goal(-3,-3) \n",
    "    world.append(goal)\n",
    "    \n",
    "    ##水たまりの追加##\n",
    "    world.append(Puddle((-2, 0), (0, 2), 0.1)) \n",
    "    world.append(Puddle((-0.5, -2), (2.5, 1), 0.1)) \n",
    "\n",
    "    ##ロボットを1台登場させる##\n",
    "    init_pose = np.array([3, 3, 0]).T\n",
    "    kf = KalmanFilter(m, init_pose)\n",
    "    a = QAgent(time_interval, kf)\n",
    "    r = WarpRobot(init_pose, sensor=Camera(m, distance_bias_rate_stddev=0, direction_bias_stddev=0), \n",
    "              agent=a, color=\"red\", bias_rate_stds=(0,0))\n",
    "    world.append(r)\n",
    "    \n",
    "    world.draw()\n",
    "    return a\n",
    "    \n",
    "a = trial()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#p = np.zeros(a.index_nums[0:2])\n",
    "#for x in range(a.index_nums[0]):\n",
    "#   for y in range(a.index_nums[1]):\n",
    "#        act = a.ss[(x,y,22)].greedy()\n",
    "#        p[x,y] = a.actions[act][0] + a.actions[act][1]\n",
    "        \n",
    "#import seaborn as sns   \n",
    "#sns.heatmap(np.rot90(p), square=False)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#v = np.zeros(a.index_nums[0:2])\n",
    "#for x in range(a.index_nums[0]):\n",
    "#    for y in range(a.index_nums[1]):\n",
    "#        v[x,y] = a.ss[(x,y,18)].max_q()\n",
    "        \n",
    "#import seaborn as sns   \n",
    "#sns.heatmap(np.rot90(v), square=False)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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