Temporal Difference.ipynb

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    "# Assignment: Policy Evaluation in Cliff Walking Environment\n",
    "\n",
    "Welcome to the Course 2 Module 2 Programming Assignment! In this assignment, you will implement one of the fundamental sample and bootstrapping based model free reinforcement learning agents for prediction. This is namely one that uses one-step temporal difference learning, also known as TD(0). The task is to design an agent for policy evaluation in the Cliff Walking environment. Recall that policy evaluation is the prediction problem where the goal is to accurately estimate the values of states given some policy.\n",
    "\n",
    "### Learning Objectives\n",
    "- Implement parts of the Cliff Walking environment, to get experience specifying MDPs [Section 1].\n",
    "- Implement an agent that uses bootstrapping and, particularly, TD(0) [Section 2].\n",
    "- Apply TD(0) to estimate value functions for different policies, i.e., run policy evaluation experiments [Section 3]."
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    "## The Cliff Walking Environment\n",
    "\n",
    "The Cliff Walking environment is a gridworld with a discrete state space and discrete action space. The agent starts at grid cell S. The agent can move (deterministically) to the four neighboring cells by taking actions Up, Down, Left or Right. Trying to move out of the boundary results in staying in the same location. So, for example, trying to move left when at a cell on the leftmost column results in no movement at all and the agent remains in the same location. The agent receives -1 reward per step in most states, and -100 reward when falling off of the cliff. This is an episodic task; termination occurs when the agent reaches the goal grid cell G. Falling off of the cliff results in resetting to the start state, without termination.\n",
    "\n",
    "The diagram below showcases the description above and also illustrates two of the policies we will be evaluating.\n",
    "\n",
    "<img src=\"cliffwalk.png\" style=\"height:400px\">"
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    "## Packages.\n",
    "\n",
    "We import the following libraries that are required for this assignment. We shall be using the following libraries:\n",
    "1. jdc: Jupyter magic that allows defining classes over multiple jupyter notebook cells.\n",
    "2. numpy: the fundamental package for scientific computing with Python.\n",
    "3. matplotlib: the library for plotting graphs in Python.\n",
    "4. RL-Glue: the library for reinforcement learning experiments.\n",
    "5. BaseEnvironment, BaseAgent: the base classes from which we will inherit when creating the environment and agent classes in order for them to support the RL-Glue framework.\n",
    "6. Manager: the file allowing for visualization and testing.\n",
    "7. itertools.product: the function that can be used easily to compute permutations.\n",
    "8. tqdm.tqdm: Provides progress bars for visualizing the status of loops.\n",
    "\n",
    "**Please do not import other libraries** — this will break the autograder.\n",
    "\n",
    "**NOTE: For this notebook, there is no need to make any calls to methods of random number generators. Spurious or missing calls to random number generators may affect your results.**"
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    "# Do not modify this cell!\n",
    "\n",
    "import jdc\n",
    "# --\n",
    "import numpy as np\n",
    "# --\n",
    "from rl_glue import RLGlue\n",
    "# --\n",
    "from Agent import BaseAgent \n",
    "from Environment import BaseEnvironment  \n",
    "# --\n",
    "from manager import Manager\n",
    "# --\n",
    "from itertools import product\n",
    "# --\n",
    "from tqdm import tqdm"
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    "## Section 1. Environment\n",
    "\n",
    "In the first part of this assignment, you will get to see how the Cliff Walking environment is implemented. You will also get to implement parts of it to aid your understanding of the environment and more generally how MDPs are specified. In particular, you will implement the logic for:\n",
    " 1. Converting 2-dimensional coordinates to a single index for the state,\n",
    " 2. One of the actions (action up), and,\n",
    " 3. Reward and termination.\n",
    " \n",
    "Given below is an annotated diagram of the environment with more details that may help in completing the tasks of this part of the assignment. Note that we will be creating a more general environment where the height and width positions can be variable but the start, goal and cliff grid cells have the same relative positions (bottom left, bottom right and the cells between the start and goal grid cells respectively).\n",
    "\n",
    "<img src=\"cliffwalk-annotated.png\" style=\"height:400px\">\n",
    "\n",
    "Once you have gone through the code and begun implementing solutions, it may be a good idea to come back here and see if you can convince yourself that the diagram above is an accurate representation of the code given and the code you have written."
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    "# Do not modify this cell!\n",
    "\n",
    "# Create empty CliffWalkEnvironment class.\n",
    "# These methods will be filled in later cells.\n",
    "class CliffWalkEnvironment(BaseEnvironment):\n",
    "    def env_init(self, agent_info={}):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def env_start(self, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def env_step(self, reward, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def env_end(self, reward):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def env_cleanup(self, reward):\n",
    "        raise NotImplementedError\n",
    "    \n",
    "    # helper method\n",
    "    def state(self, loc):\n",
    "        raise NotImplementedError"
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    "## env_init()\n",
    "\n",
    "The first function we add to the environment is the initialization function which is called once when an environment object is created. In this function, the grid dimensions and special locations (start and goal locations and the cliff locations) are stored for easy use later."
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    "%%add_to CliffWalkEnvironment\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def env_init(self, env_info={}):\n",
    "        \"\"\"Setup for the environment called when the experiment first starts.\n",
    "        Note:\n",
    "            Initialize a tuple with the reward, first state, boolean\n",
    "            indicating if it's terminal.\n",
    "        \"\"\"\n",
    "        \n",
    "        # Note, we can setup the following variables later, in env_start() as it is equivalent. \n",
    "        # Code is left here to adhere to the note above, but these variables are initialized once more\n",
    "        # in env_start() [See the env_start() function below.]\n",
    "        \n",
    "        reward = None\n",
    "        state = None # See Aside\n",
    "        termination = None\n",
    "        self.reward_state_term = (reward, state, termination)\n",
    "        \n",
    "        # AN ASIDE: Observation is a general term used in the RL-Glue files that can be interachangeably \n",
    "        # used with the term \"state\" for our purposes and for this assignment in particular. \n",
    "        # A difference arises in the use of the terms when we have what is called Partial Observability where \n",
    "        # the environment may return states that may not fully represent all the information needed to \n",
    "        # predict values or make decisions (i.e., the environment is non-Markovian.)\n",
    "        \n",
    "        # Set the default height to 4 and width to 12 (as in the diagram given above)\n",
    "        self.grid_h = env_info.get(\"grid_height\", 4) \n",
    "        self.grid_w = env_info.get(\"grid_width\", 12)\n",
    "        \n",
    "        # Now, we can define a frame of reference. Let positive x be towards the direction down and \n",
    "        # positive y be towards the direction right (following the row-major NumPy convention.)\n",
    "        # Then, keeping with the usual convention that arrays are 0-indexed, max x is then grid_h - 1 \n",
    "        # and max y is then grid_w - 1. So, we have:\n",
    "        # Starting location of agent is the bottom-left corner, (max x, min y). \n",
    "        self.start_loc = (self.grid_h - 1, 0)\n",
    "        # Goal location is the bottom-right corner. (max x, max y).\n",
    "        self.goal_loc = (self.grid_h - 1, self.grid_w - 1)\n",
    "        \n",
    "        # The cliff will contain all the cells between the start_loc and goal_loc.\n",
    "        self.cliff = [(self.grid_h - 1, i) for i in range(1, (self.grid_w - 1))]\n",
    "        \n",
    "        # Take a look at the annotated environment diagram given in the above Jupyter Notebook cell to \n",
    "        # verify that your understanding of the above code is correct for the default case, i.e., where \n",
    "        # height = 4 and width = 12."
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    "## *Implement* state()\n",
    "    \n",
    "The agent location can be described as a two-tuple or coordinate (x, y) describing the agent’s position. \n",
    "However, we can convert the (x, y) tuple into a single index and provide agents with just this integer.\n",
    "One reason for this choice is that the spatial aspect of the problem is secondary and there is no need \n",
    "for the agent to know about the exact dimensions of the environment. \n",
    "From the agent’s viewpoint, it is just perceiving some states, accessing their corresponding values \n",
    "in a table, and updating them. Both the coordinate (x, y) state representation and the converted coordinate representation are thus equivalent in this sense.\n",
    "\n",
    "Given a grid cell location, the state() function should return the state; a single index corresponding to the location in the grid.\n",
    "\n",
    "\n",
    "```\n",
    "Example: Suppose grid_h is 2 and grid_w is 2. Then, we can write the grid cell two-tuple or coordinate\n",
    "states as follows (following the usual 0-index convention):\n",
    "|(0, 0) (0, 1)| |0 1|\n",
    "|(1, 0) (1, 1)| |2 3|\n",
    "Assuming row-major order as NumPy does,  we can flatten the latter to get a vector [0 1 2 3].\n",
    "So, if loc = (0, 0) we return 0. While, for loc = (1, 1) we return 3.\n",
    "```"
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    "%%add_to CliffWalkEnvironment\n",
    "#GRADED FUNCTION: [state]\n",
    "\n",
    "# Work Required: Yes. Modify the return statement of this function to return a correct single index as \n",
    "# the state (see the logic for this in the previous cell.)\n",
    "# Lines: 1\n",
    "def state(self, loc):\n",
    "    i, j = loc\n",
    "    return i * self.grid_w + j\n",
    "    ### END CODE HERE ###"
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    "### AUTOGRADER TESTS FOR STATE (5 POINTS)\n",
    "# NOTE: The test below corresponds to the annotated diagram for the environment\n",
    "#       given previously and is limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_state():\n",
    "    env = CliffWalkEnvironment()\n",
    "    env.env_init({\"grid_height\": 4, \"grid_width\": 12})\n",
    "    coords_to_test = [(0, 0), (0, 11), (1, 5), (3, 0), (3, 9), (3, 11)]\n",
    "    true_states = [0, 11, 17, 36, 45, 47]\n",
    "    output_states = [env.state(coords) for coords in coords_to_test]\n",
    "    assert(output_states == true_states)\n",
    "test_state()"
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    "## env_start()\n",
    "\n",
    "In env_start(), we initialize the agent location to be the start location and return the state corresponding to it as the first state for the agent to act upon. Additionally, we also set the reward and termination terms to be 0 and False respectively as they are consistent with the notion that there is no reward nor termination before the first action is even taken."
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    "%%add_to CliffWalkEnvironment\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def env_start(self):\n",
    "    \"\"\"The first method called when the episode starts, called before the\n",
    "    agent starts.\n",
    "\n",
    "    Returns:\n",
    "        The first state from the environment.\n",
    "    \"\"\"\n",
    "    reward = 0\n",
    "    # agent_loc will hold the current location of the agent\n",
    "    self.agent_loc = self.start_loc\n",
    "    # state is the one dimensional state representation of the agent location.\n",
    "    state = self.state(self.agent_loc)\n",
    "    termination = False\n",
    "    self.reward_state_term = (reward, state, termination)\n",
    "\n",
    "    return self.reward_state_term[1]"
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    "## *Implement* env_step()\n",
    "\n",
    "Once an action is taken by the agent, the environment must provide a new state, reward and termination signal. \n",
    "\n",
    "In the Cliff Walking environment, agents move around using a 4-cell neighborhood called the Von Neumann neighborhood (https://en.wikipedia.org/wiki/Von_Neumann_neighborhood). Thus, the agent has 4 available actions at each state. Three of the actions have been implemented for you and your first task is to implement the logic for the fourth action (Action UP).\n",
    "\n",
    "Your second task for this function is to implement the reward logic. Look over the environment description given earlier in this notebook if you need a refresher for how the reward signal is defined."
   ]
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    "%%add_to CliffWalkEnvironment\n",
    "#GRADED FUNCTION: [env_step]\n",
    "\n",
    "# Work Required: Yes. Fill in the code for action UP and implement the logic for reward and termination.\n",
    "# Lines: ~7.\n",
    "def env_step(self, action):\n",
    "    \"\"\"A step taken by the environment.\n",
    "\n",
    "    Args:\n",
    "        action: The action taken by the agent\n",
    "\n",
    "    Returns:\n",
    "        (float, state, Boolean): a tuple of the reward, state,\n",
    "            and boolean indicating if it's terminal.\n",
    "    \"\"\"\n",
    "\n",
    "    if action == 0: # UP (Task 1)\n",
    "        ### START CODE HERE ###\n",
    "        possible_next_loc = (self.agent_loc[0]-1, self.agent_loc[1])\n",
    "        if possible_next_loc[0] >= 0:\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass\n",
    "        ### END CODE HERE ###\n",
    "    elif action == 1: # LEFT\n",
    "        possible_next_loc = (self.agent_loc[0], self.agent_loc[1] - 1)\n",
    "        if possible_next_loc[1] >= 0: # Within Bounds?\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass # Stay.\n",
    "    elif action == 2: # DOWN\n",
    "        possible_next_loc = (self.agent_loc[0] + 1, self.agent_loc[1])\n",
    "        if possible_next_loc[0] < self.grid_h: # Within Bounds?\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass # Stay.\n",
    "    elif action == 3: # RIGHT\n",
    "        possible_next_loc = (self.agent_loc[0], self.agent_loc[1] + 1)\n",
    "        if possible_next_loc[1] < self.grid_w: # Within Bounds?\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass # Stay.\n",
    "    else: \n",
    "        raise Exception(str(action) + \" not in recognized actions [0: Up, 1: Left, 2: Down, 3: Right]!\")\n",
    "\n",
    "    reward = -1\n",
    "    terminal = False\n",
    "\n",
    "    ### START CODE HERE ###\n",
    "    # Hint: Consider the initialization of reward and terminal variables above. Then, note the \n",
    "    # conditional statements and comments given below and carefully ensure to set the variables reward \n",
    "    # and terminal correctly for each case.\n",
    "    if self.agent_loc == self.goal_loc: # Reached Goal!\n",
    "        reward = -1\n",
    "        terminal = True\n",
    "    elif self.agent_loc in self.cliff: # Fell into the cliff!\n",
    "        reward = -100\n",
    "        terminal = False\n",
    "        self.agent_loc = self.start_loc\n",
    "    else: \n",
    "        reward = -1\n",
    "        terminal = False\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    self.reward_state_term = (reward, self.state(self.agent_loc), terminal)\n",
    "    return self.reward_state_term"
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    "### AUTOGRADER TESTS FOR ACTION UP (5 POINTS)\n",
    "# NOTE: The test below is again limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_action_up():\n",
    "    env = CliffWalkEnvironment()\n",
    "    env.env_init({\"grid_height\": 4, \"grid_width\": 12})\n",
    "    env.agent_loc = (0, 0)\n",
    "    env.env_step(0)\n",
    "    assert(env.agent_loc == (0, 0))\n",
    "    \n",
    "    env.agent_loc = (1, 0)\n",
    "    env.env_step(0)\n",
    "    assert(env.agent_loc == (0, 0))\n",
    "test_action_up()"
   ]
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    "### AUTOGRADER TESTS FOR REWARD & TERMINATION (10 POINTS)\n",
    "# NOTE: The test below is limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_reward():\n",
    "    env = CliffWalkEnvironment()\n",
    "    env.env_init({\"grid_height\": 4, \"grid_width\": 12})\n",
    "    env.agent_loc = (0, 0)\n",
    "    reward_state_term = env.env_step(0)\n",
    "    assert(reward_state_term[0] == -1 and reward_state_term[1] == env.state((0, 0)) and\n",
    "           reward_state_term[2] == False)\n",
    "    \n",
    "    env.agent_loc = (3, 1)\n",
    "    reward_state_term = env.env_step(2)\n",
    "    assert(reward_state_term[0] == -100 and reward_state_term[1] == env.state((3, 0)) and\n",
    "           reward_state_term[2] == False)\n",
    "    \n",
    "    env.agent_loc = (2, 11)\n",
    "    reward_state_term = env.env_step(2)\n",
    "    assert(reward_state_term[0] == -1 and reward_state_term[1] == env.state((3, 11)) and\n",
    "           reward_state_term[2] == True)\n",
    "test_reward()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "4a499bb0871653446d6b303c2625df09",
     "grade": false,
     "grade_id": "cell-88082a3dab94c4a8",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## env_cleanup()\n",
    "\n",
    "There is not much cleanup to do for the Cliff Walking environment. Here, we simply reset the agent location to be the start location in this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "ef29fa83791e5f064a53af742f2859a2",
     "grade": false,
     "grade_id": "cell-4c235eb2667f9f0d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "%%add_to CliffWalkEnvironment\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def env_cleanup(self):\n",
    "    \"\"\"Cleanup done after the environment ends\"\"\"\n",
    "    self.agent_loc = self.start_loc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "180b31b525fb94442eb79f07d7097700",
     "grade": false,
     "grade_id": "cell-ff4e65eb47d735c9",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 2. Agent\n",
    "\n",
    "In this second part of the assignment, you will be implementing the key updates for Temporal Difference Learning. There are two cases to consider depending on whether an action leads to a terminal state or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "d4eaa5e48e54f9c5e55149325b215d47",
     "grade": false,
     "grade_id": "cell-3abcfd06cde56935",
     "locked": true,
     "schema_version": 1,
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   "outputs": [],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "# Create empty TDAgent class.\n",
    "# These methods will be filled in later cells.\n",
    "\n",
    "class TDAgent(BaseAgent):\n",
    "    def agent_init(self, agent_info={}):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def agent_start(self, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def agent_step(self, reward, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def agent_end(self, reward):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def agent_cleanup(self):        \n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def agent_message(self, message):\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "83d69efb64983138188a2bad513622b1",
     "grade": false,
     "grade_id": "cell-edd826505e77e70a",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## agent_init()\n",
    "\n",
    "As we did with the environment, we first initialize the agent once when a TDAgent object is created. In this function, we create a random number generator, seeded with the seed provided in the agent_info dictionary to get reproducible results. We also set the policy, discount and step size based on the agent_info dictionary. Finally, with a convention that the policy is always specified as a mapping from states to actions and so is an array of size (# States, # Actions), we initialize a values array of shape (# States,) to zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "24d451072e0b43f3ad7980887c6f5861",
     "grade": false,
     "grade_id": "cell-077135deef2f8fd9",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_init(self, agent_info={}):\n",
    "    \"\"\"Setup for the agent called when the experiment first starts.\"\"\"\n",
    "\n",
    "    # Create a random number generator with the provided seed to seed the agent for reproducibility.\n",
    "    self.rand_generator = np.random.RandomState(agent_info.get(\"seed\"))\n",
    "\n",
    "    # Policy will be given, recall that the goal is to accurately estimate its corresponding value function. \n",
    "    self.policy = agent_info.get(\"policy\")\n",
    "    # Discount factor (gamma) to use in the updates.\n",
    "    self.discount = agent_info.get(\"discount\")\n",
    "    # The learning rate or step size parameter (alpha) to use in updates.\n",
    "    self.step_size = agent_info.get(\"step_size\")\n",
    "\n",
    "    # Initialize an array of zeros that will hold the values.\n",
    "    # Recall that the policy can be represented as a (# States, # Actions) array. With the \n",
    "    # assumption that this is the case, we can use the first dimension of the policy to\n",
    "    # initialize the array for values.\n",
    "    self.values = np.zeros((self.policy.shape[0],))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "a4f9d05505f09198739674b1b330b9cd",
     "grade": false,
     "grade_id": "cell-99e8e59cd1f7a5ef",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "# agent_start()\n",
    "\n",
    "In agent_start(), we choose an action based on the initial state and policy we are evaluating. We also cache the state so that we can later update its value when we perform a Temporal Difference update. Finally, we return the action chosen so that the RL loop can continue and the environment can execute this action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8c5490c3d5fa88339b02cdf933f7f106",
     "grade": false,
     "grade_id": "cell-1b6dd05f7f49c1fc",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_start(self, state):\n",
    "    \"\"\"The first method called when the episode starts, called after\n",
    "    the environment starts.\n",
    "    Args:\n",
    "        state (Numpy array): the state from the environment's env_start function.\n",
    "    Returns:\n",
    "        The first action the agent takes.\n",
    "    \"\"\"\n",
    "    # The policy can be represented as a (# States, # Actions) array. So, we can use \n",
    "    # the second dimension here when choosing an action.\n",
    "    action = self.rand_generator.choice(range(self.policy.shape[1]), p=self.policy[state])\n",
    "    self.last_state = state\n",
    "    return action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "fd9be2a7be0a8fbd3aee6b7c14f4ec37",
     "grade": false,
     "grade_id": "cell-a472a21b4a57b885",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## *Implement* agent_step()\n",
    "\n",
    "In agent_step(), the agent must:\n",
    "\n",
    "- Perform an update to improve the value estimate of the previously visited state, and\n",
    "- Act based on the state provided by the environment.\n",
    "\n",
    "The latter of the two steps above has been implemented for you. Implement the former. Note that, unlike later in agent_end(), the episode has not yet ended in agent_step(). in other words, the previously observed state was not a terminal state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "4c68b1daf60c5fbf3c75d4329048af15",
     "grade": false,
     "grade_id": "cell-2bec3235783127e8",
     "locked": false,
     "schema_version": 1,
     "solution": true
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   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "#[GRADED] FUNCTION: [agent_step]\n",
    "\n",
    "# Work Required: Yes. Fill in the TD-target and update.\n",
    "# Lines: ~2.\n",
    "def agent_step(self, reward, state):\n",
    "    \"\"\"A step taken by the agent.\n",
    "    Args:\n",
    "        reward (float): the reward received for taking the last action taken\n",
    "        state (Numpy array): the state from the\n",
    "            environment's step after the last action, i.e., where the agent ended up after the\n",
    "            last action\n",
    "    Returns:\n",
    "        The action the agent is taking.\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ###\n",
    "    # Hint: We should perform an update with the last state given that we now have the reward and\n",
    "    # next state. We break this into two steps. Recall for example that the Monte-Carlo update \n",
    "    # had the form: V[S_t] = V[S_t] + alpha * (target - V[S_t]), where the target was the return, G_t.\n",
    "    target = reward + self.discount * self.values[state]\n",
    "    self.values[self.last_state] = self.values[self.last_state] + self.step_size * (target - self.values[self.last_state])\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Having updated the value for the last state, we now act based on the current \n",
    "    # state, and set the last state to be current one as we will next be making an \n",
    "    # update with it when agent_step is called next once the action we return from this function \n",
    "    # is executed in the environment.\n",
    "\n",
    "    action = self.rand_generator.choice(range(self.policy.shape[1]), p=self.policy[state])\n",
    "    self.last_state = state\n",
    "\n",
    "    return action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8f516e1bdb6ca3f1a1ce385cf2c56765",
     "grade": false,
     "grade_id": "cell-f5ed31e224e22cda",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## *Implement* agent_end() \n",
    "\n",
    "Implement the TD update for the case where an action leads to a terminal state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "65b123f954dd8176f089d389bc2e9735",
     "grade": false,
     "grade_id": "cell-08c5ac56c1a0a841",
     "locked": false,
     "schema_version": 1,
     "solution": true
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   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "#[GRADED] FUNCTION: [agent_end]\n",
    "\n",
    "# Work Required: Yes. Fill in the TD-target and update.\n",
    "# Lines: ~2.\n",
    "def agent_end(self, reward):\n",
    "    \"\"\"Run when the agent terminates.\n",
    "    Args:\n",
    "        reward (float): the reward the agent received for entering the terminal state.\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ###\n",
    "    # Hint: Here too, we should perform an update with the last state given that we now have the \n",
    "    # reward. Note that in this case, the action led to termination. Once more, we break this into \n",
    "    # two steps, computing the target and the update itself that uses the target and the \n",
    "    # current value estimate for the state whose value we are updating.\n",
    "    target = reward\n",
    "    self.values[self.last_state] = self.values[self.last_state] + self.step_size * (target - self.values[self.last_state])\n",
    "    ### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8e12df5724ecfa704bc67e2577c2ea34",
     "grade": false,
     "grade_id": "cell-06cbc6053aa156bc",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## agent_cleanup()\n",
    "\n",
    "In cleanup, we simply reset the last state to be None to ensure that we are not storing any states past an episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "540f808d320ce14b29ee7e8ddcda6ae7",
     "grade": false,
     "grade_id": "cell-c39f860970dfe3a7",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_cleanup(self):\n",
    "    \"\"\"Cleanup done after the agent ends.\"\"\"\n",
    "    self.last_state = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "f62a4cb327c230937f3bc459f305e6f6",
     "grade": false,
     "grade_id": "cell-aadbaa36598224e6",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## agent_message()\n",
    "\n",
    "agent_message() can generally be used to get different kinds of information about an RLGlue agent in the interaction loop of RLGlue. Here, we conditonally check for a message matching \"get_values\" and use it to retrieve the values table the agent has been updating over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "9e222e9cc5396065dafe6e39a2052097",
     "grade": false,
     "grade_id": "cell-a440254b4f0573e2",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_message(self, message):\n",
    "    \"\"\"A function used to pass information from the agent to the experiment.\n",
    "    Args:\n",
    "        message: The message passed to the agent.\n",
    "    Returns:\n",
    "        The response (or answer) to the message.\n",
    "    \"\"\"\n",
    "    if message == \"get_values\":\n",
    "        return self.values\n",
    "    else:\n",
    "        raise Exception(\"TDAgent.agent_message(): Message not understood!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "6830d90d89beea893034ebe0d7b2373c",
     "grade": true,
     "grade_id": "td_updates",
     "locked": true,
     "points": 20,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "### AUTOGRADER TESTS FOR TD-UPDATES (20 POINTS)\n",
    "# NOTE: The test belows serve as a good check in debugging your code for the TD updates. However, \n",
    "#       as with the other tests, it is limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_td_updates():\n",
    "    # The following test checks that the TD check works for a case where the transition \n",
    "    # garners reward -1 and does not lead to a terminal state. This is in a simple two state setting \n",
    "    # where there is only one action. The first state's current value estimate is 0 while the second is 1.\n",
    "    # Note the discount and step size if you are debugging this test.\n",
    "    agent = TDAgent()\n",
    "    policy_list = np.array([[1.], [1.]])\n",
    "    agent.agent_init({\"policy\": np.array(policy_list), \"discount\": 0.99, \"step_size\": 0.1})\n",
    "    agent.values = np.array([0., 1.])\n",
    "    agent.agent_start(0)\n",
    "    reward = -1\n",
    "    next_state = 1\n",
    "    agent.agent_step(reward, next_state)\n",
    "    assert(np.isclose(agent.values[0], -0.001) and np.isclose(agent.values[1], 1.))\n",
    "    \n",
    "    # The following test checks that the TD check works for a case where the transition \n",
    "    # garners reward -100 and lead to a terminal state. This is in a simple one state setting \n",
    "    # where there is only one action. The state's current value estimate is 0.\n",
    "    # Note the discount and step size if you are debugging this test.\n",
    "    agent = TDAgent()\n",
    "    policy_list = np.array([[1.]])\n",
    "    agent.agent_init({\"policy\": np.array(policy_list), \"discount\": 0.99, \"step_size\": 0.1})\n",
    "    agent.values = np.array([0.])\n",
    "    agent.agent_start(0)\n",
    "    reward = -100\n",
    "    next_state = 0\n",
    "    agent.agent_end(reward)\n",
    "    assert(np.isclose(agent.values[0], -10))\n",
    "    \n",
    "test_td_updates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": false,
    "nbgrader": {
     "checksum": "ba2a884124c4e5c073f529f94a2bd580",
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     "grade_id": "cell-3f4e7f85a3f548e9",
     "locked": true,
     "schema_version": 1,
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   "source": [
    "## Section 3. Policy Evaluation Experiments\n",
    "\n",
    "Finally, in this last part of the assignment, you will get to see the TD policy evaluation algorithm in action by looking at the estimated values, the per state value error and after the experiment is complete, the Mean Squared Value Error curve vs. episode number, summarizing how the value error changed over time.\n",
    "\n",
    "The code below runs one run of an experiment given env_info and agent_info dictionaries. A \"manager\" object is created for visualizations and is used in part for the autograder. By default, the run will be for 5000 episodes. The true_values_file is specified to compare the learned value function with the values stored in the true_values_file. Plotting of the learned value  function occurs by default after every 100 episodes. In addition, when true_values_file is specified, the value error per state and the root mean square value error will also be plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "d929e2760dd07cbdd0bea18f05639d81",
     "grade": false,
     "grade_id": "cell-7b2ee7906789af2d",
     "locked": true,
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   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "# Work Required: No. \n",
    "def run_experiment(env_info, agent_info, \n",
    "                   num_episodes=5000,\n",
    "                   experiment_name=None,\n",
    "                   plot_freq=100,\n",
    "                   true_values_file=None,\n",
    "                   value_error_threshold=1e-8):\n",
    "    env = CliffWalkEnvironment\n",
    "    agent = TDAgent\n",
    "    rl_glue = RLGlue(env, agent)\n",
    "\n",
    "    rl_glue.rl_init(agent_info, env_info)\n",
    "\n",
    "    manager = Manager(env_info, agent_info, true_values_file=true_values_file, experiment_name=experiment_name)\n",
    "    for episode in range(1, num_episodes + 1):\n",
    "        rl_glue.rl_episode(0) # no step limit\n",
    "        if episode % plot_freq == 0:\n",
    "            values = rl_glue.agent.agent_message(\"get_values\")\n",
    "            manager.visualize(values, episode)\n",
    "\n",
    "    values = rl_glue.agent.agent_message(\"get_values\")\n",
    "    if true_values_file is not None:\n",
    "        # Grading: The Manager will check that the values computed using your TD agent match \n",
    "        # the true values (within some small allowance) across the states. In addition, it also\n",
    "        # checks whether the root mean squared value error is close to 0.\n",
    "        manager.run_tests(values, value_error_threshold)\n",
    "    \n",
    "    return values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "4fad676a6e39768fdfe6897b2fb9032a",
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     "grade_id": "cell-17e3e97164635860",
     "locked": true,
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   },
   "source": [
    "The cell below just runs a policy evaluation experiment with the determinstic optimal policy that strides just above the cliff. You should observe that the per state value error and RMSVE curve asymptotically go towards 0. The arrows in the four directions denote the probabilities of taking each action. This experiment is ungraded but should serve as a good test for the later experiments. The true values file provided for this experiment may help with debugging as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "5f6345cb14ab92ac88ba1653695a1cec",
     "grade": false,
     "grade_id": "cell-70b3816dea097209",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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>"
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     "output_type": "display_data"
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "env_info = {\"grid_height\": 4, \"grid_width\": 12, \"seed\": 0}\n",
    "agent_info = {\"discount\": 1, \"step_size\": 0.01, \"seed\": 0}\n",
    "\n",
    "# The Optimal Policy that strides just along the cliff\n",
    "policy = np.ones(shape=(env_info['grid_width'] * env_info['grid_height'], 4)) * 0.25\n",
    "policy[36] = [1, 0, 0, 0]\n",
    "for i in range(24, 35):\n",
    "    policy[i] = [0, 0, 0, 1]\n",
    "policy[35] = [0, 0, 1, 0]\n",
    "\n",
    "agent_info.update({\"policy\": policy})\n",
    "\n",
    "true_values_file = \"optimal_policy_value_fn.npy\"\n",
    "_ = run_experiment(env_info, agent_info, num_episodes=5000, experiment_name=\"Policy Evaluation on Optimal Policy\",\n",
    "                   plot_freq=500, true_values_file=true_values_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "9b8fd1dd026cb36082aa0d6245763627",
     "grade": false,
     "grade_id": "cell-11c76882b7939d66",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# The Safe Policy\n",
    "# Hint: Fill in the array below (as done in the previous cell) based on the safe policy illustration \n",
    "# in the environment diagram. This is the policy that strides as far as possible away from the cliff. \n",
    "# We call it a \"safe\" policy because if the environment has any stochasticity, this policy would do a good job in \n",
    "# keeping the agent from falling into the cliff (in contrast to the optimal policy shown before). \n",
    "# BOILERPLATE:\n",
    "policy = np.ones(shape=(env_info['grid_width'] * env_info['grid_height'], 4)) * 0.25\n",
    "### START CODE HERE ###\n",
    "for i in range(env_info['grid_height']-1, 0, -1):\n",
    "    policy[i * env_info['grid_width']] = [1,0,0,0]\n",
    "for i in range(env_info['grid_width']):\n",
    "    policy[i] = [0,0,0,1]\n",
    "for i in range(env_info['grid_height']-1):\n",
    "    policy[i * env_info['grid_width'] + env_info['grid_width'] - 1] = [0,0,1,0]\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "b67dc6aaaa345faed7768ab6c07a926c",
     "grade": true,
     "grade_id": "safe_policy",
     "locked": true,
     "points": 20,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### AUTO-GRADER TESTS FOR POLICY EVALUATION WITH SAFE POLICY\n",
    "agent_info.update({\"policy\": policy})\n",
    "v = run_experiment(env_info, agent_info,\n",
    "               experiment_name=\"Policy Evaluation On Safe Policy\",\n",
    "               num_episodes=5000, plot_freq=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "64c386534d040888bfa2fe3ba58f1370",
     "grade": false,
     "grade_id": "cell-3a8720c5685c8716",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "# A Near Optimal Stochastic Policy\n",
    "# Now, we try a stochastic policy that deviates a little from the optimal policy seen above. \n",
    "# This means we can get different results due to randomness.\n",
    "# We will thus average the value function estimates we get over multiple runs. \n",
    "# This can take some time, upto about 5 minutes from previous testing. \n",
    "# NOTE: The autograder will compare . Re-run this cell upon making any changes.\n",
    "\n",
    "env_info = {\"grid_height\": 4, \"grid_width\": 12}\n",
    "agent_info = {\"discount\": 1, \"step_size\": 0.01}\n",
    "\n",
    "policy = np.ones(shape=(env_info['grid_width'] * env_info['grid_height'], 4)) * 0.25\n",
    "policy[36] = [0.9, 0.1/3., 0.1/3., 0.1/3.]\n",
    "for i in range(24, 35):\n",
    "    policy[i] = [0.1/3., 0.1/3., 0.1/3., 0.9]\n",
    "policy[35] = [0.1/3., 0.1/3., 0.9, 0.1/3.]\n",
    "agent_info.update({\"policy\": policy})\n",
    "agent_info.update({\"step_size\": 0.01})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "deletable": false,
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     "grade_id": "near_optimal_stochastic_policy",
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     "text": [
      "100%|██████████| 30/30 [01:52<00:00,  3.73s/it]\n"
     ]
    }
   ],
   "source": [
    "### AUTO-GRADER TESTS FOR POLICY EVALUATION WITH NEAR OPTIMAL STOCHASTIC POLICY (40 POINTS)\n",
    "arr = []\n",
    "from tqdm import tqdm\n",
    "for i in tqdm(range(30)):\n",
    "    env_info['seed'] = i\n",
    "    agent_info['seed'] = i\n",
    "    v = run_experiment(env_info, agent_info,\n",
    "                   experiment_name=\"Policy Evaluation On Optimal Policy\",\n",
    "                   num_episodes=5000, plot_freq=10000)\n",
    "    arr.append(v)\n",
    "average_v = np.array(arr).mean(axis=0)"
   ]
  },
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   "source": [
    "## Wrapping Up\n",
    "Congratulations, you have completed assignment 2! In this assignment, we investigated a very useful concept for sample-based online learning: temporal difference. We particularly looked at the prediction problem where the goal is to find the value function corresponding to a given policy. In the next assignment, by learning the action-value function instead of the state-value function, you will get to see how temporal difference learning can be used in control as well."
   ]
  }
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