reinforcement_learning/sarsa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../scripts/')\n",
    "from dp_policy_agent import *\n",
    "import random, copy"
   ]
  },
  {
   "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 SarsaAgent(DpPolicyAgent):  #名前をSarsaAgentに ###sarsa1\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]): \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##\n",
    "        self.alpha = alpha\n",
    "        self.s, self.a = None, None\n",
    "        self.update_end = False\n",
    "\n",
    "    def set_action_value_function(self):\n",
    "        ss = {}\n",
    "        for line in open(\"../data/puddle_ignore_values.txt\", \"r\"):\n",
    "            d = line.split()\n",
    "            index, value = (int(d[0]), int(d[1]), int(d[2])), float(d[3])\n",
    "            ss[index] = StateInfo(len(self.actions))\n",
    "            \n",
    "            for i, a in enumerate(self.actions):\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): \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\n",
    "    \n",
    "    def decision(self, observation=None):\n",
    "        ###終了処理###\n",
    "        if self.update_end:  return 0.0, 0.0\n",
    "        if self.in_goal:          self.update_end = True\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)\n",
    "        r = self.time_interval*self.reward_per_sec() \n",
    "        self.total_reward += r\n",
    "        \n",
    "        ##学習と現在の状態と行動の保存##\n",
    "        self.q_update(self.s, self.a, r, s_, a_) #a_も引数に加える\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_, a_):###sarsa1\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_].q[a_]     #max_qからQ(s_,a_)に書き換え\n",
    "        self.ss[s].q[a] = (1.0 - self.alpha)*q + self.alpha*(r + q_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WarpRobot(Robot):\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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       "}\n",
       "\n",
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       "    // Updates the figure title.\n",
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       "\n",
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       "        break;\n",
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       "        break;\n",
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       "}\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 = SarsaAgent(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": [
    "def draw_policy():\n",
    "    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": [
    "def draw_value():\n",
    "    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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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