{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 실습 08 · 달 착륙선 강화학습 (DQN)\n",
    "\n",
    "> **연계 강의자료:** M8 「강화학습과 LLM 정렬」 · 14주차\n",
    "> **목표:** stable-baselines3의 DQN으로 달 착륙선을 학습시키고, 신경망 층수(nn_layers)가 학습 속도(speed)와 안정성(stability)에 미치는 영향을 실험·분석한다.\n",
    ">\n",
    "> 학기 말 프로젝트. gymnasium 0.29 + SB3 2.3으로 버전을 고정했다(2026-08 동작 확인 구성). 학습 전/후 영상 비교가 관찰 포인트이며, 강의자료 M8 실습 1(그리드월드 Q-learning)의 신경망 확장판이다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "WcQqwxRPJhLJ"
   },
   "source": [
    "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n",
    "\n",
    "**By Neuromatch Academy**\n",
    "\n",
    "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n",
    "\n",
    "__Content editors:__ Shaonan Wang, Spiros Chavlis\n",
    "\n",
    "출처 : https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/projects/ReinforcementLearning/lunar_lander.ipynb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "EgQh7-UuJhLL"
   },
   "source": "---\n# Objective\n\nIn this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?\n\n이 프로젝트의 목표는 달 착륙선이라는 흥미로운 과제에서 딥 Q-러닝(Deep Q-Learning) 알고리즘의 성능을 분석하는 것입니다. 과제를 설명하기 전에, 여기서 말하는 '분석'과 '성능'이라는 두 키워드부터 짚어 보겠습니다. 강화학습(RL)의 맥락에서 이 두 키워드는 정확히 무엇을 뜻할까요?"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "mQh91deLJhLL"
   },
   "source": [
    "---\n",
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install swig\n",
    "!pip install gymnasium[box2d]==0.29.1\n",
    "!pip install stable_baselines3==2.3.2\n",
    "!pip install pyvirtualdisplay"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DNo0zFL2KCuV",
    "outputId": "86cb5dfb-03c5-4871-eb11-8079066e130b"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Collecting swig\n",
      "  Downloading swig-4.1.1.post1-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.8 MB)\n",
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      "\u001b[?25hInstalling collected packages: swig\n",
      "Successfully installed swig-4.1.1.post1\n",
      "Collecting gymnasium\n",
      "  Downloading gymnasium-0.29.1-py3-none-any.whl (953 kB)\n",
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      "  Downloading Farama_Notifications-0.0.4-py3-none-any.whl (2.5 kB)\n",
      "Installing collected packages: farama-notifications, gymnasium\n",
      "Successfully installed farama-notifications-0.0.4 gymnasium-0.29.1\n",
      "Collecting stable_baselines3\n",
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      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->stable_baselines3) (2023.3.post1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->stable_baselines3) (1.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13->stable_baselines3) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13->stable_baselines3) (1.3.0)\n",
      "Installing collected packages: stable_baselines3\n",
      "Successfully installed stable_baselines3-2.2.1\n",
      "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.1.2)\n",
      "Requirement already satisfied: install in /usr/local/lib/python3.10/dist-packages (1.3.5)\n",
      "Requirement already satisfied: gymnasium[box2d] in /usr/local/lib/python3.10/dist-packages (0.29.1)\n",
      "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (1.23.5)\n",
      "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (2.2.1)\n",
      "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (4.5.0)\n",
      "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (0.0.4)\n",
      "Collecting box2d-py==2.3.5 (from gymnasium[box2d])\n",
      "  Downloading box2d-py-2.3.5.tar.gz (374 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m374.4/374.4 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: pygame>=2.1.3 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (2.5.2)\n",
      "Requirement already satisfied: swig==4.* in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (4.1.1.post1)\n",
      "Building wheels for collected packages: box2d-py\n",
      "  Building wheel for box2d-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for box2d-py: filename=box2d_py-2.3.5-cp310-cp310-linux_x86_64.whl size=2373076 sha256=eaf90e5ee32dcd150d84cf226f47c70b91a48779cd9555cb1c5e56e4677b9f60\n",
      "  Stored in directory: /root/.cache/pip/wheels/db/8f/6a/eaaadf056fba10a98d986f6dce954e6201ba3126926fc5ad9e\n",
      "Successfully built box2d-py\n",
      "Installing collected packages: box2d-py\n",
      "Successfully installed box2d-py-2.3.5\n",
      "Collecting pyvirtualdisplay\n",
      "  Downloading PyVirtualDisplay-3.0-py3-none-any.whl (15 kB)\n",
      "Installing collected packages: pyvirtualdisplay\n",
      "Successfully installed pyvirtualdisplay-3.0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "q2Aur3BLJhLM"
   },
   "outputs": [],
   "source": [
    "# @title Update/Upgrade the system and install libs\n",
    "!apt-get update > /dev/null 2>&1\n",
    "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n",
    "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n",
    "!apt-get install x11-utils > /dev/null 2>&1\n",
    "!apt-get install xvfb > /dev/null 2>&1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "vADWDYggJhLM",
    "outputId": "7b1530a4-21ce-4aaa-ee8b-4749e44762b7",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "# @title Install dependencies\n",
    "!pip install rarfile --quiet\n",
    "!pip install stable-baselines3[extra]==2.3.2 --quiet\n",
    "!pip install ale-py --quiet\n",
    "!pip install gymnasium[box2d]==0.29.1 --quiet\n",
    "!pip install pyvirtualdisplay --quiet\n",
    "!pip install pyglet --quiet\n",
    "!pip install pygame --quiet\n",
    "!pip install minigrid --quiet\n",
    "!pip install -q swig --quiet\n",
    "!pip install -q gymnasium[box2d]==0.29.1 --quiet\n",
    "!pip install 'minigrid<=2.1.1' --quiet\n",
    "!pip3 install box2d-py --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "0_-oxehLJhLN",
    "outputId": "d12eab80-8051-4bf6-efe3-c7220c228e58",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/dtypes.py:35: DeprecationWarning: ml_dtypes.float8_e4m3b11 is deprecated. Use ml_dtypes.float8_e4m3b11fnuz\n",
      "  from tensorflow.tsl.python.lib.core import pywrap_ml_dtypes\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import io\n",
    "import os\n",
    "import glob\n",
    "import torch\n",
    "import base64\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys\n",
    "import gymnasium\n",
    "\n",
    "import stable_baselines3\n",
    "from stable_baselines3 import DQN\n",
    "from stable_baselines3.common.results_plotter import ts2xy, load_results\n",
    "from stable_baselines3.common.callbacks import EvalCallback\n",
    "from stable_baselines3.common.env_util import make_atari_env\n",
    "\n",
    "import gymnasium as gym\n",
    "from gymnasium import spaces\n",
    "from gymnasium.envs.box2d.lunar_lander import *\n",
    "from gymnasium.wrappers.monitoring.video_recorder import VideoRecorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "KajgmfzbJhLO"
   },
   "outputs": [],
   "source": [
    "# @title Play Video function\n",
    "from IPython.display import HTML\n",
    "from base64 import b64encode\n",
    "from pyvirtualdisplay import Display\n",
    "\n",
    "# create the directory to store the video(s)\n",
    "os.makedirs(\"./video\", exist_ok=True)\n",
    "\n",
    "display = Display(visible=False, size=(1400, 900))\n",
    "_ = display.start()\n",
    "\n",
    "\"\"\"\n",
    "Utility functions to enable video recording of gym environment\n",
    "and displaying it.\n",
    "To enable video, just do \"env = wrap_env(env)\"\"\n",
    "\"\"\"\n",
    "def render_mp4(videopath: str) -> str:\n",
    "  \"\"\"\n",
    "  Gets a string containing a b4-encoded version of the MP4 video\n",
    "  at the specified path.\n",
    "  \"\"\"\n",
    "  mp4 = open(videopath, 'rb').read()\n",
    "  base64_encoded_mp4 = b64encode(mp4).decode()\n",
    "  return f'<video width=400 controls><source src=\"data:video/mp4;' \\\n",
    "         f'base64,{base64_encoded_mp4}\" type=\"video/mp4\"></video>'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "YwJBRHdVJhLO"
   },
   "source": "---\n# Introduction\n\nIn a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n\n**Speed:** How fast the algorithm reaches the maximum possible reward.\n\n**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n\nIn this project, you should investigate the following question:\n\n**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n\nYou do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n\nNow, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n\nThe input state of the Lunar Lander consists of following components:\n\n표준적인 RL 환경에서 에이전트는 주어진 목표를 최대화하도록 피드백을 통해 환경으로부터 최적의 행동을 학습합니다. 에이전트가 최적의 행동을 학습하는 데 쓸 수 있는 알고리즘은 RL 분야에서 다양하게 제안되어 왔고, 그중 널리 쓰이는 것이 딥 Q-네트워크(DQN)입니다. 이 알고리즘은 심층 신경망을 사용하여 최적의 행동을 계산합니다. 이 프로젝트의 목표는 신경망 레이어 수가 알고리즘의 성능에 미치는 영향을 이해하는 것입니다. 알고리즘의 성능은 속도와 안정성이라는 두 가지 지표로 평가할 수 있습니다.\n\n속도: 알고리즘이 도달 가능한 최대 보상에 얼마나 빨리 도달하는가.\n\n안정성: 일부 응용(특히 온라인 학습이 관련된 경우)에서는 속도만큼이나 알고리즘의 안정성, 즉 성능의 변동이 작은 것도 중요합니다.\n\n이 프로젝트에서 여러분이 조사할 질문은 다음과 같습니다.\n\n신경망 레이어 수가 알고리즘의 속도와 안정성에 미치는 영향은 무엇인가?\n\nDQN 코드를 처음부터 작성할 필요는 없습니다. DQN 알고리즘의 기본 구현은 아래에 제공되어 있습니다. 여러분은 하이퍼파라미터(신경망 크기, 학습률 등)를 조정하고 성능을 관찰·분석하기만 하면 됩니다. 자세한 내용은 아래에서 이어집니다.\n\n이제 이번 프로젝트에서 다룰 RL 과제인 달 착륙선을 살펴보겠습니다. 이 과제는 착륙선과 두 개의 깃발로 표시된 착륙 패드로 구성됩니다. 에피소드는 착륙선이 중력에 이끌려 아래로 떨어지면서 시작됩니다. 목표는 착륙선에 달린 여러 엔진을 이용해 최대한 빠르고 연료를 아끼면서 착륙 패드에 속도 0으로 안전하게 착륙하는 것입니다. 화면 위쪽에서 내려와 착륙 패드에 속도 0으로 착륙하면 100점에서 140점 사이의 보상을 받습니다. 착륙 다리가 지면에 닿을 때마다 10점을 받습니다. 주 엔진을 점화하면 매 프레임 -0.3점, 보조 엔진을 점화하면 매 프레임 -0.03점을 받습니다. 착륙선이 추락하거나 완전히 멈추면 각각 -100점, +100점의 추가 보상이 주어지고, 이때 에피소드도 함께 종료됩니다.\n\n달 착륙선의 입력 상태는 다음과 같은 구성 요소로 이루어져 있습니다:\n\n\n  1. Horizontal Position\n  2. Vertical Position\n  3. Horizontal Velocity\n  4. Vertical Velocity\n  5. Angle\n  6. Angular Velocity\n  7. Left Leg Contact\n  8. Right Leg Contact\n\nThe actions of the agents are:\n  1. Do Nothing\n  2. Fire Main Engine\n  3. Fire Left Engine\n  4. Fire Right Engine\n\n\n<img src=\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content-dl/main/projects/static/lunar_lander.png\">"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "INVDFqDyJhLP"
   },
   "source": [
    "---\n",
    "# Basic DQN Implementation\n",
    "\n",
    "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "RenOxeiNJhLP"
   },
   "source": [
    "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "1GI5h_7uJhLQ"
   },
   "outputs": [],
   "source": [
    "nn_layers = [64, 64]  # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n",
    "                      # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n",
    "\n",
    "learning_rate = 0.001  # This is the step-size with which the gradient descent is carried out.\n",
    "                       # Tip: Use smaller step-sizes for larger networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "sOE-R5lAJhLR"
   },
   "source": [
    "Now, let us setup our model and the DQN algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "7F3J1_SCJhLR"
   },
   "outputs": [],
   "source": [
    "log_dir = \"/tmp/gym/\"\n",
    "os.makedirs(log_dir, exist_ok=True)\n",
    "\n",
    "# Create environment\n",
    "env_name = 'LunarLander-v2'\n",
    "env = gym.make(env_name)\n",
    "# You can also load other environments like cartpole, MountainCar, Acrobot.\n",
    "# Refer to https://gym.openai.com/docs/ for descriptions.\n",
    "\n",
    "# For example, if you would like to load Cartpole,\n",
    "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n",
    "\n",
    "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n",
    "\n",
    "callback = EvalCallback(env, log_path=log_dir, deterministic=True)  # For evaluating the performance of the agent periodically and logging the results.\n",
    "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n",
    "                     net_arch=nn_layers)\n",
    "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n",
    "            learning_rate=learning_rate,\n",
    "            batch_size=1,  # for simplicity, we are not doing batch update.\n",
    "            buffer_size=1,  # size of experience of replay buffer. Set to 1 as batch update is not done\n",
    "            learning_starts=1,  # learning starts immediately!\n",
    "            gamma=0.99,  # discount facto. range is between 0 and 1.\n",
    "            tau = 1,  # the soft update coefficient for updating the target network\n",
    "            target_update_interval=1,  # update the target network immediately.\n",
    "            train_freq=(1,\"step\"),  # train the network at every step.\n",
    "            max_grad_norm = 10,  # the maximum value for the gradient clipping\n",
    "            exploration_initial_eps = 1,  # initial value of random action probability\n",
    "            exploration_fraction = 0.5,  # fraction of entire training period over which the exploration rate is reduced\n",
    "            gradient_steps = 1,  # number of gradient steps\n",
    "            seed = 1,  # seed for the pseudo random generators\n",
    "            verbose=0)  # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n",
    "\n",
    "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n",
    "# Refer to  https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n",
    "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "pg1yw9tDJhLS"
   },
   "source": [
    "Before we train the model, let us look at an instance of Lunar Lander **before training**.  \n",
    "\n",
    "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "nkg_8N8WJhLS",
    "outputId": "a4d77c6a-4805-426d-a99c-2d14156a0b59",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "State shape:  (8,)\n",
      "Number of actions:  4\n"
     ]
    }
   ],
   "source": [
    "env_name = 'LunarLander-v2'\n",
    "env = gym.make(env_name)\n",
    "print('State shape: ', env.observation_space.shape)\n",
    "print('Number of actions: ', env.action_space.n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "mPSb09jcJhLS",
    "outputId": "bec45152-6458-412b-a187-d9fa12a75feb",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: <class 'gym.wrappers.monitoring.video_recorder.VideoRecorder'> is marked as deprecated and will be removed in the future.\u001b[0m\n",
      "  logger.deprecation(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Total reward: -550.8026438298007\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "<video width=400 controls><source 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type=\"video/mp4\"></video>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "env = gym.make(env_name, render_mode=\"rgb_array\")\n",
    "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n",
    "observation = env.reset()[0]\n",
    "\n",
    "total_reward = 0\n",
    "done = False\n",
    "while not done:\n",
    "  frame = env.render()\n",
    "  vid.capture_frame()\n",
    "  action, states = model.predict(observation, deterministic=True)\n",
    "  observation, reward, done, info, _ = env.step(action)\n",
    "  total_reward += reward\n",
    "vid.close()\n",
    "env.close()\n",
    "print(f\"\\nTotal reward: {total_reward}\")\n",
    "\n",
    "# show video\n",
    "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n",
    "HTML(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "0HNICJbAJhLS"
   },
   "source": [
    "From the video above, we see that the lander has crashed!\n",
    "It is now the time for training!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "JWQFrMifJhLT",
    "outputId": "9d28dfa2-0708-4fb0-d22b-c2e21886b1ca",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Eval num_timesteps=10000, episode_reward=-420.98 +/- 27.22\n",
      "Episode length: 151.80 +/- 30.46\n",
      "New best mean reward!\n",
      "Eval num_timesteps=20000, episode_reward=-561.62 +/- 27.61\n",
      "Episode length: 878.80 +/- 72.71\n",
      "Eval num_timesteps=30000, episode_reward=-249.88 +/- 48.31\n",
      "Episode length: 240.00 +/- 51.61\n",
      "New best mean reward!\n",
      "Eval num_timesteps=40000, episode_reward=-161.24 +/- 24.32\n",
      "Episode length: 338.20 +/- 107.08\n",
      "New best mean reward!\n",
      "Eval num_timesteps=50000, episode_reward=160.32 +/- 108.81\n",
      "Episode length: 241.20 +/- 55.82\n",
      "New best mean reward!\n",
      "Eval num_timesteps=60000, episode_reward=190.88 +/- 14.49\n",
      "Episode length: 646.80 +/- 65.03\n",
      "New best mean reward!\n",
      "Eval num_timesteps=70000, episode_reward=67.05 +/- 92.04\n",
      "Episode length: 139.80 +/- 35.46\n",
      "Eval num_timesteps=80000, episode_reward=267.52 +/- 20.00\n",
      "Episode length: 321.60 +/- 31.12\n",
      "New best mean reward!\n",
      "Eval num_timesteps=90000, episode_reward=67.08 +/- 126.76\n",
      "Episode length: 536.00 +/- 257.21\n",
      "Eval num_timesteps=100000, episode_reward=259.59 +/- 13.39\n",
      "Episode length: 339.80 +/- 19.18\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<stable_baselines3.dqn.dqn.DQN at 0x7b40c4b4cbb0>"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n",
    "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n",
    "# view the performance at every training episode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "bWZLHgzQJhLT"
   },
   "source": [
    "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n",
    "\n",
    "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n",
    "\n",
    "```\n",
    "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n",
    "Episode length: 259.80 +/- 47.47\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "CO0ahm_qJhLT"
   },
   "source": [
    "Now, let us look at the visual performance of the lander.\n",
    "\n",
    "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "ejkengvoJhLU",
    "outputId": "58b6bf9f-a95e-4b1a-d639-bcb871dbdcb7",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: <class 'gym.wrappers.monitoring.video_recorder.VideoRecorder'> is marked as deprecated and will be removed in the future.\u001b[0m\n",
      "  logger.deprecation(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Total reward: 241.530575753891\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "<video width=400 controls><source 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type=\"video/mp4\"></video>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "env = gym.make(env_name, render_mode=\"rgb_array\")\n",
    "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n",
    "observation = env.reset()[0]\n",
    "\n",
    "total_reward = 0\n",
    "done = False\n",
    "while not done:\n",
    "  frame = env.render()\n",
    "  vid.capture_frame()\n",
    "  action, states = model.predict(observation, deterministic=True)\n",
    "  observation, reward, done, info, _ = env.step(action)\n",
    "  total_reward += reward\n",
    "vid.close()\n",
    "env.close()\n",
    "print(f\"\\nTotal reward: {total_reward}\")\n",
    "\n",
    "# show video\n",
    "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n",
    "HTML(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "Y2YRNo3FJhLU"
   },
   "source": [
    "The lander has landed safely!!\n",
    "\n",
    "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "id": "lwHs_6B5JhLU",
    "outputId": "099cbd91-6e86-45f1-9c3d-a1a01fc4745f",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "x, y = ts2xy(load_results(log_dir), 'timesteps')  # Organising the logged results in to a clean format for plotting.\n",
    "plt.plot(x, y)\n",
    "plt.ylim([-300, 300])\n",
    "plt.xlabel('Timesteps')\n",
    "plt.ylabel('Episode Rewards')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "tOVFulHNJhLV"
   },
   "source": [
    "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n",
    "\n",
    "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration.   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {},
    "id": "8sotVlcGJhLc"
   },
   "source": [
    "---\n",
    "# References\n",
    "\n",
    "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n",
    "2. [Lunar Lander Environment](https://gymnasium.farama.org/environments/box2d/lunar_lander/)\n",
    "3. [OpenAI gym environments](https://gymnasium.farama.org/)\n",
    "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernel": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}