Fine-Tuning the Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/yue-zhongqi/cartpole_colab/blob/main/cartpole.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZauhjPSfX7pI"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UBiYOoesYMvr"
   },
   "source": [
    "## Installing dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PbgnVwZmX5uW",
    "outputId": "7dcfe5ac-b419-4d5d-8305-1ba3c1240df3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: setuptools in /Users/nguyentuan/anaconda3/lib/python3.11/site-packages (69.2.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install gym pyvirtualdisplay > /dev/null 2>&1\n",
    "!pip install gym pyvirtualdisplay > /dev/null 2>&1\n",
    "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n",
    "!pip install \"gymnasium[classic_control]\" > /dev/null 2>&1\n",
    "!apt-get update > /dev/null 2>&1\n",
    "!apt-get install cmake > /dev/null 2>&1\n",
    "!pip install --upgrade setuptools 2>&1\n",
    "!pip install ez_setup > /dev/null 2>&1\n",
    "\n",
    "# Added dependency\n",
    "!pip install tensorflow > /dev/null 2>&1\n",
    "!pip install imageio > /dev/null 2>&1\n",
    "!pip install PIL > /dev/null 2>&1\n",
    "!pip install opencv-python > /dev/null 2>&1\n",
    "!pip instal moviepy > /dev/null 2>&1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start testing from here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries and dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from collections import deque, namedtuple\n",
    "import random\n",
    "\n",
    "import gymnasium as gym\n",
    "from   gym          import logger as gymlogger\n",
    "from   gym.wrappers import RecordVideo\n",
    "gymlogger.set_level(40) #error only\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from tensorflow.keras.losses import MSE # Mean Square Error\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "from tensorflow.keras.losses import Huber\n",
    "\n",
    "\n",
    "\n",
    "from tensorflow.keras.optimizers.legacy import Adam\n",
    "\n",
    "\n",
    "# This library is defined to generate and display the result video\n",
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make(\"CartPole-v1\", render_mode=\"rgb_array\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define some Constants and Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MEMORY_SIZE = 1_000_000     # size of memory buffer\n",
    "GAMMA = 0.99              # discount factor\n",
    "ALPHA = 1e-4              # learning rate  \n",
    "NUM_STEPS_FOR_UPDATE = 20  # perform a learning update every C time steps\n",
    "\n",
    "STATE_SIZE = env.observation_space.shape  # The model can return 4 state attributes\n",
    "NUM_OF_ACTION = env.action_space.n        # the model can only use 2 action push_left(0), push_right(1)\n",
    "\n",
    "MINIBATCH_SIZE = 10  # Mini-batch size.\n",
    "TAU = 0.005          # Soft update parameter.\n",
    "E_DECAY = 0.9995     # ε-decay rate for the ε-greedy policy.\n",
    "E_MIN = 0.047        # Minimum ε value for the ε-greedy policy.\n",
    "SEED = 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some notes about the model\n",
    "Reference: <a href = https://gymnasium.farama.org/environments/classic_control/cart_pole/> Cart Pole documentation</a>\n",
    "\n",
    "### Actions:\n",
    "- Variable type: `ndarray (1,)`\n",
    "```python\n",
    "PUSH_LEFT  = 0\n",
    "PUSH_RIGHT = 1\n",
    "```\n",
    "### States (observations):\n",
    "- Variable type: `ndarray (4,)`<br>\n",
    "\n",
    "| Index | Observation | Min | Max |\n",
    "|:-------:|:-------------:|:-----:|:-----:|\n",
    "|0|Cart Position|-4.8|4.8|\n",
    "|1|Cart Velocity|-Inf|Inf|\n",
    "|2|Pole Angle|-0.418|0.418|\n",
    "|3|Pole Angular Velocity|-Inf|Inf|\n",
    "\n",
    "### Reward:\n",
    "`+1` for every step (including terminal state)\n",
    "\n",
    "### Episode end:\n",
    "- Termination: Pole Angle outside of $\\pm 12^{\\circ}$\n",
    "- Termination: Cart Position outside of $\\pm 2.4$\n",
    "- Truncation: Episole length > 500\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep-Q learning\n",
    "\n",
    "- In the lecture, we apply Q-Learning by creating a `State, Action` Table to capture the `Q(s, a)` and since we are in a continuous environment it is quite difficult to capture all the states in the same table.<br>\n",
    "- Therefore, it is better to use the weights in the Deep Neural Network (DNN) to capture all that information. <br>\n",
    "\n",
    "\n",
    "Moreover, it could behave like a Q-value table, since you can get Q(s, a) by just calling \n",
    "\n",
    "```python\n",
    "q_network(state)[a]\n",
    "``` \n",
    "and update it by training the neural network through loss function (Mean Square Error) to approximate the true value which behave just like what describe in the lecture.<br>\n",
    "- The true value:\n",
    "$$y=R(s, a)+\\gamma max_a(Q_{network}(s', a'))$$\n",
    "- The prediction:\n",
    "$$\\hat{y} = Q_{network}(s)$$\n",
    "\n",
    "### The Q-network problem\n",
    "\n",
    "One action `a` of the agent may lead to many possible $y$ since `Q_network` is involve in the calculation of $y$, which could make the model become unstable in the training process. So we will need another DNN (with identical structure to `Q_network`) to return the `Q(s', a')` called `target_Q_network`. i.e.\n",
    "\n",
    "$$y=R(s,a)+\\gamma max_a(TargetQ_{network}(s',a'))$$\n",
    "and\n",
    "$$\\hat{y} = Q_{network}(s)$$\n",
    "\n",
    "The `target_Q_network` and `Q_network` will be updated (trained) after a certain amount of steps.\n",
    "- `Q_network`'s weights $w$ will still be updated using the loss computed from $y$ and $\\hat{y}$ with learning rate $\\alpha$.\n",
    "- `Target_Q_network`'s weights $w^-$ will be update using soft update technique where it keep most of the part from the last experience and a small update based on the actual change of $w$. It will create a smoother learning curve and become more stable in the long run.\n",
    "\n",
    "$$\n",
    "w^- \\leftarrow \\tau w + (1-\\tau)w^-\n",
    "$$\n",
    "where $\\tau << 1$\n",
    "\n",
    "\n",
    "We will define each layer of both DNN:\n",
    "- `INPUT`: no. of units = `STATE_SIZE`\n",
    "- `DENSE`: 64 units of 'relu' activate function\n",
    "- `DENSE`: 64 units of 'relu' activate function\n",
    "- `DENSE`: output layers with no. of units = `NUM_OF_ACTION` with 'linear' activate function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q_network and Target_Q_network stucture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_network = Sequential([\n",
    "    Input(shape = STATE_SIZE),\n",
    "    Dense(units = 128, activation = 'relu'),\n",
    "    Dense(units = 256, activation = 'relu'),\n",
    "    Dense(units = NUM_OF_ACTION, activation = 'linear')\n",
    "])\n",
    "\n",
    "target_q_network = Sequential([\n",
    "    Input(shape = STATE_SIZE),\n",
    "    Dense(units = 128, activation = 'relu'),\n",
    "    Dense(units = 256, activation = 'relu'),\n",
    "    Dense(units = NUM_OF_ACTION, activation = 'linear')\n",
    "])\n",
    "\n",
    "# Optimizer will update the learning rate appropriately to help speed up the training process\n",
    "optimizer = Adam(learning_rate=ALPHA) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft update function for Target_Q_Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_target_network(q_network, target_q_network):\n",
    "\n",
    "    for target_weights, q_net_weights in zip(\n",
    "        target_q_network.weights, q_network.weights\n",
    "    ):\n",
    "        target_weights.assign(TAU * q_net_weights + (1.0 - TAU) * target_weights)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experience Relay\n",
    "\n",
    "In Deep-Q Learning, when we record the experience in the sequence it will create a lot of correlation between the consecutive states which may create bias in our model.<br>\n",
    "To account for this, we will create a collection of experience and develop a function to sample the data randomly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random.seed(SEED)\n",
    "def get_experiences(memory_buffer):\n",
    "    \n",
    "    experiences = random.sample(memory_buffer, k = MINIBATCH_SIZE)\n",
    "    states = tf.convert_to_tensor(\n",
    "        np.array([e.state for e in experiences if e is not None]), dtype=tf.float32\n",
    "    )\n",
    "    actions = tf.convert_to_tensor(\n",
    "        np.array([e.action for e in experiences if e is not None]), dtype=tf.float32\n",
    "    )\n",
    "    rewards = tf.convert_to_tensor(\n",
    "        np.array([e.reward for e in experiences if e is not None]), dtype=tf.float32\n",
    "    )\n",
    "    next_states = tf.convert_to_tensor(\n",
    "        np.array([e.next_state for e in experiences if e is not None]), dtype=tf.float32\n",
    "    )\n",
    "    done_vals = tf.convert_to_tensor(\n",
    "        np.array([e.done for e in experiences if e is not None]).astype(np.uint8),\n",
    "        dtype=tf.float32,\n",
    "    )\n",
    "    return (states, actions, rewards, next_states, done_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_loss(experiences, gamma, q_network, target_q_network):\n",
    "\n",
    "\n",
    "    # Unpack the mini-batch of experience tuples\n",
    "    states, actions, rewards, next_states, done_vals = experiences\n",
    "    \n",
    "    # Compute max Q^(s,a)\n",
    "    max_qsa = tf.reduce_max(target_q_network(next_states), axis=-1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Set y = R if episode terminates, otherwise set y = R + γ max Q^(s,a).\n",
    "    y_targets = rewards + (1 - done_vals) * (gamma * max_qsa)\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    # Get the q_values and reshape to match y_targets\n",
    "    q_values = q_network(states)\n",
    "    q_values = tf.gather_nd(q_values, tf.stack([tf.range(q_values.shape[0]),\n",
    "                                                tf.cast(actions, tf.int32)], axis=1))\n",
    "        \n",
    "    # Compute the loss\n",
    "    loss = MSE(y_targets, q_values)\n",
    "\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a function to update the weight of both network model\n",
    "\n",
    "This is the stage where the 2 models update their weight (train the weight) accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def agent_learn(experiences, gamma):\n",
    "    \n",
    "    # Calculate the loss\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss = compute_loss(experiences, gamma, q_network, target_q_network)\n",
    "\n",
    "    # Get the gradients of the loss with respect to the weights.\n",
    "    gradients = tape.gradient(loss, q_network.trainable_variables)\n",
    "    \n",
    "    # Update the weights of the q_network.\n",
    "    optimizer.apply_gradients(zip(gradients, q_network.trainable_variables))\n",
    "\n",
    "    # update the weights of target q_network\n",
    "    update_target_network(q_network, target_q_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check update function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_update_conditions(t, num_steps_upd, memory_buffer):\n",
    "\n",
    "    if len(memory_buffer) > MINIBATCH_SIZE:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\varepsilon$-greedy action choice\n",
    "\n",
    "- We will defined the $\\varepsilon$ at the beginning of the training stage<br>\n",
    "- We will take action $a^{*}$ which could maximize the $Q(s, a)$ if the probability is greater than $\\varepsilon$, else we will choose the action at random, because there could be the case where random action could be better\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_action(q_values, epsilon = 0.0):\n",
    "\n",
    "    if random.random() > epsilon:\n",
    "        return np.argmax(q_values.numpy()[0])\n",
    "    else:\n",
    "        return random.choice(np.arange(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, overtime the agent (model) have learned and improve a lot so we have to decay the $\\varepsilon$ value with rate `E_DECAY`, i.e. the model will take action based on what it has learned more frequently. We also put a lower threshold of $\\varepsilon$ (`E_MIN`) to allow the model the chance to do random action sometime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_new_eps(epsilon):\n",
    "    \n",
    "    return max(E_MIN, E_DECAY * epsilon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training step\n",
    "\n",
    "After the training time when we obtain the average value > 200, we consider our model is good to deploy and we deploy our trained parameters into .h5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_q_network.load_weights('cart_pole_model_temp.h5')\n",
    "q_network.load_weights('cart_pole_model_temp.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 19 | Total point average of the last 100 episodes: 402.26\n",
      "\n",
      "Environment solved in 19 episodes!\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nguyentuan/anaconda3/lib/python3.11/site-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "num_episodes = 10_000_000\n",
    "max_num_timesteps = 500\n",
    "\n",
    "total_point_history = []\n",
    "\n",
    "num_p_av = 100    # number of total points to use for averaging\n",
    "epsilon = 0.047     # initial ε value for ε-greedy policy\n",
    "\n",
    "# Create a memory buffer D with capacity N\n",
    "memory_buffer = deque(maxlen = MEMORY_SIZE)\n",
    "\n",
    "# Set the target network weights equal to the Q-Network weights\n",
    "target_q_network.set_weights(q_network.get_weights())\n",
    "\n",
    "for i in range(num_episodes):\n",
    "    \n",
    "    # Reset the environment to the initial state and get the initial state\n",
    "    state = env.reset()[0]\n",
    "    total_points = 0\n",
    "    \n",
    "    for t in range(max_num_timesteps):\n",
    "        \n",
    "        # From the current state S choose an action A using an ε-greedy policy\n",
    "        state_qn = np.expand_dims(state, axis = 0)  # state needs to be the right shape for the q_network\n",
    "        q_values = q_network(state_qn)\n",
    "        action = get_action(q_values, epsilon)\n",
    "        \n",
    "        # Take action A and receive reward R and the next state S'\n",
    "        next_state, reward, done, truncated, info = env.step(action)\n",
    "        \n",
    "        # Store experience tuple (S,A,R,S') in the memory buffer.\n",
    "        # We store the done variable as well for convenience.\n",
    "        memory_buffer.append(experience(state, action, reward, next_state, done))\n",
    "        \n",
    "        \n",
    "        # Only update the network every NUM_STEPS_FOR_UPDATE time steps.\n",
    "        update = check_update_conditions(t, NUM_STEPS_FOR_UPDATE, memory_buffer)\n",
    "        \n",
    "\n",
    "        if update:\n",
    "            # Sample random mini-batch of experience tuples (S,A,R,S') from D\n",
    "            experiences = get_experiences(memory_buffer)\n",
    "            \n",
    "            # Set the y targets, perform a gradient descent step,\n",
    "            # and update the network weights.\n",
    "            agent_learn(experiences, GAMMA)\n",
    "        \n",
    "        state = next_state.copy()\n",
    "        \n",
    "        \n",
    "        #total_points += reward\n",
    "        total_points += 1\n",
    "        \n",
    "        \n",
    "        if done:\n",
    "            break\n",
    "            \n",
    "\n",
    "            \n",
    "    total_point_history.append(total_points)\n",
    "    av_latest_points = np.mean(total_point_history[-num_p_av:])\n",
    "    \n",
    "\n",
    "    # Update the ε value\n",
    "    epsilon = get_new_eps(epsilon)\n",
    "\n",
    "    print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\", end=\"\")\n",
    "\n",
    "    if (i+1) % num_p_av == 0:\n",
    "        print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\")\n",
    "\n",
    "    # We will consider that the environment is solved if we get an\n",
    "    # average of 200 points in the last 100 episodes.\n",
    "    if av_latest_points >= 400.0:\n",
    "        print(f\"\\n\\nEnvironment solved in {i+1} episodes!\")\n",
    "        q_network.save('cart_pole_model_temp.h5')\n",
    "        break\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OpenCV: FFMPEG: tag 0x34363248/'H264' is not supported with codec id 27 and format 'mp4 / MP4 (MPEG-4 Part 14)'\n",
      "OpenCV: FFMPEG: fallback to use tag 0x31637661/'avc1'\n"
     ]
    }
   ],
   "source": [
    "utils.create_video(\"video/video.mp4\", env, q_network, test = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video alt=\"test\" autoplay \n",
       "                    loop controls style=\"height: 400px;\">\n",
       "                    <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
       "                 </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.show_video()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_q_network.load_weights('cart_pole_model_temp.h5')\n",
    "q_network.load_weights('cart_pole_model_temp.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Episode 1 | Total point average of the last 100 episodes: 500.00\n",
      "\n",
      "Environment solved in 1 episodes!\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "num_episodes = 10_000_000\n",
    "max_num_timesteps = 500\n",
    "\n",
    "total_point_history = []\n",
    "\n",
    "num_p_av = 100    # number of total points to use for averaging\n",
    "epsilon = 0.047     # initial ε value for ε-greedy policy\n",
    "\n",
    "# Create a memory buffer D with capacity N\n",
    "memory_buffer = deque(maxlen = MEMORY_SIZE)\n",
    "\n",
    "# Set the target network weights equal to the Q-Network weights\n",
    "target_q_network.set_weights(q_network.get_weights())\n",
    "\n",
    "for i in range(num_episodes):\n",
    "    \n",
    "    # Reset the environment to the initial state and get the initial state\n",
    "    state = env.reset()[0]\n",
    "    total_points = 0\n",
    "    \n",
    "    for t in range(max_num_timesteps):\n",
    "        \n",
    "        # From the current state S choose an action A using an ε-greedy policy\n",
    "        state_qn = np.expand_dims(state, axis = 0)  # state needs to be the right shape for the q_network\n",
    "        q_values = q_network(state_qn)\n",
    "        action = get_action(q_values, epsilon)\n",
    "        \n",
    "        # Take action A and receive reward R and the next state S'\n",
    "        next_state, reward, done, truncated, info = env.step(action)\n",
    "        \n",
    "        # Store experience tuple (S,A,R,S') in the memory buffer.\n",
    "        # We store the done variable as well for convenience.\n",
    "        memory_buffer.append(experience(state, action, reward, next_state, done))\n",
    "        \n",
    "        \n",
    "        # Only update the network every NUM_STEPS_FOR_UPDATE time steps.\n",
    "        update = check_update_conditions(t, NUM_STEPS_FOR_UPDATE, memory_buffer)\n",
    "        \n",
    "\n",
    "        if update:\n",
    "            # Sample random mini-batch of experience tuples (S,A,R,S') from D\n",
    "            experiences = get_experiences(memory_buffer)\n",
    "            \n",
    "            # Set the y targets, perform a gradient descent step,\n",
    "            # and update the network weights.\n",
    "            agent_learn(experiences, GAMMA)\n",
    "        \n",
    "        state = next_state.copy()\n",
    "        \n",
    "        \n",
    "        #total_points += reward\n",
    "        total_points += 1\n",
    "        \n",
    "        \n",
    "        if done:\n",
    "            break\n",
    "            \n",
    "\n",
    "            \n",
    "    total_point_history.append(total_points)\n",
    "    av_latest_points = np.mean(total_point_history[-num_p_av:])\n",
    "    \n",
    "\n",
    "    # Update the ε value\n",
    "    epsilon = get_new_eps(epsilon)\n",
    "\n",
    "    print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\", end=\"\")\n",
    "\n",
    "    if (i+1) % num_p_av == 0:\n",
    "        print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\")\n",
    "\n",
    "    # We will consider that the environment is solved if we get an\n",
    "    # average of 200 points in the last 100 episodes.\n",
    "    if av_latest_points >= 500.0:\n",
    "        print(f\"\\n\\nEnvironment solved in {i+1} episodes!\")\n",
    "        q_network.save('cart_pole_model_temp.h5')\n",
    "        break\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OpenCV: FFMPEG: tag 0x34363248/'H264' is not supported with codec id 27 and format 'mp4 / MP4 (MPEG-4 Part 14)'\n",
      "OpenCV: FFMPEG: fallback to use tag 0x31637661/'avc1'\n"
     ]
    }
   ],
   "source": [
    "utils.create_video(\"video/video.mp4\", env, q_network, test = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video alt=\"test\" autoplay \n",
       "                    loop controls style=\"height: 400px;\">\n",
       "                    <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
       "                 </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.show_video()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNczA+lJQOKjOo4XtzYjWQc",
   "include_colab_link": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}