model-free-learning/continuous-problems/discretization/tiling_discretization_acrobot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tile Coding\n",
    "\n",
    "### 0.Background: Automatically Discretization Scheme\n",
    "\n",
    "In reinforcement learning, if you have prior knowledge about the state space, you can manually design an appropriate discretization scheme. For example, in the MountainCar scenario, we could design a specific discretization method, see previous [notebook](./discretization_mountaincar.ipynb). However, when dealing with unknown or arbitrary environments, a more generic method is required.\n",
    "\n",
    "#### Tile Coding\n",
    "\n",
    "Tile coding is an elegant discretization method that works well for continuous state spaces. The main steps and characteristics of tile coding are as follows:\n",
    "\n",
    "1. **Overlaying Multiple Grids**  \n",
    "   - In a continuous state space (e.g., a 2D space), multiple grids (called \"tiles\" or \"tilings\") are overlaid on top of each other.  \n",
    "   - Each grid is slightly offset from the others to capture finer-grained information about the states.\n",
    "\n",
    "2. **State Representation**  \n",
    "   - Any position $ S $ in the state space can be coarsely identified by the tiles it activates.  \n",
    "   - Each tile is assigned a binary bit: tiles that are activated are represented as 1, while inactive tiles are represented as 0.  \n",
    "   - As a result, the state $ S $ can be represented as a sparse binary vector.\n",
    "\n",
    "3. **Efficient State Value Representation**  \n",
    "   - Instead of storing a separate value for each state $ V(S) $, the state value function is computed using the binary vector representation of the state and a weight for each tile.  \n",
    "   - The tile coding algorithm iteratively updates these weights.\n",
    "\n",
    "4. **Smoothing the Value Function**  \n",
    "   - Since neighboring positions share some of the same tiles, their state values also share some weight components.  \n",
    "   - This mechanism effectively smooths the learned value function.\n",
    "\n",
    "#### Limitations of Tile Coding\n",
    "\n",
    "- Tile sizes, offsets, and the number of tilings must be manually selected.  \n",
    "- These hyperparameters can significantly affect the performance and learning outcomes.\n",
    "\n",
    "#### Adaptive Tile Coding\n",
    "\n",
    "To overcome the limitations of manually setting parameters, adaptive tile coding can be used. Its key features include:\n",
    "\n",
    "1. **Dynamic Partitioning**  \n",
    "   - Start with relatively large tiles.  \n",
    "   - Split tiles into smaller sub-tiles when finer representations are needed.\n",
    "\n",
    "2. **Splitting Conditions**  \n",
    "   - Trigger splitting when the current representation is no longer effective for learning (e.g., when the value function stops changing significantly).  \n",
    "   - Splitting stops when a predefined maximum number of splits or iterations is reached.\n",
    "\n",
    "3. **Selecting Tiles to Split**  \n",
    "   - Track sub-tiles and their projected weights to identify tiles that have the greatest impact on the value function.  \n",
    "   - Choose the tile with the largest difference between sub-tile weights for splitting.\n",
    "\n",
    "4. **Advantages**  \n",
    "   - Adaptive tile coding does not rely on humans to specify the discretization scheme in advance.  \n",
    "   - The resulting state space is automatically partitioned based on the complexity of the problem.\n",
    "\n",
    "### 1. Import the Necessary Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import common libraries\n",
    "import sys\n",
    "import gymnasium as gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for game rendering\n",
    "import time\n",
    "from PIL import Image\n",
    "from IPython import display\n",
    "\n",
    "from plot_utils import visualize_tilings, visualize_encoded_samples\n",
    "\n",
    "# Set plotting options\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "np.set_printoptions(precision=3, linewidth=120)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Explore Environment\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "    <img src=\"./images/acrobot.gif\" alt=\"Mountain Car Environment\" width=\"30%\">\n",
    "</div>\n",
    "\n",
    "We'll use [OpenAI Gym Acrobot-v1](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/classic_control/acrobot.py) environments to test and develop our algorithms. This environment have:\n",
    "\n",
    "#### Continuous State Space\n",
    "\n",
    "The observation is a `ndarray` with shape `(6,)` that provides information about the two rotational joint angles as well as their angular velocities:\n",
    "\n",
    "| Num  | Observation                  | Min                 | Max               |\n",
    "| ---- | ---------------------------- | ------------------- | ----------------- |\n",
    "| 0    | Cosine of `theta1`           | -1                  | 1                 |\n",
    "| 1    | Sine of `theta1`             | -1                  | 1                 |\n",
    "| 2    | Cosine of `theta2`           | -1                  | 1                 |\n",
    "| 3    | Sine of `theta2`             | -1                  | 1                 |\n",
    "| 4    | Angular velocity of `theta1` | ~ -12.567 (-4 * pi) | ~ 12.567 (4 * pi) |\n",
    "| 5    | Angular velocity of `theta2` | ~ -28.274 (-9 * pi) | ~ 28.274 (9 * pi) |\n",
    "\n",
    "where\n",
    "\n",
    "- `theta1` is the angle of the first joint, where an angle of 0 indicates the first link is pointing directly downwards.\n",
    "- `theta2` is ***relative to the angle of the first link.*** An angle of 0 corresponds to having the same angle between the two links.\n",
    "\n",
    "The angular velocities of `theta1` and `theta2` are bounded at ±4π, and ±9π rad/s respectively. A state of `[1, 0, 1, 0, ..., ...]` indicates that both links are pointing downwards.\n",
    "\n",
    "#### Discrete Action Space\n",
    "\n",
    "The action is discrete, deterministic, and represents the torque applied on the actuated joint between the two links.\n",
    "\n",
    "| Num  | Action                                | Unit         |\n",
    "| ---- | ------------------------------------- | ------------ |\n",
    "| 0    | apply -1 torque to the actuated joint | torque (N m) |\n",
    "| 1    | apply 0 torque to the actuated joint  | torque (N m) |\n",
    "| 2    | apply 1 torque to the actuated joint  | torque (N m) |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make('Acrobot-v1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the next code cell to watch a random agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score: -200.0\n"
     ]
    }
   ],
   "source": [
    "# Create an environment and set random seed\n",
    "state, info = env.reset(seed=42)\n",
    "score = 0\n",
    "for t in range(200):\n",
    "    action = env.action_space.sample()\n",
    "\n",
    "    state, reward, done, _, _ = env.step(action)\n",
    "    score += reward\n",
    "    if done:\n",
    "        break\n",
    "\n",
    "print('Final score:', score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore the state and action spaces, as well as sample them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State space:\n",
      " Box([ -1.     -1.     -1.     -1.    -12.566 -28.274], [ 1.     1.     1.     1.    12.566 28.274], (6,), float32)\n",
      " - low: [ -1.     -1.     -1.     -1.    -12.566 -28.274]\n",
      " - high: [ 1.     1.     1.     1.    12.566 28.274]\n",
      "Action space:\n",
      " Discrete(3)\n",
      "--------------------------------------------------\n",
      "State space 10 samples:\n",
      "[[-3.437e-01 -8.008e-01 -4.368e-01 -5.740e-01  4.587e+00  8.053e+00]\n",
      " [-3.239e-02 -5.069e-01  1.542e-02 -2.859e-01 -1.128e+01  2.058e+01]\n",
      " [-8.117e-01 -6.361e-01 -1.239e-01  8.608e-01 -6.395e+00  9.587e+00]\n",
      " [ 3.923e-01  6.577e-01 -1.301e-01  1.453e-01  4.709e+00  2.104e+01]\n",
      " [ 7.010e-01  2.092e-01  4.305e-01 -6.450e-01  2.985e+00 -2.314e+01]\n",
      " [-2.575e-01  8.722e-01  1.400e-02 -6.969e-01  6.413e+00 -2.301e+01]\n",
      " [-3.137e-01  5.310e-01  4.375e-01 -8.212e-01 -1.226e+01  9.126e+00]\n",
      " [-4.687e-02  7.214e-01 -1.839e-01  4.987e-01  6.293e+00 -4.460e+00]\n",
      " [ 6.472e-01 -2.495e-01  1.342e-01 -3.073e-01  4.398e+00  2.500e+01]\n",
      " [ 6.546e-01  3.240e-01 -7.078e-01 -4.660e-01 -7.003e+00  6.625e+00]]\n",
      "Action space 10 samples:\n",
      "[0 2 0 0 1 2 0 2 1 2]\n"
     ]
    }
   ],
   "source": [
    "# Explore state (observation) space\n",
    "print(\"State space:\\n\", env.observation_space)\n",
    "print(\" - low:\", env.observation_space.low)\n",
    "print(\" - high:\", env.observation_space.high)\n",
    "\n",
    "# Explore action space\n",
    "print(\"Action space:\\n\", env.action_space)\n",
    "\n",
    "print(\"-\"*50)\n",
    "# Generate some samples from the state space \n",
    "print(\"State space 10 samples:\")\n",
    "print(np.array([env.observation_space.sample() for i in range(10)]))\n",
    "\n",
    "# Generate some samples from the action space\n",
    "print(\"Action space 10 samples:\")\n",
    "print(np.array([env.action_space.sample() for i in range(10)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the state space is multi-dimensional, with most dimensions ranging from -1 to 1 (positions of the two joints), while the final two dimensions have a larger range. How do we discretize such a space using tiles?\n",
    "\n",
    "### 3. Tiling\n",
    "\n",
    "\n",
    "#### Create Tiling Grid\n",
    "\n",
    "Let's first design a way to create a single tiling for a given state space. This is very similar to a uniform grid! The only difference is that you should include an offset for each dimension that shifts the split points.\n",
    "\n",
    "For instance, if we have\n",
    "- `low = [-1.0, -5.0]`, \n",
    "- `high = [1.0, 5.0]`, \n",
    "- `bins = (10, 10)`,\n",
    "- `offsets = (-0.1, 0.5)`, \n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "    <img src=\"./images/tile-coding.png\" alt=\"Mountain Car Environment\" width=\"70%\">\n",
    "</div>\n",
    "\n",
    "then return a list of 2 NumPy arrays (2 dimensions) each containing the following split points (9 split points per dimension):\n",
    "\n",
    "```\n",
    "[array([-0.9, -0.7, -0.5, -0.3, -0.1,  0.1,  0.3,  0.5,  0.7]),\n",
    " array([-3.5, -2.5, -1.5, -0.5,  0.5,  1.5,  2.5,  3.5,  4.5])]\n",
    "```\n",
    "\n",
    "Notice how the split points for the first dimension are offset by `-0.1`, and for the second dimension are offset by `+0.5`. \n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "    <img src=\"./images/tiling-grid.png\" alt=\"Mountain Car Environment\" width=\"70%\">\n",
    "</div>\n",
    "\n",
    "This might mean that some of our tiles, especially along the perimeter, are partially outside the valid state space, but that is unavoidable and harmless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-0.9, -0.7, -0.5, -0.3, -0.1,  0.1,  0.3,  0.5,  0.7]),\n",
       " array([-3.5, -2.5, -1.5, -0.5,  0.5,  1.5,  2.5,  3.5,  4.5])]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_tiling_grid(low, high, bins=(10, 10), offsets=(0.0, 0.0)):\n",
    "    \"\"\"Define a uniformly-spaced grid that can be used for tile-coding a space.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    low : array_like\n",
    "        Lower bounds for each dimension of the continuous space.\n",
    "    high : array_like\n",
    "        Upper bounds for each dimension of the continuous space.\n",
    "    bins : tuple\n",
    "        Number of bins or tiles along each corresponding dimension.\n",
    "    offsets : tuple\n",
    "        Split points for each dimension should be offset by these values.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    grid : list of array_like\n",
    "        A list of arrays containing split points for each dimension.\n",
    "    \"\"\"\n",
    "    grid = []\n",
    "    for low, high, bin, offset in zip(low, high, bins, offsets):\n",
    "        bin_size = (high - low) / bin\n",
    "        grid.append(low + bin_size * np.arange(1, bin) + offset)\n",
    "    return grid\n",
    "\n",
    "low = [-1.0, -5.0]\n",
    "high = [1.0, 5.0]\n",
    "create_tiling_grid(low, high, bins=(10, 10), offsets=(-0.1, 0.5))  # [test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use this function to define a set of tilings that are a little offset from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[array([-0.866, -0.666, -0.466, -0.266, -0.066,  0.134,  0.334,  0.534,  0.734]),\n",
       "  array([-4.33, -3.33, -2.33, -1.33, -0.33,  0.67,  1.67,  2.67,  3.67])],\n",
       " [array([-0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8]),\n",
       "  array([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])],\n",
       " [array([-0.734, -0.534, -0.334, -0.134,  0.066,  0.266,  0.466,  0.666,  0.866]),\n",
       "  array([-3.67, -2.67, -1.67, -0.67,  0.33,  1.33,  2.33,  3.33,  4.33])]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_tilings(low, high, tiling_specs):\n",
    "    \"\"\"Define multiple tilings using the provided specifications.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    low : array_like\n",
    "        Lower bounds for each dimension of the continuous space.\n",
    "    high : array_like\n",
    "        Upper bounds for each dimension of the continuous space.\n",
    "    tiling_specs : list of tuples\n",
    "        A sequence of (bins, offsets) to be passed to create_tiling_grid().\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tilings : list\n",
    "        A list of tilings (grids), each produced by create_tiling_grid().\n",
    "    \"\"\"\n",
    "    tilings = []\n",
    "    for bins, offsets in tiling_specs:\n",
    "        tilings.append(create_tiling_grid(low, high, bins, offsets))\n",
    "    return tilings\n",
    "\n",
    "\n",
    "# Tiling specs: [(<bins>, <offsets>), ...]\n",
    "tiling_specs = [((10, 10), (-0.066, -0.33)),\n",
    "                ((10, 10), (0.0, 0.0)),\n",
    "                ((10, 10), (0.066, 0.33))]\n",
    "tilings = create_tilings(low, high, tiling_specs)\n",
    "tilings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Tiling Grid\n",
    "It may be hard to gauge whether you are getting desired results or not. So let's try to visualize these tilings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Tilings'}>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 835,
       "width": 825
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_tilings(tilings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### State Space From Continous to Discreate\n",
    "\n",
    "Now that we have a way to generate these tilings, we can next write our encoding function that will convert any given continuous state value to a discrete vector.\n",
    "\n",
    "Below function is to produce a vector that contains the indices for each tile that the input state value belongs to. The shape of the vector can be the same as the arrangment of tiles, or it can be ultimately flattened for convenience.\n",
    "\n",
    "Similarly, for each tile we can use the [`numpy.digitize()`](https://docs.scipy.org/doc/numpy-1.9.3/reference/generated/numpy.digitize.html) function for this purpose.\n",
    "\n",
    "Assume the grid is a list of NumPy arrays containing the following split points:\n",
    "```\n",
    "[array([-0.8, -0.6, -0.4, -0.2,  0.0,  0.2,  0.4,  0.6,  0.8]),\n",
    " array([-4.0, -3.0, -2.0, -1.0,  0.0,  1.0,  2.0,  3.0,  4.0])]\n",
    "```\n",
    "\n",
    "Here are some potential samples and their corresponding discretized representations:\n",
    "```\n",
    "[-1.0 , -5.0] => [0, 0]\n",
    "[-0.81, -4.1] => [0, 0]\n",
    "[-0.8 , -4.0] => [1, 1]\n",
    "[-0.5 ,  0.0] => [2, 5]\n",
    "[ 0.2 , -1.9] => [6, 3]\n",
    "[ 0.8 ,  4.0] => [9, 9]\n",
    "[ 0.81,  4.1] => [9, 9]\n",
    "[ 1.0 ,  5.0] => [9, 9]\n",
    "```\n",
    "\n",
    "Finally, we use the same `discretize()` function here from grid-based discretization, and simply call it for each tiling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-1.2, -5.1) \t=>  tile 0 : [0 0],  tile 1 : [0 0],  tile 2 : [0 0], \n",
      "(-0.75, 3.25) \t=>  tile 0 : [1 8],  tile 1 : [1 8],  tile 2 : [0 7], \n",
      "(-0.5, 0.0) \t=>  tile 0 : [2 5],  tile 1 : [2 5],  tile 2 : [2 4], \n",
      "(0.25, -1.9) \t=>  tile 0 : [6 3],  tile 1 : [6 3],  tile 2 : [5 2], \n",
      "(0.15, -1.75) \t=>  tile 0 : [6 3],  tile 1 : [5 3],  tile 2 : [5 2], \n",
      "(0.75, 2.5) \t=>  tile 0 : [9 7],  tile 1 : [8 7],  tile 2 : [8 7], \n",
      "(0.7, -3.7) \t=>  tile 0 : [8 1],  tile 1 : [8 1],  tile 2 : [8 0], \n",
      "(1.0, 5.0) \t=>  tile 0 : [9 9],  tile 1 : [9 9],  tile 2 : [9 9], \n"
     ]
    }
   ],
   "source": [
    "def discretize(sample, grid):\n",
    "    \"\"\"Discretize a sazmple as per given grid.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    sample : array_like\n",
    "        A single sample from the (original) continuous space.\n",
    "    grid : list of array_like\n",
    "        A list of arrays containing split points for each dimension.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    discretized_sample : array_like\n",
    "        A sequence of integers with the same number of dimensions as sample.\n",
    "    \"\"\"\n",
    "    return tuple(np.digitize(s, g) for s, g in zip(sample, grid))\n",
    "\n",
    "\n",
    "def tile_encode(sample, tilings, flatten=False):\n",
    "    \"\"\"Encode given sample using tile-coding.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    sample : array_like\n",
    "        A single sample from the (original) continuous space.\n",
    "    tilings : list\n",
    "        A list of tilings (grids), each produced by create_tiling_grid().\n",
    "    flatten : bool\n",
    "        If true, flatten the resulting binary arrays into a single long vector.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    encoded_sample : list or array_like\n",
    "        A list of binary vectors, one for each tiling, or flattened into one.\n",
    "    \"\"\"\n",
    "    encoded_sample = np.array(\n",
    "        [discretize(sample, grid) for grid in tilings]\n",
    "    )\n",
    "    if flatten:\n",
    "        encoded_sample = np.concatenate(encoded_sample)\n",
    "    return encoded_sample\n",
    "\n",
    "    \n",
    "\n",
    "# Test with some sample values on 2D space\n",
    "# Reminder: in acrobot problem we have 6 dim in each sample\n",
    "samples = [(-1.2 , -5.1 ),\n",
    "           (-0.75,  3.25),\n",
    "           (-0.5 ,  0.0 ),\n",
    "           ( 0.25, -1.9 ),\n",
    "           ( 0.15, -1.75),\n",
    "           ( 0.75,  2.5 ),\n",
    "           ( 0.7 , -3.7 ),\n",
    "           ( 1.0 ,  5.0 )]\n",
    "encoded_samples = [tile_encode(sample, tilings, flatten=False) for sample in samples]\n",
    "for sample, encoded in zip(samples, encoded_samples):\n",
    "    print(f\"{sample} \\t=> \", end=\"\")\n",
    "    if len(encoded) == len(tilings):\n",
    "        for i, enc in enumerate(encoded):\n",
    "            print(f\" tile {i} : {enc}, \", end=\"\")\n",
    "        print()\n",
    "    else:\n",
    "        print(encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Discretized State Space\n",
    "\n",
    "Note that we did not flatten the encoding above, which is why each sample's representation is a pair of indices for each tiling. This makes it easy to visualize it using the tilings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Tile-encoded samples'}>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 835,
       "width": 825
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_encoded_samples(samples, encoded_samples, tilings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the results, we can see:\n",
    "   - Any position $ S $ in the state space can be coarsely identified by the tiles it activates.  \n",
    "   - Each tile is assigned a binary bit: tiles that are activated and then given indice corresponding with specific tile, while inactive tiles are represented as 0.  \n",
    "\n",
    "### 4. Q-Table with Tile Coding\n",
    "\n",
    "The next step is to design a special Q-table that is able to utilize this tile coding scheme. It  have the same kind of interface as a regular table, i.e. given a `<state, action>` pair, it return a `<value>`. Similarly, it also allow update the `<value>` for a given `<state, action>` pair (note that this should update all the tiles that `<state>` belongs to).\n",
    "\n",
    "In contrast to previous [uniform discretization method](./discretization_mountaincar.ipynb), we use matrix instead of dict to represent Q table, for vectorize computation convience.\n",
    "\n",
    "The `<state>` supplied here is assumed to be from the original continuous state space, and `<action>` is discrete (and integer index). The Q-table should internally convert the `<state>` to its tile-coded representation when required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q table size = (9, 9, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.QTable at 0x122881e70>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class QTable:\n",
    "    \"\"\"Simple Q-table.\"\"\"\n",
    "\n",
    "    def __init__(self, state_size, action_size):\n",
    "        \"\"\"Initialize Q-table.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        state_size : tuple\n",
    "            Number of discrete values along each dimension of state space.\n",
    "        action_size : int\n",
    "            Number of discrete actions in action space.\n",
    "        \"\"\"\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "\n",
    "        # Create Q-table, initialize all Q-values to zero\n",
    "        # Note: If state_size = (9, 9), action_size = 2, q_table.shape should be (9, 9, 2)\n",
    "        self.Q = np.zeros((*state_size, action_size))\n",
    "        print(f\"Q table size = {self.Q.shape}\")\n",
    "\n",
    "QTable((9, 9), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create 3 tiling grids based on:\n",
      " [((10, 10), (-0.066, -0.33)), ((10, 10), (0.0, 0.0)), ((10, 10), (0.066, 0.33))]\n",
      "Q table size = (10, 10, 2)\n",
      "Q table size = (10, 10, 2)\n",
      "Q table size = (10, 10, 2)\n",
      "TiledQTable(): no. of internal tables =  3\n",
      "[GET]    Q((0.25, -1.9), 0) = 0.0\n",
      "[UPDATE] Q((0.25, -1.9), 0) = 1.0\n",
      "[GET]    Q((0.15, -1.75), 0) = 0.06666666666666667\n"
     ]
    }
   ],
   "source": [
    "class TiledQTable:\n",
    "    \"\"\"Composite Q-table with an internal tile coding scheme.\"\"\"\n",
    "    \n",
    "    def __init__(self, low, high, tiling_specs, action_size):\n",
    "        \"\"\"Create tilings and initialize internal Q-table(s).\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        low : array_like\n",
    "            Lower bounds for each dimension of state space.\n",
    "        high : array_like\n",
    "            Upper bounds for each dimension of state space.\n",
    "        tiling_specs : list of tuples\n",
    "            A sequence of (bins, offsets) to be passed to create_tilings() along with low, high.\n",
    "        action_size : int\n",
    "            Number of discrete actions in action space.\n",
    "        \"\"\"\n",
    "        self.tilings_grid = create_tilings(low, high, tiling_specs)\n",
    "        self.state_sizes = [tuple(len(splits)+1 for splits in grid) \n",
    "                            for grid in self.tilings_grid]\n",
    "        self.action_size = action_size\n",
    "        self.q_tables = [QTable(state_size, self.action_size) for state_size in self.state_sizes]\n",
    "        print(\"TiledQTable(): no. of internal tables = \", len(self.q_tables))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.q_tables)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.q_tables[index].Q\n",
    "    \n",
    "    def get(self, state, action):\n",
    "        \"\"\"Get Q-value for given <state, action> pair.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        state : array_like\n",
    "            Vector representing the state in the original continuous space.\n",
    "        action : int\n",
    "            Index of desired action.\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        value : float\n",
    "            Q-value of given <state, action> pair, averaged from all internal Q-tables.\n",
    "        \"\"\"\n",
    "        # Encode state to get tile indices\n",
    "        states = tile_encode(state, self.tilings_grid)\n",
    "\n",
    "        q = 0.\n",
    "        for i, state in enumerate(states):\n",
    "            # Retrieve q-value for each tiling, and return their average\n",
    "            q += self.q_tables[i].Q[(*state, action)]\n",
    "        return q / len(self.q_tables)\n",
    "\n",
    "    def update(self, state, action, value, alpha=0.1):\n",
    "        \"\"\"Soft-update Q-value for given <state, action> pair to value.\n",
    "        \n",
    "        Instead of overwriting Q(state, action) with value, perform soft-update:\n",
    "            Q(state, action) = alpha * value + (1.0 - alpha) * Q(state, action)\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        state : array_like\n",
    "            Vector representing the state in the original continuous space.\n",
    "        action : int\n",
    "            Index of desired action.\n",
    "        value : float\n",
    "            Desired Q-value for <state, action> pair.\n",
    "        alpha : float\n",
    "            Update factor to perform soft-update, in [0.0, 1.0] range.\n",
    "        \"\"\"\n",
    "        # Encode state to get tile indices\n",
    "        states = tile_encode(state, self.tilings_grid)\n",
    "        \n",
    "        # Update q-value for each tiling by update factor alpha\n",
    "        for i, state in enumerate(states):\n",
    "            curr_q = self.q_tables[i].Q[(*state, action)]\n",
    "            self.q_tables[i].Q[(*state, action)] = curr_q + alpha * (value - curr_q)\n",
    "\n",
    "        \n",
    "print(f\"Create {len(tiling_specs)} tiling grids based on:\\n {tiling_specs}\")\n",
    "# Test with a sample Q-table\n",
    "Qs = TiledQTable(low, high, tiling_specs, 2)\n",
    "s1 = 3; s2 = 4; a = 0; q = 1.0\n",
    "# check value at sample = s1, state = samples[s1], action = a\n",
    "print(\"[GET]    Q({}, {}) = {}\".format(samples[s1], a, Qs.get(samples[s1], a)))\n",
    "# update value for sample with some common tile(s)\n",
    "print(\"[UPDATE] Q({}, {}) = {}\".format(samples[s1], a, q)); Qs.update(samples[s1], a, q)\n",
    "# check value again, should be slightly updated\n",
    "print(\"[GET]    Q({}, {}) = {}\".format(samples[s2], a, Qs.get(samples[s2], a)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you update the q-value for a particular state (say, `(0.25, -1.91)`) and action (say, `0`), then you should notice the q-value of a nearby state (e.g. `(0.15, -1.75)` and same action) has changed as well! This is how tile-coding is able to generalize values across the state space better than a single uniform grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Q-Learning (or SARSA Max) using Tile-Coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q table size = (5, 5, 5, 5, 5, 5, 3)\n",
      "Q table size = (5, 5, 5, 5, 5, 5, 3)\n",
      "Q table size = (5, 5, 5, 5, 5, 5, 3)\n",
      "TiledQTable(): no. of internal tables =  3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3 tiles, each tile:\n",
       "State space size: (5, 5, 5, 5, 5, 5)\n",
       "Action space size: 3"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class QLearningAgent:\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        env,\n",
    "        tiling_specs,\n",
    "        epsilon=1.0,\n",
    "        alpha=0.02,\n",
    "        gamma=0.99,\n",
    "        eps_decay=0.9995,\n",
    "        eps_min=0.01,\n",
    "        seed=505,\n",
    "    ):\n",
    "        \"\"\"Initialize agent.\n",
    "\n",
    "        Params\n",
    "        ======\n",
    "        - state_grid: discretized state space\n",
    "        - nA: number of actions available to the agent\n",
    "\n",
    "        \"\"\"\n",
    "        self.seed = np.random.seed(seed)\n",
    "\n",
    "        low = env.observation_space.low\n",
    "        high = env.observation_space.high\n",
    "        self.nA = env.action_space.n\n",
    "\n",
    "        # Create Q-table\n",
    "        self.Qs = TiledQTable(low, high, tiling_specs, self.nA)\n",
    "\n",
    "        # Learning parameters\n",
    "        self.alpha = alpha  # learning rate\n",
    "        self.gamma = gamma  # discount factor\n",
    "        self.epsilon = epsilon  # initial exploration rate\n",
    "        self.eps_decay = eps_decay  # how quickly should we decrease epsilon\n",
    "        self.eps_min = eps_min\n",
    "\n",
    "    def __repr__(self):\n",
    "        return (\n",
    "            f\"{len(self.Qs)} tiles, each tile:\\n\"\n",
    "            f\"State space size: {self.Qs[0].shape[:-1]}\\n\"\n",
    "            f\"Action space size: {self.nA}\"\n",
    "        )\n",
    "\n",
    "    def select_action(self, state):\n",
    "        \"\"\"Given the state, select an action.\n",
    "\n",
    "        Params\n",
    "        ======\n",
    "        - state: the current state of the environment\n",
    "\n",
    "        Returns\n",
    "        =======\n",
    "        - action: an integer, compatible with the task's action space\n",
    "        \"\"\"\n",
    "        self.epsilon *= self.eps_decay\n",
    "        self.epsilon = max(self.epsilon, self.eps_min)\n",
    "\n",
    "        do_exploration = np.random.random() < self.epsilon\n",
    "        if do_exploration:\n",
    "            return np.random.choice(np.arange(self.nA))\n",
    "        else:\n",
    "            return np.argmax(self.Qs.get(state, None))\n",
    "\n",
    "    def step(self, state, action, reward, next_state, done, mode=\"train\"):\n",
    "        \"\"\"Update the agent's knowledge, using the most recently sampled tuple.\n",
    "\n",
    "        Params\n",
    "        ======\n",
    "        - state: the previous state of the environment\n",
    "        - action: the agent's previous choice of action\n",
    "        - reward: last reward received\n",
    "        - next_state: the current state of the environment\n",
    "        - done: whether the episode is complete (True or False)\n",
    "        \"\"\"\n",
    "        if mode == \"test\":\n",
    "            # Test mode: Simply produce an action\n",
    "            return np.argmax(self.Qs.get(state, None))\n",
    "        else:\n",
    "            if done:\n",
    "                self.Qs.update(state, action, reward, self.alpha)\n",
    "            else:\n",
    "                G_estimate = reward + self.gamma * np.max(self.Qs.get(next_state, None))\n",
    "                self.Qs.update(state, action, G_estimate, self.alpha)\n",
    "\n",
    "\n",
    "def interact(env, agent, num_episodes=20000, window=100, mode=\"train\"):\n",
    "    \"\"\"Monitor agent's performance.\n",
    "\n",
    "    Params\n",
    "    ======\n",
    "    - env: instance of OpenAI Gym's(maintained by gymnasium) mountain car environment\n",
    "    - agent: instance of class Agent (see Agent.py for details)\n",
    "    - num_episodes: number of episodes of agent-environment interaction\n",
    "    - window: number of episodes to consider when calculating average rewards\n",
    "\n",
    "    Returns\n",
    "    =======\n",
    "    - scores: list containing average rewards\n",
    "    - max_avg_score: largest value in the scores list\n",
    "    \"\"\"\n",
    "\n",
    "    # initialize best average reward\n",
    "    max_avg_score = -np.inf\n",
    "\n",
    "    # initialize monitor for most recent rewards\n",
    "    scores = []\n",
    "\n",
    "    # for each episode\n",
    "    for i_episode in range(1, num_episodes + 1):\n",
    "        # begin the episode\n",
    "        state, _ = env.reset()\n",
    "\n",
    "        # initialize the sampled reward\n",
    "        total_reward = 0\n",
    "        while True:\n",
    "            # agent selects an action\n",
    "            action = agent.select_action(state)\n",
    "\n",
    "            # agent performs the selected action\n",
    "            next_state, reward, done, _, _ = env.step(action)\n",
    "\n",
    "            # agent performs internal updates based on sampled experience\n",
    "            agent.step(state, action, reward, next_state, done)\n",
    "\n",
    "            # update the sampled reward\n",
    "            total_reward += reward\n",
    "\n",
    "            # update the state (s <- s') to next time step\n",
    "            state = next_state\n",
    "\n",
    "            if done:\n",
    "                # save final sampled reward\n",
    "                scores.append(total_reward)\n",
    "                break\n",
    "\n",
    "        if mode == \"train\":\n",
    "            # Print episode stats\n",
    "            if len(scores) > window:\n",
    "                avg_score = np.mean(scores[-window:])\n",
    "                if avg_score > max_avg_score:\n",
    "                    max_avg_score = avg_score\n",
    "\n",
    "            if i_episode % window == 0:\n",
    "                print(\n",
    "                    \"\\rEpisode {}/{} | Max Average Score: {}\".format(\n",
    "                        i_episode, num_episodes, max_avg_score\n",
    "                    ),\n",
    "                    end=\"\",\n",
    "                )\n",
    "                sys.stdout.flush()\n",
    "\n",
    "            if i_episode == num_episodes:\n",
    "                print(\"\\n\")\n",
    "    return scores\n",
    "\n",
    "\n",
    "n_bins = 5\n",
    "bins = tuple([n_bins]*env.observation_space.shape[0])\n",
    "offset_pos = (env.observation_space.high - env.observation_space.low)/(3*n_bins)\n",
    "\n",
    "tiling_specs = [(bins, -offset_pos),\n",
    "                (bins, tuple([0.0]*env.observation_space.shape[0])),\n",
    "                (bins, offset_pos)]\n",
    "\n",
    "agent = QLearningAgent(\n",
    "    env,\n",
    "    tiling_specs,\n",
    "    epsilon=1.0,\n",
    "    alpha=0.02,\n",
    "    gamma=0.99,\n",
    "    eps_decay=0.9995,\n",
    "    eps_min=0.01,\n",
    "    seed=505,\n",
    ")\n",
    "agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 20000/20000 | Max Average Score: -211.92\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scores = interact(env, agent, num_episodes=20000, window=100, mode='train')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Learning curve\n",
    "\n",
    "The best way to analyze if agent was learning the task is to plot the scores. It should generally increase as the agent goes through more episodes.\n",
    "\n",
    "If scores are noisy, it might be difficult to tell whether agent is actually learning. To find the underlying trend, we also want to plot a rolling mean of the scores.\n",
    "\n",
    "As below figure showed, the mean episode scores go up over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 572
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plot_utils import plot_scores\n",
    "rolling_mean = plot_scores(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing\n",
    "\n",
    "Next, we freeze the agent and run the agent in test mode to see how well it performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Completed on 100 episodes with avg. score = -279.49\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 563
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run in test mode and analyze scores obtained\n",
    "test_scores = interact(env, agent, num_episodes=100, mode=\"test\")\n",
    "print(\n",
    "    f\"Test Completed on {len(test_scores)} episodes with avg. score = {np.mean(test_scores)}\"\n",
    ")\n",
    "_ = plot_scores(test_scores, rolling_window=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Watch a Smart Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=500x500>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score: -328.0\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "env = gym.make('Acrobot-v1', render_mode=\"rgb_array\")\n",
    "\n",
    "state, info = env.reset(seed=42)\n",
    "score = 0\n",
    "for t in range(500):\n",
    "    # clear frame\n",
    "    display.clear_output(wait=True)\n",
    "\n",
    "    action = agent.step(state, None, None, None, None, mode='test')\n",
    "\n",
    "    # render game\n",
    "    frame = env.render()\n",
    "    display.display(Image.fromarray(frame))\n",
    "    time.sleep(0.05)\n",
    "\n",
    "    state, reward, done, _, _ = env.step(action)\n",
    "    score += reward\n",
    "    if done:\n",
    "        break\n",
    "\n",
    "print('Final score:', score)\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Acrobot is a two-link pendulum system with one fixed joint and one actuated joint. The training objective is to control the torque applied at the actuated joint to swing the system such that the end of the second link reaches or exceeds the target height (above the horizontal line). This agent using Q-learning with tile coding succucessfully solve nonlinear dynamic problems by leveraging momentum and coordination between the links."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion: from traditional RL to Deep RL\n",
    "\n",
    "Yes, we can definitely use tile-coding or uniform discretization methods to convert continuous space into a discrete one, but these methods have some limitations. For example:\n",
    "\n",
    "- **Curse of Dimensionality**: As the dimensionality of the state space increases, the number of discrete states grows exponentially, making it computationally expensive to store and update the value function.\n",
    "- **Loss of Precision**: Discretization can lead to a loss of information about the underlying continuous dynamics, which may result in suboptimal policies, especially when the resolution of discretization is coarse.\n",
    "- **Fixed Representation**: Once the discretization granularity is chosen, it cannot adapt dynamically to the complexity of different regions in the state space, leading to either inefficiencies or insufficient detail.\n",
    "\n",
    "#### Parameterized Function Approximation\n",
    "To overcome these limitations, instead of discretizing the space, we can use a parameterized function to approximate the true value function:\n",
    "\n",
    "$$\n",
    "\\hat{v}(s, w) \\approx v_\\pi(s)\n",
    "$$\n",
    "$$\n",
    "\\hat{q}(s, a, w) \\approx q_\\pi(s, a)\n",
    "$$\n",
    "\n",
    "Because, for a problem domain with continuous state space $ s \\in S = \\mathbb{R}^n $, we aim to represent the value function $ v_\\pi(s) $ (for prediction) or $ q_\\pi(s, a) $ (for control).\n",
    "\n",
    "#### Feature Vectors\n",
    "A common intermediate step involves computing a feature vector $ x(s) $ that represents the state in a way suitable for function approximation. This allows the parameterized function to generalize across the continuous state space, enabling efficient learning and better scalability to high-dimensional problems.\n",
    "\n",
    "Using this method of extending linear function approximation helps us capture the non-linear relationships between input states and output values. The core idea is achieved through **feature transformation**.\n",
    "\n",
    "Feature transformation is a process that converts a state $ s $, or a state-action pair $ (s, a) $, into a feature vector $ x(s) $.  \n",
    "\n",
    "Each element of the feature vector can be generated by an individual function, which may be non-linear.\n",
    "\n",
    "for example, assume the state $ s $ is a real number. We can define a set of non-linear feature functions:\n",
    "\n",
    "$$\n",
    "x_1(s) = s\n",
    "$$\n",
    "$$\n",
    "x_2(s) = s^2\n",
    "$$\n",
    "$$\n",
    "x_3(s) = s^3\n",
    "$$\n",
    "$$\n",
    "\\text{and so on...}\n",
    "$$\n",
    "\n",
    "Radial Basis Functions are a common type of kernel function:\n",
    "\n",
    "- The current state $ s $ is treated as a point in the continuous state space.\n",
    "- Each basis function corresponds to a \"fuzzy region\" (or \"blob\"), centered around a specific point in the state space.\n",
    "- The closer the state is to the center of the basis function, the higher the response value. As the distance increases, the response value gradually decreases.\n",
    "\n",
    "which are typically defined using a Gaussian kernel\n",
    "\n",
    "$$\n",
    "\\phi(s) = \\exp\\left(-\\frac{\\|s - \\mu\\|^2}{2\\sigma^2}\\right)\n",
    "$$\n",
    "\n",
    "Where:\n",
    "- $ \\mu $ is the center of the basis function.\n",
    "- $ \\sigma $ determines how quickly the response value decays as the distance increases.\n",
    "\n",
    "\n",
    "Given a state $ s $, we can use multiple radial basis functions to generate a feature vector:\n",
    "\n",
    "$$\n",
    "x(s) = [\\phi_1(s), \\phi_2(s), \\dots, \\phi_k(s)]\n",
    "$$\n",
    "\n",
    "Although non-linear feature transformations are used, the resulting value function is still a linear combination of these features:\n",
    "\n",
    "$$\n",
    "\\hat{v}(s, w) = w_1 x_1(s) + w_2 x_2(s) + w_3 x_3(s) + \\dots\n",
    "$$\n",
    "\n",
    "This means that we can still use linear function approximation methods(see below) while capturing non-linear relationships through feature transformations.\n",
    "\n",
    "But, in this situation, we still use a linear function approximation for $\\hat{v}(s, w)$, what if we completly using non-linear behavior of input $x(s)$ and $\\hat{v}(s, w)$, yes, we can pass linear response (dot product of weights and features) through a non-linear activation function. This approach forms the foundation of artificial neural networks, where the activation function significantly enhances the representational power of the approximator.\n",
    "\n",
    "#### Learning Parameters $ w $\n",
    "\n",
    "Our objective is then to determine a set of parameters $ w $ that produces an optimal value function. This can be accomplished using the general reinforcement learning framework, employing either Monte Carlo or Temporal-Difference (TD) methods. The update mechanism is adjusted based on the chosen parameterized function:\n",
    "\n",
    "1. **Monte Carlo Methods**:  \n",
    "   - Use the complete trajectory's return $ G_t $ to update the parameters.\n",
    "   - Often works well but can be slow due to high variance and the need to wait until the end of an episode.\n",
    "\n",
    "2. **Temporal-Difference (TD) Methods**:  \n",
    "   - Use the TD target $ r + \\gamma \\hat{v}(s', w) $ as an estimate of $ v_\\pi(s) $ to update the parameters.\n",
    "   - More efficient and widely used in practice, as it updates parameters incrementally without waiting for the episode to finish.\n",
    "\n",
    "The update rule for $ w $ is generally of the form:\n",
    "$$\n",
    "w \\leftarrow w + \\alpha \\delta x(s)\n",
    "$$\n",
    "where:\n",
    "- $ \\delta $ is the TD error:  \n",
    "  $$\n",
    "  \\delta = r + \\gamma \\hat{v}(s', w) - \\hat{v}(s, w)\n",
    "  $$\n",
    "- $ x(s) $ is the feature vector for the state $ s $.\n",
    "\n",
    "#### Applications in Continuous Action Spaces\n",
    "\n",
    "In problems with **continuous action spaces**, parameterized functions are also used to approximate the policy directly. For example:\n",
    "- Instead of choosing the best action $ a^* $ by iterating over all possible actions (as in discrete action spaces), the model directly outputs the optimal action $ a^* $ using a parameterized policy $ \\pi_\\theta(s) $:\n",
    "  $$\n",
    "  a^* = \\pi_\\theta(s)\n",
    "  $$\n",
    "- This is particularly useful in tasks like robotics and autonomous driving, where actions such as steering angle or throttle are continuous variables.\n"
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