From cad1dcacb9a6b6ab9167db61bfd88c7aac9901aa Mon Sep 17 00:00:00 2001 From: n03an Date: Sun, 8 Sep 2024 11:23:29 +0800 Subject: [PATCH] complete simple reiforcement agent example | restrcuture book structure --- 00 Couse Overview.md | 2 +- INTRO.md | 17 + README.md | 2 +- Unit03/neural_network.excalidraw | 9659 +++++++++++++++++++++++++++ Unit05/Reinforcement_Learning.ipynb | 142 +- Unit05/demo.ipynb | 356 - _config.yml | 4 +- _toc.yml | 5 +- 8 files changed, 9817 insertions(+), 370 deletions(-) create mode 100644 INTRO.md create mode 100644 Unit03/neural_network.excalidraw delete mode 100644 Unit05/demo.ipynb diff --git a/00 Couse Overview.md b/00 Couse Overview.md index 4f6f500..1a7356a 100644 --- a/00 Couse Overview.md +++ b/00 Couse Overview.md @@ -1,4 +1,4 @@ -## Welcome to 6.86x *Machine Learning with Python–From Linear Models to Deep Learning*. +## Welcome to _Machine Learning with Python–From Linear Models to Deep Learning_. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. diff --git a/INTRO.md b/INTRO.md new file mode 100644 index 0000000..1c8a3f3 --- /dev/null +++ b/INTRO.md @@ -0,0 +1,17 @@ +# n03@n's Machine Learning notes + +[Machine Learning with Python: from Linear Models to Deep Learning](https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-with-python-from-linear-models-to-deep-learning) + +[MIT MicroMasters® Program Calendar](https://micromasters.mit.edu/ds/upcoming-dates/) + +**Disclaimer**: The following notes are a mesh of my own notes, forums, youtube, GPT, medium, arxiv, some useful forum threads and various course materials (standford, MIT, DeepLearning.AI, udacity etc). There may be various error and that could very well be due to my own interpretation during learning or over simplifying. + +--- + +**Note:** As a prerequisite, I had to brush up some basic math important for machine learning. Here are my Jupiter notes published as [Digital Book 📚](https://N0-man.github.io/math-for-machine-learning/) + +![alt text](image.png) + +```{tableofcontents} + +``` diff --git a/README.md b/README.md index 318e676..e91ef13 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Learning Notes from MITx 6.86x semister +# Learning Notes from MITx semister [Machine Learning with Python: from Linear Models to Deep Learning](https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-with-python-from-linear-models-to-deep-learning) diff --git a/Unit03/neural_network.excalidraw b/Unit03/neural_network.excalidraw new file mode 100644 index 0000000..ea196c8 --- /dev/null +++ b/Unit03/neural_network.excalidraw @@ -0,0 +1,9659 @@ +{ + "type": "excalidraw", + "version": 2, + "source": "https://excalidraw.com", + "elements": [ + { + "id": "lUqURID79ojGqj0m3oPP0", + "type": "ellipse", + "x": 439.14744260284067, + "y": 307.9169791284273, + "width": 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+ "created": 1725692960996, + "lastRetrieved": 1725692960996 + } + } +} \ No newline at end of file diff --git a/Unit05/Reinforcement_Learning.ipynb b/Unit05/Reinforcement_Learning.ipynb index c0bfc66..73008c6 100644 --- a/Unit05/Reinforcement_Learning.ipynb +++ b/Unit05/Reinforcement_Learning.ipynb @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +138,10 @@ "\n", "# Returns a new state after taking action\n", "def take_action(action: str, actions_map: dict, current_state: tuple):\n", - " return current_state + actions_map[action]" + " return tuple(current_state + actions_map[action])\n", + "\n", + "def calculate_reward(next_state: tuple, goal: tuple):\n", + " return 100 if (next_state == goal) else -1" ] }, { @@ -149,7 +152,7 @@ "source": [ "def renderTinyWorld(environment: TinyBoxWorld):\n", " grid = np.zeros((environment.height, environment.width))\n", - " grid[environment.current_state] = 1 # starting point\n", + " grid[environment.current_state] = 1\n", " grid[environment.goal] = 3 # goal\n", " for obstacle in environment.obstacles:\n", " grid[obstacle] = -1 # obstacles\n", @@ -160,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -173,15 +176,132 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Current State: (0, 0) -> Next Action: down -> Next State: (1, 0), Reward Earned: -1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Current State: (1, 0) -> Next Action: down -> Next State: (2, 0), Reward Earned: -1\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Current State: (3, 0) -> Next Action: down -> Next State: (4, 0), Reward Earned: -1\n" + ] + }, { "data": { - "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Current State: (4, 0) -> Next Action: right -> Next State: (4, 1), Reward Earned: -1\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Current State: (4, 2) -> Next Action: right -> Next State: (4, 3), Reward Earned: -1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Current State: (4, 3) -> Next Action: right -> Next State: (4, 4), Reward Earned: 100\n", + "INFO:root:Goal Reached!!!\n" + ] } ], "source": [ @@ -195,12 +315,16 @@ " actions_map = {'up': np.array([-1, 0]), 'down': np.array([1, 0]), 'left': np.array([0, -1]), 'right': np.array([0, 1])}\n", ")\n", "\n", - "renderTinyWorld(environment)\n", + "while (environment.current_state != environment.goal):\n", + " renderTinyWorld(environment)\n", "\n", - "action = policy(environment)\n", - "environment.current_state = take_action(action, environment.actions_map, environment.current_state)\n", + " action = policy(environment)\n", + " next_state = take_action(action, environment.actions_map, environment.current_state)\n", + " reward = calculate_reward(next_state, environment.goal)\n", + " logging.info(f\"Current State: {environment.current_state} -> Next Action: {action} -> Next State: {next_state}, Reward Earned: {reward}\")\n", + " environment.current_state = next_state\n", "\n", - "renderTinyWorld(environment)\n" + "logging.info(f\"Goal Reached!!!\")" ] } ], diff --git a/Unit05/demo.ipynb b/Unit05/demo.ipynb deleted file mode 100644 index d53f407..0000000 --- a/Unit05/demo.ipynb +++ /dev/null @@ -1,356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Import Required Libraries**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import logging\n", - "logging.basicConfig(level=logging.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Environment Class**" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class GridWorld:\n", - " \"\"\"\n", - " GridWorld environment for navigation.\n", - " \n", - " Args:\n", - " - width: Width of the grid\n", - " - height: Height of the grid\n", - " - start: Start position of the agent\n", - " - goal: Goal position of the agent\n", - " - obstacles: List of obstacles in the grid\n", - " \n", - " Methods:\n", - " - reset: Reset the environment to the start state\n", - " - is_valid_state: Check if the given state is valid\n", - " - step: Take a step in the environment\n", - " \"\"\"\n", - " def __init__(self, width: int = 5, height: int = 5, start: tuple = (0, 0), goal: tuple = (4, 4), obstacles: list = None):\n", - " self.width = width\n", - " self.height = height\n", - " self.start = np.array(start)\n", - " self.goal = np.array(goal)\n", - " self.obstacles = [np.array(obstacle) for obstacle in obstacles] if obstacles else []\n", - " self.state = self.start\n", - " self.actions = {'up': np.array([-1, 0]), 'down': np.array([1, 0]), 'left': np.array([0, -1]), 'right': np.array([0, 1])}\n", - "\n", - " def reset(self):\n", - " \"\"\" \n", - " Reset the environment to the start state\n", - " \n", - " Returns:\n", - " - Start state of the environment\n", - " \"\"\"\n", - " self.state = self.start\n", - " return self.state\n", - "\n", - " def is_valid_state(self, state):\n", - " \"\"\"\n", - " Check if the given state is valid\n", - "\n", - " Args:\n", - " - state: State to be checked\n", - "\n", - " Returns:\n", - " - True if the state is valid, False otherwise\n", - " \"\"\"\n", - " return 0 <= state[0] < self.height and 0 <= state[1] < self.width and all((state != obstacle).any() for obstacle in self.obstacles)\n", - "\n", - " def step(self, action: str):\n", - " \"\"\"\n", - " Take a step in the environment\n", - "\n", - " Args:\n", - " - action: Action to be taken\n", - "\n", - " Returns:\n", - " - Next state, reward, done\n", - " \"\"\"\n", - " next_state = self.state + self.actions[action]\n", - " if self.is_valid_state(next_state):\n", - " self.state = next_state\n", - " reward = 100 if (self.state == self.goal).all() else -1\n", - " done = (self.state == self.goal).all()\n", - " return self.state, reward, done" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Navigation Policy**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def navigation_policy(state: np.array, goal: np.array, obstacles: list):\n", - " \"\"\"\n", - " Policy for navigating the agent in the grid world environment\n", - "\n", - " Args:\n", - " - state: Current state of the agent\n", - " - goal: Goal state of the agent\n", - " - obstacles: List of obstacles in the environment\n", - "\n", - " Returns:\n", - " - Action to be taken by the agent\n", - " \"\"\"\n", - " actions = ['up', 'down', 'left', 'right']\n", - " valid_actions = {}\n", - " for action in actions:\n", - " next_state = state + env.actions[action]\n", - " if env.is_valid_state(next_state):\n", - " valid_actions[action] = np.sum(np.abs(next_state - goal))\n", - " return min(valid_actions, key=valid_actions.get) if valid_actions else None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Run Simulation**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Start State: [0 0], Goal: [4 4], Obstacles: [array([1, 1]), array([1, 2]), array([2, 1]), array([3, 3])]\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:State: [0 0] -> Action: down -> Next State: [1 0], Reward: -1\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:State: [4 3] -> Action: right -> Next State: [4 4], Reward: 100\n", - "INFO:root:Goal reached!\n" - ] - } - ], - "source": [ - "def run_simulation_with_policy(env: GridWorld, policy):\n", - " \"\"\"\n", - " Run the simulation with the given policy\n", - "\n", - " Args:\n", - " - env: GridWorld environment\n", - " - policy: Policy to be used for navigation\n", - " \"\"\"\n", - " state = env.reset()\n", - " done = False\n", - " logging.info(f\"Start State: {state}, Goal: {env.goal}, Obstacles: {env.obstacles}\")\n", - " while not done:\n", - " # Visualization\n", - " grid = np.zeros((env.height, env.width))\n", - " grid[tuple(state)] = 1 # current state\n", - " grid[tuple(env.goal)] = 2 # goal\n", - " for obstacle in env.obstacles:\n", - " grid[tuple(obstacle)] = -1 # obstacles\n", - "\n", - " plt.imshow(grid, cmap='Pastel1')\n", - " plt.show()\n", - "\n", - " action = policy(state, env.goal, env.obstacles)\n", - " if action is None:\n", - " logging.info(\"No valid actions available, agent is stuck.\")\n", - " break\n", - " next_state, reward, done = env.step(action)\n", - " logging.info(f\"State: {state} -> Action: {action} -> Next State: {next_state}, Reward: {reward}\")\n", - " state = next_state\n", - " if done:\n", - " logging.info(\"Goal reached!\")\n", - "\n", - "# Define obstacles in the environment\n", - "obstacles = [(1, 1), (1, 2), (2, 1), (3, 3)]\n", - "\n", - "# Create the environment with obstacles\n", - "env = GridWorld(obstacles=obstacles)\n", - "\n", - "# Run the simulation\n", - "run_simulation_with_policy(env, navigation_policy)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_config.yml b/_config.yml index 68c118d..03dfaf9 100644 --- a/_config.yml +++ b/_config.yml @@ -1,8 +1,8 @@ # Book settings # Learn more at https://jupyterbook.org/customize/config.html -title: MIT Micromasters Machine Learning From Linear Models to Deep Learning -author: n03an +title: n03@n's Machine Learning notes +author: n03@n logo: logo_n.png # Force re-execution of notebooks on each build. diff --git a/_toc.yml b/_toc.yml index 63caca9..16adfdb 100644 --- a/_toc.yml +++ b/_toc.yml @@ -2,7 +2,7 @@ # Learn more at https://jupyterbook.org/customize/toc.html format: jb-book -root: README +root: INTRO parts: - caption: 00 Prerequisite Mathematics chapters: @@ -62,3 +62,6 @@ parts: - file: Unit03/Neural_Networks - file: Unit03/10_Feed_Forward_Neural_Network - file: Unit03/Stochastic_Gradient_Descent + - caption: 05 Reinforcement Learning + chapters: + - file: Unit05/Reinforcement_Learning