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    "# Assignment 1 - TD with State Aggregation\n",
    "\n",
    "Welcome to your Course 3 Programming Assignment 1. In this assignment, you will implement **semi-gradient TD(0) with State Aggregation** in an environment with a large state space. This assignment will focus on the **policy evaluation task** (prediction problem) where the goal is to accurately estimate state values under a given (fixed) policy.\n",
    "\n",
    "\n",
    "**In this assignment, you will:**\n",
    "1. Implement semi-gradient TD(0) with function approximation (state aggregation).\n",
    "2. Understand how to use supervised learning approaches to approximate value functions.\n",
    "3. Compare the impact of different resolutions of state aggregation, and see first hand how function approximation can speed up learning through generalization.\n",
    "\n",
    "**Note: You can create new cells for debugging purposes but please do not duplicate any Read-only cells. This may break the grader.**"
   ]
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    "## 500-State RandomWalk Environment\n",
    "\n",
    "In this assignment, we will implement and use a smaller 500 state  version of the problem we covered in lecture  (see \"State Aggregation with Monte Carlo”, and Example 9.1 in the [textbook](http://www.incompleteideas.net/book/RLbook2018.pdf)). The diagram below illustrates the problem.\n",
    "\n",
    "![](data/randomwalk_diagram.png)\n",
    "\n",
    "There are 500 states numbered from 1 to 500, left to right, and all episodes begin with the agent located at the center, in state 250. For simplicity, we will consider state 0 and state 501 as the left and right terminal states respectively. \n",
    "\n",
    "The episode terminates when the agent reaches the terminal state (state 0) on the left, or the terminal state (state 501) on the right. Termination on the left (state 0) gives the agent a reward of -1, and termination on the right (state 501) gives the agent a reward of +1.\n",
    "\n",
    "The agent can take one of two actions: go left or go right. If the agent chooses the left action, then it transitions uniform randomly into one of the 100 neighboring states to its left. If the agent chooses the right action, then it transitions randomly into one of the 100 neighboring states to its right. \n",
    "\n",
    "States near the edge may have fewer than 100 neighboring states on that side. In this case, all transitions that would have taken the agent past the edge result in termination. If the agent takes the left action from state 50, then it has a 0.5 chance of terminating on the left. If it takes the right action from state 499, then it has a 0.99 chance of terminating on the right.\n",
    "\n",
    "\n",
    "### Your Goal\n",
    "\n",
    "For this assignment, we will consider the problem of **policy evaluation**: estimating state-value function for a fixed policy.You will evaluate a uniform random policy in the 500-State Random Walk environment. This policy takes the right action with 0.5 probability and the left with 0.5 probability, regardless of which state it is in. \n",
    "\n",
    "This environment has a relatively large number of states. Generalization can significantly speed learning as we will show in this assignment. Often in realistic environments, states are high-dimensional and continuous. For these problems, function approximation is not just useful, it is also necessary."
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    "## Packages\n",
    "\n",
    "You will use the following packages in this assignment.\n",
    "\n",
    "- [numpy](www.numpy.org) : Fundamental package for scientific computing with Python.\n",
    "- [matplotlib](http://matplotlib.org) : Library for plotting graphs in Python.\n",
    "- [RL-Glue](http://www.jmlr.org/papers/v10/tanner09a.html) : Library for reinforcement learning experiments.\n",
    "- [jdc](https://alexhagen.github.io/jdc/) : Jupyter magic that allows defining classes over multiple jupyter notebook cells.\n",
    "- [tqdm](https://tqdm.github.io/) : A package to display progress bar when running experiments\n",
    "- plot_script : custom script to plot results\n",
    "\n",
    "**Please do not import other libraries** — this will break the autograder.\n"
   ]
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    "# Do not modify this cell!\n",
    "\n",
    "# Import necessary libraries\n",
    "# DO NOT IMPORT OTHER LIBRARIES - This will break the autograder.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import jdc\n",
    "from tqdm import tqdm\n",
    "\n",
    "from rl_glue import RLGlue\n",
    "from environment import BaseEnvironment\n",
    "from agent import BaseAgent\n",
    "import plot_script"
   ]
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   "source": [
    "## Section 1: Create the 500-State RandomWalk Environment\n",
    "\n",
    "In this section we have provided you with the implementation of the 500-State RandomWalk Environment. It is useful to know how the environment is implemented. We will also use this environment in the next programming assignment. \n",
    "\n",
    "Once the agent chooses which direction to move, the environment determines how far the agent is moved in that direction. Assume the agent passes either 0 (indicating left) or 1 (indicating right) to the environment.\n",
    "\n",
    "Methods needed to implement the environment are: `env_init`, `env_start`, and `env_step`.\n",
    "\n",
    "- `env_init`: This method sets up the environment at the very beginning of the experiment. Relevant parameters are passed through `env_info` dictionary.\n",
    "- `env_start`: This is the first method called when the experiment starts, returning the start state.\n",
    "- `env_step`: This method takes in action and returns reward, next_state, and is_terminal."
   ]
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    "# Do not modify this cell!\n",
    "\n",
    "class RandomWalkEnvironment(BaseEnvironment):\n",
    "    def env_init(self, env_info={}):\n",
    "        \"\"\"\n",
    "        Setup for the environment called when the experiment first starts.\n",
    "        \n",
    "        Set parameters needed to setup the 500-state random walk environment.\n",
    "        \n",
    "        Assume env_info dict contains:\n",
    "        {\n",
    "            num_states: 500 [int],\n",
    "            start_state: 250 [int],\n",
    "            left_terminal_state: 0 [int],\n",
    "            right_terminal_state: 501 [int],\n",
    "            seed: int\n",
    "        }\n",
    "        \"\"\"\n",
    "        \n",
    "        # set random seed for each run\n",
    "        self.rand_generator = np.random.RandomState(env_info.get(\"seed\")) \n",
    "        \n",
    "        # set each class attribute\n",
    "        self.num_states = env_info[\"num_states\"] \n",
    "        self.start_state = env_info[\"start_state\"] \n",
    "        self.left_terminal_state = env_info[\"left_terminal_state\"] \n",
    "        self.right_terminal_state = env_info[\"right_terminal_state\"]\n",
    "\n",
    "    def env_start(self):\n",
    "        \"\"\"\n",
    "        The first method called when the experiment starts, called before the\n",
    "        agent starts.\n",
    "\n",
    "        Returns:\n",
    "            The first state from the environment.\n",
    "        \"\"\"\n",
    "\n",
    "        # set self.reward_state_term tuple\n",
    "        reward = 0.0\n",
    "        state = self.start_state\n",
    "        is_terminal = False\n",
    "                \n",
    "        self.reward_state_term = (reward, state, is_terminal)\n",
    "        \n",
    "        # return first state from the environment\n",
    "        return self.reward_state_term[1]\n",
    "        \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",
    "        last_state = self.reward_state_term[1]\n",
    "        \n",
    "        # set reward, current_state, and is_terminal\n",
    "        #\n",
    "        # action: specifies direction of movement - 0 (indicating left) or 1 (indicating right)  [int]\n",
    "        # current state: next state after taking action from the last state [int]\n",
    "        # reward: -1 if terminated left, 1 if terminated right, 0 otherwise [float]\n",
    "        # is_terminal: indicates whether the episode terminated [boolean]\n",
    "        #\n",
    "        # Given action (direction of movement), determine how much to move in that direction from last_state\n",
    "        # All transitions beyond the terminal state are absorbed into the terminal state.\n",
    "        \n",
    "        if action == 0: # left\n",
    "            current_state = max(self.left_terminal_state, last_state + self.rand_generator.choice(range(-100,0)))\n",
    "        elif action == 1: # right\n",
    "            current_state = min(self.right_terminal_state, last_state + self.rand_generator.choice(range(1,101)))\n",
    "        else: \n",
    "            raise ValueError(\"Wrong action value\")\n",
    "        \n",
    "        # terminate left\n",
    "        if current_state == self.left_terminal_state: \n",
    "            reward = -1.0\n",
    "            is_terminal = True\n",
    "        \n",
    "        # terminate right\n",
    "        elif current_state == self.right_terminal_state:\n",
    "            reward = 1.0\n",
    "            is_terminal = True\n",
    "        \n",
    "        else:\n",
    "            reward = 0.0\n",
    "            is_terminal = False\n",
    "        \n",
    "        self.reward_state_term = (reward, current_state, is_terminal)\n",
    "        \n",
    "        return self.reward_state_term\n",
    "        "
   ]
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   "source": [
    "## Section 2: Create Semi-gradient TD(0) Agent with State Aggregation\n",
    "\n",
    "Now let's create the Agent that interacts with the Environment.\n",
    "\n",
    "You will create an Agent that learns with semi-gradient TD(0) with state aggregation.\n",
    "For state aggregation, if the resolution (num_groups) is 10, then 500 states are partitioned into 10 groups of 50 states each (i.e., states 1-50 are one group, states 51-100 are another, and so on.)\n",
    "\n",
    "Hence, 50 states would share the same feature and value estimate, and there would be 10 distinct features. The feature vector for each state is a one-hot feature vector of length 10, with a single one indicating the group for that state. (one-hot vector of length 10)"
   ]
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   "source": [
    "## Section 2-1: Implement Useful Functions\n",
    "\n",
    "Before we implement the agent, we need to define a couple of useful helper functions.\n",
    "\n",
    "**Please note all random method calls should be called through random number generator. Also do not use random method calls unless specified. In the agent, only `agent_policy` requires random method calls.**"
   ]
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   "source": [
    "## Section 2-1a: Selecting actions\n",
    "\n",
    "In this part we have implemented `agent_policy()` for you.\n",
    "\n",
    "This method is used in `agent_start()` and `agent_step()` to select appropriate action.\n",
    "Normally, the agent acts differently given state, but in this environment the agent chooses randomly to move either left or right with equal probability.\n",
    "\n",
    "Agent returns 0 for left, and 1 for right."
   ]
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    "# Do not modify this cell!\n",
    "\n",
    "def agent_policy(rand_generator, state):\n",
    "    \"\"\"\n",
    "    Given random number generator and state, returns an action according to the agent's policy.\n",
    "    \n",
    "    Args:\n",
    "        rand_generator: Random number generator\n",
    "\n",
    "    Returns:\n",
    "        chosen action [int]\n",
    "    \"\"\"\n",
    "    \n",
    "    # set chosen_action as 0 or 1 with equal probability\n",
    "    # state is unnecessary for this agent policy\n",
    "    chosen_action = rand_generator.choice([0,1])\n",
    "    \n",
    "    return chosen_action"
   ]
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   "source": [
    "Run the following code to verify `agent_policy`. Expected output should match since we are controlling the random seed. Verify that actions 0 and 1 are chosen equally randomly."
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "action_array: [1, 1, 1, 0, 1, 0, 0, 0, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "## Test Code for agent_policy() ##\n",
    "\n",
    "test_rand_generator = np.random.RandomState(99) \n",
    "state = 250\n",
    "\n",
    "action_array = []\n",
    "for i in range(10):\n",
    "    action_array.append(agent_policy(test_rand_generator, 250))\n",
    "    \n",
    "print('action_array: {}'.format(action_array))"
   ]
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   "source": [
    "  **Expected output**:\n",
    "  \n",
    "action_array: [1, 1, 1, 0, 1, 0, 0, 0, 1, 0]\n"
   ]
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   "source": [
    "## Section 2-1b: Processing State Features with State Aggregation\n",
    "\n",
    "In this part you will implement `get_state_feature()`\n",
    "\n",
    "This method takes in a state and returns the aggregated feature (one-hot-vector) of that state.\n",
    "The feature vector size is determined by `num_groups`. Use `state` and `num_states_in_group` to determine which element in the feature vector is active.\n",
    "\n",
    "`get_state_feature()` is necessary whenever the agent receives a state and needs to convert it to a feature for learning. The features will thus be used in `agent_step()` and `agent_end()` when the agent updates its state values."
   ]
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   "source": [
    "# [Graded]\n",
    "\n",
    "def get_state_feature(num_states_in_group, num_groups, state):\n",
    "    \"\"\"\n",
    "    Given state, return the feature of that state\n",
    "    \n",
    "    Args:\n",
    "        num_states_in_group [int]\n",
    "        num_groups [int] \n",
    "        state [int] : 1~500\n",
    "\n",
    "    Returns:\n",
    "        one_hot_vector [numpy array]\n",
    "    \"\"\"\n",
    "    \n",
    "    ### Generate state feature (2~4 lines)\n",
    "    # Create one_hot_vector with size of the num_groups, according to state\n",
    "    # For simplicity, assume num_states is always perfectly divisible by num_groups\n",
    "    # Note that states start from index 1, not 0!\n",
    "    \n",
    "    # Example:\n",
    "    # If num_states = 100, num_states_in_group = 20, num_groups = 5,\n",
    "    # one_hot_vector would be of size 5.\n",
    "    # For states 1~20, one_hot_vector would be: [1, 0, 0, 0, 0]\n",
    "    # \n",
    "    # one_hot_vector = ?\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    one_hot_vector = np.zeros(num_groups)\n",
    "    one_hot_vector[(state-1) // num_states_in_group] = 1\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return one_hot_vector\n"
   ]
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   "source": [
    "Run the following code to verify your `get_state_feature()` function."
   ]
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    {
     "name": "stdout",
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     "text": [
      "1st group: [1. 0. 0. 0. 0.]\n",
      "2nd group: [0. 1. 0. 0. 0.]\n",
      "3rd group: [0. 0. 1. 0. 0.]\n",
      "4th group: [0. 0. 0. 1. 0.]\n",
      "5th group: [0. 0. 0. 0. 1.]\n"
     ]
    }
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   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "## Test Code for get_state_feature() ##\n",
    "\n",
    "# Given that num_states = 10 and num_groups = 5, test get_state_feature()\n",
    "# There are states 1~10, and the state feature vector would be of size 5.\n",
    "# Only one element would be active for any state feature vector.\n",
    "\n",
    "# get_state_feature() should support various values of num_states, num_groups, not just this example\n",
    "# For simplicity, assume num_states will always be perfectly divisible by num_groups\n",
    "num_states = 10\n",
    "num_groups = 5\n",
    "num_states_in_group = int(num_states / num_groups)\n",
    "\n",
    "# Test 1st group, state = 1\n",
    "state = 1\n",
    "print(\"1st group: {}\".format(get_state_feature(num_states_in_group, num_groups, state)))\n",
    "\n",
    "# Test 2nd group, state = 3\n",
    "state = 3\n",
    "print(\"2nd group: {}\".format(get_state_feature(num_states_in_group, num_groups, state)))\n",
    "\n",
    "# Test 3rd group, state = 6\n",
    "state = 6\n",
    "print(\"3rd group: {}\".format(get_state_feature(num_states_in_group, num_groups, state)))\n",
    "\n",
    "# Test 4th group, state = 7\n",
    "state = 7\n",
    "print(\"4th group: {}\".format(get_state_feature(num_states_in_group, num_groups, state)))\n",
    "\n",
    "# Test 5th group, state = 10\n",
    "state = 10\n",
    "print(\"5th group: {}\".format(get_state_feature(num_states_in_group, num_groups, state)))\n"
   ]
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    "nbgrader": {
     "checksum": "c3639dc51824bb421855833469675fc5",
     "grade": false,
     "grade_id": "cell-a55d91373189d887",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "  **Expected output**:\n",
    "  \n",
    "    1st group: [1. 0. 0. 0. 0.]\n",
    "    2nd group: [0. 1. 0. 0. 0.]\n",
    "    3rd group: [0. 0. 1. 0. 0.]\n",
    "    4th group: [0. 0. 0. 1. 0.]\n",
    "    5th group: [0. 0. 0. 0. 1.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "bd0606054e551739f18f601b6d4840be",
     "grade": false,
     "grade_id": "cell-eed6babe9b563391",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 2-2: Implement Agent Methods\n",
    "\n",
    "Now that we have implemented all the helper functions, let's create an agent. In this part, you will implement `agent_init()`, `agent_start()`, `agent_step()` and `agent_end()`. You will have to use `agent_policy()` that we implemented above. We will implement `agent_message()` later, when returning the learned state-values.\n",
    "\n",
    "To save computation time, we precompute features for all states beforehand in `agent_init()`. The pre-computed features are saved in `self.all_state_features` numpy array. Hence, you do not  need to call `get_state_feature()` every time in `agent_step()` and `agent_end()`.\n",
    "\n",
    "The shape of `self.all_state_features` numpy array is `(num_states, feature_size)`, with features of states from State 1-500. Note that index 0 stores features for State 1 (Features for State 0 does not exist). Use `self.all_state_features` to access each feature vector for a state.\n",
    "\n",
    "When saving state values in the agent, recall how the state values are represented with linear function approximation.\n",
    "\n",
    "**State Value Representation**: $\\hat{v}(s,\\mathbf{w}) = \\mathbf{w}\\cdot\\mathbf{x^T}$ where $\\mathbf{w}$ is a weight vector and $\\mathbf{x}$ is the feature vector of the state.\n",
    "\n",
    "\n",
    "When performing TD(0) updates with Linear Function Approximation, recall how we perform semi-gradient TD(0) updates using supervised learning.\n",
    "\n",
    "**semi-gradient TD(0) Weight Update Rule**: $\\mathbf{w_{t+1}} = \\mathbf{w_{t}} + \\alpha [R_{t+1} + \\gamma \\hat{v}(S_{t+1},\\mathbf{w}) - \\hat{v}(S_t,\\mathbf{w})] \\nabla \\hat{v}(S_t,\\mathbf{w})$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "639e32d76f77e0d254d8b731ada078ad",
     "grade": false,
     "grade_id": "cell-cfe76b4f7bad2b67",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# [Graded]\n",
    "\n",
    "# Create TDAgent\n",
    "class TDAgent(BaseAgent):\n",
    "    def __init__(self):\n",
    "        self.num_states = None\n",
    "        self.num_groups = None\n",
    "        self.step_size = None\n",
    "        self.discount_factor = None\n",
    "        \n",
    "    def agent_init(self, agent_info={}):\n",
    "        \"\"\"Setup for the agent called when the experiment first starts.\n",
    "\n",
    "        Set parameters needed to setup the semi-gradient TD(0) state aggregation agent.\n",
    "\n",
    "        Assume agent_info dict contains:\n",
    "        {\n",
    "            num_states: 500 [int],\n",
    "            num_groups: int, \n",
    "            step_size: float, \n",
    "            discount_factor: float,\n",
    "            seed: int\n",
    "        }\n",
    "        \"\"\"\n",
    "\n",
    "        # set random seed for each run\n",
    "        self.rand_generator = np.random.RandomState(agent_info.get(\"seed\")) \n",
    "\n",
    "        # set class attributes\n",
    "        self.num_states = agent_info.get(\"num_states\")\n",
    "        self.num_groups = agent_info.get(\"num_groups\")\n",
    "        self.step_size = agent_info.get(\"step_size\")\n",
    "        self.discount_factor = agent_info.get(\"discount_factor\")\n",
    "\n",
    "        # pre-compute all observable features\n",
    "        num_states_in_group = int(self.num_states / self.num_groups)\n",
    "        self.all_state_features = np.array([get_state_feature(num_states_in_group, self.num_groups, state) for state in range(1, self.num_states + 1)])\n",
    "\n",
    "        ### initialize weights correctly (1 line)\n",
    "        # initialize all weights to zero using numpy array with correct size\n",
    "        # self.weights = ?\n",
    "\n",
    "        ### START CODE HERE ###\n",
    "        self.weights = np.zeros(self.num_groups)\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        self.last_state = None\n",
    "        self.last_action = None\n",
    "\n",
    "    def agent_start(self, state):\n",
    "        \"\"\"The first method called when the experiment starts, called after\n",
    "        the environment starts.\n",
    "        Args:\n",
    "            state (Numpy array): the state from the\n",
    "                environment's evn_start function.\n",
    "        Returns:\n",
    "            self.last_action [int] : The first action the agent takes.\n",
    "        \"\"\"\n",
    "\n",
    "        ### select action given state (using agent_policy), and save current state and action (2~3 lines)\n",
    "        # Use self.rand_generator for agent_policy\n",
    "        # \n",
    "        # self.last_state = ?\n",
    "        # self.last_action = ?\n",
    "\n",
    "        ### START CODE HERE ###\n",
    "        action = agent_policy(self.rand_generator, state)\n",
    "        self.last_state = state\n",
    "        self.last_action = action\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        return self.last_action\n",
    "\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 [int]: the state from the environment's step, where the agent ended up after the last step\n",
    "        Returns:\n",
    "            self.last_action [int] : The action the agent is taking.\n",
    "        \"\"\"\n",
    "        \n",
    "        # get relevant feature\n",
    "        current_state_feature = self.all_state_features[state-1] \n",
    "        last_state_feature = self.all_state_features[self.last_state-1] \n",
    "        \n",
    "        ### update weights and select action (3~5 lines)\n",
    "        # (Hint: np.dot method is useful!)\n",
    "        #\n",
    "        # Update weights:\n",
    "        #     use self.weights, current_state_feature, and last_state_feature\n",
    "        #\n",
    "        # Select action:\n",
    "        #     use self.rand_generator for agent_policy\n",
    "        #\n",
    "        # Current state and selected action should be saved to self.last_state and self.last_action at the end\n",
    "        #\n",
    "        # self.weights = ?\n",
    "        # self.last_state = ?\n",
    "        # self.last_action = ?\n",
    "\n",
    "        ### START CODE HERE ###\n",
    "        self.weights += self.step_size * (reward + self.discount_factor * np.dot(self.weights, current_state_feature) - np.dot(self.weights, last_state_feature)) * last_state_feature\n",
    "        action = agent_policy(self.rand_generator, state)\n",
    "        self.last_state = state\n",
    "        self.last_action = action\n",
    "        ### END CODE HERE ###\n",
    "        return self.last_action\n",
    "\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\n",
    "                terminal state.\n",
    "        \"\"\"\n",
    "\n",
    "        # get relevant feature\n",
    "        last_state_feature = self.all_state_features[self.last_state-1]\n",
    "        \n",
    "        ### update weights (1~2 lines)\n",
    "        # Update weights using self.weights and last_state_feature\n",
    "        # (Hint: np.dot method is useful!)\n",
    "        # \n",
    "        # Note that here you don't need to choose action since the agent has reached a terminal state\n",
    "        # Therefore you should not update self.last_state and self.last_action\n",
    "        # \n",
    "        # self.weights = ?\n",
    "        \n",
    "        ### START CODE HERE ###\n",
    "        self.weights += self.step_size * (reward - np.dot(self.weights, last_state_feature)) * last_state_feature\n",
    "        ### END CODE HERE ###\n",
    "        return\n",
    "        \n",
    "    def agent_message(self, message):\n",
    "        # We will implement this method later\n",
    "        raise NotImplementedError\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8b53f563e46fb2e3949010f3c16f5f9a",
     "grade": false,
     "grade_id": "cell-a92a727706966b2b",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "\n",
    "Run the following code to verify `agent_init()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "79882d4de94a6db403f376d274e6cb34",
     "grade": true,
     "grade_id": "graded_agent_init",
     "locked": true,
     "points": 5,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_states: 500\n",
      "num_groups: 10\n",
      "step_size: 0.1\n",
      "discount_factor: 1.0\n",
      "weights shape: (10,)\n",
      "weights init. value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "## Test Code for agent_init() ## \n",
    "\n",
    "agent_info = {\"num_states\": 500,\n",
    "              \"num_groups\": 10,\n",
    "              \"step_size\": 0.1,\n",
    "              \"discount_factor\": 1.0,\n",
    "              \"seed\": 1}\n",
    "\n",
    "test_agent = TDAgent()\n",
    "test_agent.agent_init(agent_info)\n",
    "\n",
    "# check attributes\n",
    "print(\"num_states: {}\".format(test_agent.num_states))\n",
    "print(\"num_groups: {}\".format(test_agent.num_groups))\n",
    "print(\"step_size: {}\".format(test_agent.step_size))\n",
    "print(\"discount_factor: {}\".format(test_agent.discount_factor))\n",
    "\n",
    "print(\"weights shape: {}\".format(test_agent.weights.shape))\n",
    "print(\"weights init. value: {}\".format(test_agent.weights))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "eaa694784b499c6edf93d7c845805928",
     "grade": false,
     "grade_id": "cell-09d1b865ec761116",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "  **Expected output**:\n",
    "  \n",
    "    num_states: 500\n",
    "    num_groups: 10\n",
    "    step_size: 0.1\n",
    "    discount_factor: 1.0\n",
    "    weights shape: (10,)\n",
    "    weights init. value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "7bf26e7339307df3557652e06cfbbd54",
     "grade": false,
     "grade_id": "cell-c47a537224d052ad",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Run the following code to verify `agent_start()`.\n",
    "Although there is randomness due to `rand_generator.choice()` in `agent_policy()`, we control the seed so your output should match the expected output. \n",
    "\n",
    "Make sure `rand_generator.choice()` is called only once per `agent_policy()` call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "fbf48a7b1eeb420f1650730d050f3c8a",
     "grade": true,
     "grade_id": "graded_agent_start",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agent state: 250\n",
      "Agent selected action: 1\n"
     ]
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "## Test Code for agent_start() and agent_policy() ## \n",
    "\n",
    "agent_info = {\"num_states\": 500,\n",
    "              \"num_groups\": 10,\n",
    "              \"step_size\": 0.1,\n",
    "              \"discount_factor\": 1.0,\n",
    "              \"seed\": 1\n",
    "             }\n",
    "\n",
    "# Suppose state = 250\n",
    "state = 250\n",
    "\n",
    "test_agent = TDAgent()\n",
    "test_agent.agent_init(agent_info)\n",
    "test_agent.agent_start(state)\n",
    "\n",
    "print(\"Agent state: {}\".format(test_agent.last_state))\n",
    "print(\"Agent selected action: {}\".format(test_agent.last_action))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "b8da6624acf4294ac9b0e9844a976551",
     "grade": false,
     "grade_id": "cell-4bb285c764d8ad67",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "**Expected output**:\n",
    "\n",
    "    Agent state: 250\n",
    "    Agent selected action: 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "a78f84de7558de794442da4c496e581e",
     "grade": false,
     "grade_id": "cell-a3d392998465216c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Run the following code to verify `agent_step()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "187c92e0f22eb19c833f17276623dfca",
     "grade": true,
     "grade_id": "graded_agent_step",
     "locked": true,
     "points": 15,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial weights: [-1.5  0.5  1.  -0.5  1.5 -0.5  1.5  0.  -0.5 -1. ]\n",
      "Updated weights: [-0.26  0.5   1.   -0.5   1.5  -0.5   1.5   0.   -0.5  -1.  ]\n",
      "weight update is correct!\n",
      "\n",
      "last state: 120\n",
      "last action: 1\n"
     ]
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "## Test Code for agent_step() ## \n",
    "# Make sure agent_init() and agent_start() are working correctly first.\n",
    "# agent_step() should work correctly for other arbitrary state transitions in addition to this test case.\n",
    "agent_info = {\"num_states\": 500,\n",
    "              \"num_groups\": 10,\n",
    "              \"step_size\": 0.1,\n",
    "              \"discount_factor\": 0.9,\n",
    "              \"seed\": 1}\n",
    "\n",
    "test_agent = TDAgent()\n",
    "test_agent.agent_init(agent_info)\n",
    "\n",
    "# Initializing the weights to arbitrary values to verify the correctness of weight update\n",
    "test_agent.weights = np.array([-1.5, 0.5, 1., -0.5, 1.5, -0.5, 1.5, 0.0, -0.5, -1.0])\n",
    "print(\"Initial weights: {}\".format(test_agent.weights))\n",
    "\n",
    "# Assume the agent started at State 50\n",
    "start_state = 50\n",
    "action = test_agent.agent_start(start_state)\n",
    "\n",
    "# Assume the reward was 10.0 and the next state observed was State 120\n",
    "reward = 10.0\n",
    "next_state = 120\n",
    "test_agent.agent_step(reward, next_state)\n",
    "print(\"Updated weights: {}\".format(test_agent.weights))\n",
    "\n",
    "if np.allclose(test_agent.weights, np.array([-0.26, 0.5, 1., -0.5, 1.5, -0.5, 1.5, 0., -0.5, -1.])):\n",
    "    print(\"weight update is correct!\\n\")\n",
    "else:\n",
    "    print(\"weight update is incorrect.\\n\")\n",
    "\n",
    "print(\"last state: {}\".format(test_agent.last_state))\n",
    "print(\"last action: {}\".format(test_agent.last_action))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "28de0ce9c2293d85cfa167c3e376aca6",
     "grade": false,
     "grade_id": "cell-feab2079de2e1fc0",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "**Expected output**: (Note only the 1st element was changed)\n",
    "    \n",
    "    Initial weights: [-1.5  0.5  1.  -0.5  1.5 -0.5  1.5  0.  -0.5 -1. ]\n",
    "    Updated weights: [-0.26  0.5   1.   -0.5   1.5  -0.5   1.5   0.   -0.5  -1.  ]\n",
    "    last state: 120\n",
    "    last action: 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "74f0d3cc7f10e820a03933ff8a9c8f57",
     "grade": false,
     "grade_id": "cell-b1a7b031081d1821",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Run the following code to verify `agent_end()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "e932f659435c5fb1885fd60dc183f614",
     "grade": true,
     "grade_id": "graded_agent_end",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial weights: [-1.5  0.5  1.  -0.5  1.5 -0.5  1.5  0.  -0.5 -1. ]\n",
      "Updated weights: [-0.35  0.5   1.   -0.5   1.5  -0.5   1.5   0.   -0.5  -1.  ]\n",
      "weight update is correct!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "## Test Code for agent_end() ## \n",
    "# Make sure agent_init() and agent_start() are working correctly first.\n",
    "\n",
    "agent_info = {\"num_states\": 500,\n",
    "              \"num_groups\": 10,\n",
    "              \"step_size\": 0.1,\n",
    "              \"discount_factor\": 0.9,\n",
    "              \"seed\": 1}\n",
    "\n",
    "test_agent = TDAgent()\n",
    "test_agent.agent_init(agent_info)\n",
    "\n",
    "# Initializing the weights to arbitrary values to verify the correctness of weight update\n",
    "test_agent.weights = np.array([-1.5, 0.5, 1., -0.5, 1.5, -0.5, 1.5, 0.0, -0.5, -1.0])\n",
    "print(\"Initial weights: {}\".format(test_agent.weights))\n",
    "\n",
    "# Assume the agent started at State 50\n",
    "start_state = 50\n",
    "test_agent.agent_start(start_state)\n",
    "\n",
    "# Assume the reward was 10.0 and reached the terminal state\n",
    "test_agent.agent_end(10.0)\n",
    "print(\"Updated weights: {}\".format(test_agent.weights))\n",
    "\n",
    "if np.allclose(test_agent.weights, np.array([-0.35, 0.5, 1., -0.5, 1.5, -0.5, 1.5, 0., -0.5, -1.])):\n",
    "    print(\"weight update is correct!\\n\")\n",
    "else:\n",
    "    print(\"weight update is incorrect.\\n\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "833af2e84df2cdad6d43bacb144e7a81",
     "grade": false,
     "grade_id": "cell-f8457a84eed9709d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "**Expected output**: (Note only the 1st element was changed, and the result is different from `agent_step()` )\n",
    "    \n",
    "    Initial weights: [-1.5  0.5  1.  -0.5  1.5 -0.5  1.5  0.  -0.5 -1. ]\n",
    "    Updated weights: [-0.35  0.5   1.   -0.5   1.5  -0.5   1.5   0.   -0.5  -1.  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "7bf058b9bfcc1f79a75944e0aaf333c6",
     "grade": false,
     "grade_id": "cell-cd580cba3ee6c3a1",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 2-3: Returning Learned State Values\n",
    "\n",
    "You are almost done! Now let's implement a code block in `agent_message()` that returns the learned state values.\n",
    "\n",
    "The method `agent_message()` will return the learned state_value array when `message == 'get state value'`.\n",
    "\n",
    "**Hint**: Think about how state values are represented with linear function approximation. `state_value` array will be a 1D array with length equal to the number of states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "3919378741a1bd275799f88b40744fc1",
     "grade": false,
     "grade_id": "cell-b469919b09cd9284",
     "locked": false,
     "schema_version": 1,
     "solution": true
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   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "# [Graded]\n",
    "\n",
    "def agent_message(self, message):\n",
    "    if message == 'get state value':\n",
    "        \n",
    "        ### return state_value (1~2 lines)\n",
    "        # Use self.all_state_features and self.weights to return the vector of all state values\n",
    "        # Hint: Use np.dot()\n",
    "        #\n",
    "        # state_value = ?\n",
    "        \n",
    "        ### START CODE HERE ###\n",
    "        state_value = np.dot(self.weights, self.all_state_features.T)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        return state_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "54f25dd3167af63a5c815dda36482c22",
     "grade": false,
     "grade_id": "cell-33209f575321ccb5",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Run the following code to verify `get_state_val()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "6df9d79197c4fcc7699195570cb9c9cb",
     "grade": true,
     "grade_id": "graded_get_state_val",
     "locked": true,
     "points": 10,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State value shape: (20,)\n",
      "Initial State value for all states: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "## Test Code for agent_get_state_val() ##\n",
    "\n",
    "agent_info = {\"num_states\": 20,\n",
    "              \"num_groups\": 5,\n",
    "              \"step_size\": 0.1,\n",
    "              \"discount_factor\": 1.0}\n",
    "\n",
    "test_agent = TDAgent()\n",
    "test_agent.agent_init(agent_info)\n",
    "test_state_val = test_agent.agent_message('get state value')\n",
    "\n",
    "print(\"State value shape: {}\".format(test_state_val.shape))\n",
    "print(\"Initial State value for all states: {}\".format(test_state_val))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "0e09f2c295f33bf6daa1e13bfbb5f4b5",
     "grade": false,
     "grade_id": "cell-8b87229733a8fd76",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "**Expected Output**:\n",
    "\n",
    "    State value shape: (20,)\n",
    "    Initial State value for all states: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "fd97d78c67f1398fe62084fc178a968c",
     "grade": false,
     "grade_id": "cell-4a2937aee7e48fe0",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 3: Run Experiment\n",
    "\n",
    "Now that we've implemented all the components of environment and agent, let's run an experiment! We will plot two things: (1) the learned state value function and compare it against the true state values, and (2) a learning curve depicting the error in the learned value estimates over episodes. For the learning curve, what should we plot to see if the agent is learning well?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "dc87729f4043121c9a25e501f1929e70",
     "grade": false,
     "grade_id": "cell-9081e37ad214f0b6",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 3-1: Prediction Objective (Root Mean Squared Value Error) \n",
    "\n",
    "Recall that the Prediction Objective in function approximation is Mean Squared Value Error $\\overline{VE}(\\mathbf{w}) \\doteq \\sum\\limits_{s \\in \\mathcal{S}}\\mu(s)[v_\\pi(s)-\\hat{v}(s,\\mathbf{w})]^2$\n",
    "\n",
    "We will use the square root of this measure, the root $\\overline{VE}$ to give a rough measure of how much the learned values differ from the true values.\n",
    "\n",
    "`calc RMSVE()` computes the Root Mean Squared Value Error given learned state value $\\hat{v}(s, \\mathbf{w})$.\n",
    "We provide you with true state value $v_\\pi(s)$ and state distribution $\\mu(s)$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "7868b93e326ebd58cb7f365882009909",
     "grade": false,
     "grade_id": "cell-72fdaf375f3e3d99",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "# Here we provide you with the true state value and state distribution\n",
    "true_state_val = np.load('data/true_V.npy')    \n",
    "state_distribution = np.load('data/state_distribution.npy')\n",
    "\n",
    "def calc_RMSVE(learned_state_val):\n",
    "    assert(len(true_state_val) == len(learned_state_val) == len(state_distribution))\n",
    "    MSVE = np.sum(np.multiply(state_distribution, np.square(true_state_val - learned_state_val)))\n",
    "    RMSVE = np.sqrt(MSVE)\n",
    "    return RMSVE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "4155d4fd0c4f33610bf7d66dc5f01777",
     "grade": false,
     "grade_id": "cell-bea80af13342f057",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 3-2a: Run Experiment with 10-State Aggregation\n",
    "\n",
    "We have provided you the experiment/plot code in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "9ae12f60fde0b13b2f50ca1a121c1781",
     "grade": false,
     "grade_id": "cell-42b7e0b38d1ead4c",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "# Define function to run experiment\n",
    "def run_experiment(environment, agent, environment_parameters, agent_parameters, experiment_parameters):\n",
    "\n",
    "    rl_glue = RLGlue(environment, agent)\n",
    "    \n",
    "    # Sweep Agent parameters\n",
    "    for num_agg_states in agent_parameters[\"num_groups\"]:\n",
    "        for step_size in agent_parameters[\"step_size\"]:\n",
    "            \n",
    "            # save rmsve at the end of each evaluation episode\n",
    "            # size: num_episode / episode_eval_frequency + 1 (includes evaluation at the beginning of training)\n",
    "            agent_rmsve = np.zeros(int(experiment_parameters[\"num_episodes\"]/experiment_parameters[\"episode_eval_frequency\"]) + 1)\n",
    "            \n",
    "            # save learned state value at the end of each run\n",
    "            agent_state_val = np.zeros(environment_parameters[\"num_states\"])\n",
    "\n",
    "            env_info = {\"num_states\": environment_parameters[\"num_states\"],\n",
    "                        \"start_state\": environment_parameters[\"start_state\"],\n",
    "                        \"left_terminal_state\": environment_parameters[\"left_terminal_state\"],\n",
    "                        \"right_terminal_state\": environment_parameters[\"right_terminal_state\"]}\n",
    "\n",
    "            agent_info = {\"num_states\": environment_parameters[\"num_states\"],\n",
    "                          \"num_groups\": num_agg_states,\n",
    "                          \"step_size\": step_size,\n",
    "                          \"discount_factor\": environment_parameters[\"discount_factor\"]}\n",
    "\n",
    "            print('Setting - num. agg. states: {}, step_size: {}'.format(num_agg_states, step_size))\n",
    "            os.system('sleep 0.2')\n",
    "            \n",
    "            # one agent setting\n",
    "            for run in tqdm(range(1, experiment_parameters[\"num_runs\"]+1)):\n",
    "                env_info[\"seed\"] = run\n",
    "                agent_info[\"seed\"] = run\n",
    "                rl_glue.rl_init(agent_info, env_info)\n",
    "                \n",
    "                # Compute initial RMSVE before training\n",
    "                current_V = rl_glue.rl_agent_message(\"get state value\")\n",
    "                agent_rmsve[0] += calc_RMSVE(current_V)\n",
    "                    \n",
    "                for episode in range(1, experiment_parameters[\"num_episodes\"]+1):\n",
    "                    # run episode\n",
    "                    rl_glue.rl_episode(0) # no step limit\n",
    "                    \n",
    "                    if episode % experiment_parameters[\"episode_eval_frequency\"] == 0:\n",
    "                        current_V = rl_glue.rl_agent_message(\"get state value\")\n",
    "                        agent_rmsve[int(episode/experiment_parameters[\"episode_eval_frequency\"])] += calc_RMSVE(current_V)\n",
    "                        \n",
    "                # store only one run of state value\n",
    "                if run == 50:\n",
    "                    agent_state_val = rl_glue.rl_agent_message(\"get state value\")\n",
    "            \n",
    "            # rmsve averaged over runs\n",
    "            agent_rmsve /= experiment_parameters[\"num_runs\"]\n",
    "            \n",
    "            save_name = \"{}_agg_states_{}_step_size_{}\".format('TD_agent', num_agg_states, step_size).replace('.','')\n",
    "            \n",
    "            if not os.path.exists('results'):\n",
    "                os.makedirs('results')\n",
    "    \n",
    "            # save avg. state value\n",
    "            np.save(\"results/V_{}\".format(save_name), agent_state_val)\n",
    "\n",
    "            # save avg. rmsve\n",
    "            np.save(\"results/RMSVE_{}\".format(save_name), agent_rmsve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "c26b5d7551c7a1b3daa3ab0af916933f",
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     "grade_id": "cell-46962f34e1051db3",
     "locked": true,
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   },
   "source": [
    "\n",
    "We will first test our implementation using state aggregation with resolution of 10, with three different step sizes: {0.01, 0.05, 0.1}.\n",
    "\n",
    "Note that running the experiment cell below will take **_approximately 5 min_**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "83dc1423ac345560f1868b5eae8c57f6",
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     "grade_id": "cell-e9bf5a92d552cda5",
     "locked": true,
     "schema_version": 1,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting - num. agg. states: 10, step_size: 0.01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [01:03<00:00,  1.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting - num. agg. states: 10, step_size: 0.05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [01:03<00:00,  1.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting - num. agg. states: 10, step_size: 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [01:03<00:00,  1.27s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "#### Run Experiment\n",
    "\n",
    "# Experiment parameters\n",
    "experiment_parameters = {\n",
    "    \"num_runs\" : 50,\n",
    "    \"num_episodes\" : 2000,\n",
    "    \"episode_eval_frequency\" : 10 # evaluate every 10 episodes\n",
    "}\n",
    "\n",
    "# Environment parameters\n",
    "environment_parameters = {\n",
    "    \"num_states\" : 500, \n",
    "    \"start_state\" : 250,\n",
    "    \"left_terminal_state\" : 0,\n",
    "    \"right_terminal_state\" : 501, \n",
    "    \"discount_factor\" : 1.0\n",
    "}\n",
    "\n",
    "# Agent parameters\n",
    "# Each element is an array because we will be later sweeping over multiple values\n",
    "agent_parameters = {\n",
    "    \"num_groups\": [10],\n",
    "    \"step_size\": [0.01, 0.05, 0.1]\n",
    "}\n",
    "\n",
    "current_env = RandomWalkEnvironment\n",
    "current_agent = TDAgent\n",
    "\n",
    "run_experiment(current_env, current_agent, environment_parameters, agent_parameters, experiment_parameters)\n",
    "plot_script.plot_result(agent_parameters, 'results')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "278568c5227efec4e4e4c1c2a6e31918",
     "grade": false,
     "grade_id": "cell-cf9f9b84e4498115",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Is the learned state value plot with step-size=0.01 similar to Figure 9.2 (p.208) in Sutton and Barto?\n",
    "\n",
    "(Note that our environment has less states: 500 states and we have done 2000 episodes, and averaged the performance over 50 runs)\n",
    "\n",
    "Look at  the plot of the learning curve. Does RMSVE decrease over time?\n",
    "\n",
    "Would it be possible to reduce RMSVE to 0?\n",
    "\n",
    "You should see the RMSVE decrease over time, but the error seems to plateau. It is impossible to reduce RMSVE to 0, because of function approximation (and we do not decay the step-size parameter to zero). With function approximation, the agent has limited resources and has to trade-off the accuracy of one state for another state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "2d162faa4b5808751fcb4433bbd81b7c",
     "grade": false,
     "grade_id": "cell-7cfde5a470e987d7",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "Run the following code to verify your experimental result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "ecf0cf04d03e75d9707fc51839a05f03",
     "grade": true,
     "grade_id": "graded_exp_result",
     "locked": true,
     "points": 35,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your experiment results are correct!\n"
     ]
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "## Test Code for experimental result##\n",
    "\n",
    "agent_parameters = {\n",
    "    \"num_groups\": [10],\n",
    "    \"step_size\": [0.01, 0.05, 0.1]\n",
    "}\n",
    "\n",
    "all_correct = True\n",
    "for num_agg_states in agent_parameters[\"num_groups\"]:\n",
    "    for step_size in agent_parameters[\"step_size\"]:\n",
    "        filename = 'RMSVE_TD_agent_agg_states_{}_step_size_{}'.format(num_agg_states, step_size).replace('.','')\n",
    "        agent_RMSVE = np.load('results/{}.npy'.format(filename))\n",
    "        correct_RMSVE = np.load('correct_npy/{}.npy'.format(filename))\n",
    "\n",
    "        if not np.allclose(agent_RMSVE, correct_RMSVE):\n",
    "            all_correct=False\n",
    "\n",
    "if all_correct:\n",
    "    print(\"Your experiment results are correct!\")\n",
    "else:\n",
    "    print(\"Your experiment results does not match with ours. Please check if you have implemented all methods correctly.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "703b4f0570c04845318e54f0e03ec360",
     "grade": false,
     "grade_id": "cell-dc298f2f5dfb981a",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 3-2b: Run Experiment with Different State Aggregation Resolution and Step-Size\n",
    "\n",
    "In this section, we will run some more experiments to see how different parameter settings affect the results!\n",
    "\n",
    "In particular, we will test several values of `num_groups` and `step_size`. Parameter sweeps although necessary, can take lots of time. So now that you have verified your experiment result, here we show you the results of the parameter sweeps that you would see when running the sweeps yourself.\n",
    "\n",
    "We tested several different values of `num_groups`: {10, 100, 500}, and `step-size`: {0.01, 0.05, 0.1}. As before, we performed 2000 episodes per run, and averaged the results over 50 runs for each setting.\n",
    "\n",
    "Run the cell below to display the sweep results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "2df7cb76187f3f0b0994a5490cdb6112",
     "grade": false,
     "grade_id": "cell-63cf84b307913593",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Display result with parameter sweeps\n",
    "\n",
    "# Make sure to verify your experiment result with the test cell above.\n",
    "# Otherwise the sweep results will not be displayed.\n",
    "\n",
    "# Experiment parameters\n",
    "experiment_parameters = {\n",
    "    \"num_runs\" : 50,\n",
    "    \"num_episodes\" : 2000,\n",
    "    \"episode_eval_frequency\" : 10 # evaluate every 10 episodes\n",
    "}\n",
    "\n",
    "# Environment parameters\n",
    "environment_parameters = {\n",
    "    \"num_states\" : 500,\n",
    "    \"start_state\" : 250,\n",
    "    \"left_terminal_state\" : 0,\n",
    "    \"right_terminal_state\" : 501,\n",
    "    \"discount_factor\" : 1.0\n",
    "}\n",
    "\n",
    "# Agent parameters\n",
    "# Each element is an array because we will be sweeping over multiple values\n",
    "agent_parameters = {\n",
    "    \"num_groups\": [10, 100, 500],\n",
    "    \"step_size\": [0.01, 0.05, 0.1]\n",
    "}\n",
    "\n",
    "if all_correct:\n",
    "    plot_script.plot_result(agent_parameters, 'correct_npy')\n",
    "else:\n",
    "    raise ValueError(\"Make sure your experiment result is correct! Otherwise the sweep results will not be displayed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "49c0fb855e6595468cbc05f352e38988",
     "grade": false,
     "grade_id": "cell-e9c6a124eb3c37e6",
     "locked": true,
     "schema_version": 1,
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    }
   },
   "source": [
    "## Wrapping up\n",
    "\n",
    "Let’s think about the results of our parameter study.\n",
    "\n",
    "### State Aggregation\n",
    "\n",
    "- Which state aggregation resolution do you think is the best after running 2000 episodes? Which state aggregation resolution do you think would be the best if we could train for only 200 episodes? What if we could train for a million episodes?\n",
    "\n",
    "- Should we use tabular representation (state aggregation of resolution 500) whenever possible? Why might we want to use function approximation?\n",
    "\n",
    "\n",
    "From the plots, using 100 state aggregation with step-size 0.05 reaches the best performance: the lowest RMSVE after 2000 episodes. If the agent can only be trained for 200 episodes, then 10 state aggregation with step-size 0.05 reaches the lowest error. Increasing the resolution of state aggregation makes the function approximation closer to a  tabular representation, which would be able to learn exactly correct state values for all states. But learning will be slower. \n",
    "\n",
    "\n",
    "### Step-Size\n",
    "\n",
    "- How did different step-sizes affect learning?\n",
    "\n",
    "The best step-size is different for different state aggregation resolutions. A larger step-size allows the agent to learn faster, but might not perform as well asymptotically. A smaller step-size causes it to learn more slowly, but may perform well asymptotically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "ef771d597be2c0842b166322410cb7fa",
     "grade": false,
     "grade_id": "cell-496cb0059a0b96d1",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "### **Congratulations!** You have successfully implemented Course 3 Programming Assignment 1.\n",
    "\n",
    "You have implemented **semi-gradient TD(0) with State Aggregation** in a 500-state Random Walk. We used an environment with a large but discrete state space, where it was possible to compute the true state values. This allowed us to compare the values learned by your agent to the true state values. The same state aggregation function approximation can also be applied to continuous state space environments, where comparison to the true values is not usually possible.\n",
    "\n",
    "\n",
    "You also successfully applied supervised learning approaches to approximate value functions with semi-gradient TD(0). \n",
    "\n",
    "Finally, we plotted the learned state values and compared with true state values. We also compared learning curves of different state aggregation resolutions and learning rates. \n",
    "\n",
    "From the results, you can  see why it is often desirable to use function approximation, even when tabular learning is possible. Asymptotically, an agent with tabular representation would be able to learn the true state value function, but it would learn much more slowly compared to an agent with function approximation. On the other hand, we also want to ensure we do not reduce discrimination too far (a coarse state aggregation resolution), because it will hurt the asymptotic performance.\n"
   ]
  }
 ],
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