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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-reinforcement-learning-using-python/
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**课程名称：** Deep Reinforcement Learning using python 2025 **课程概述：** 本課程旨在教授深度强化学习（Deep Reinforcement Learning, DRL）的核心概念和实践技巧，帮助学员理解如何构建能够解决复杂问题的智能体。DRL是机器学习的一个子领域，它结合了强化学习（RL）和深度学习（DL）的优势，通过神经网络来处理和学习海量数据，已在围棋（AlphaGo）、国际象棋等领域取得了显著的突破。 **课程主要内容：** * **第一部分：深度强化学习导论** * 学习DRL的基础知识，包括策略（Policy）、状态价值函数（Value Function）、Q函数（Q Function）以及神经网络（Neural Network）在DRL中的作用。 * **第二部分：环境搭建** * 指导学员如何创建训练环境，并安装所需的Python库和工具。 * **第三部分：Grid World游戏与深度Q学习** * 学习构建第一个智能体，解决Grid World游戏。重点介绍神经网络的构建与训练，以及探索（Exploration）与利用（Exploitation）策略。 * **第四部分：Mountain Car游戏与深度Q学习** * 学习为Mountain Car游戏构建智能体。重点讲解如何构建ICM（Intrinsic Curiosity Module）和RND（Random Network Distillation）模块，以解决稀疏奖励问题。 * **第五部分：Flappy Bird游戏与深度Q学习** * 学习构建智能体，解决Flappy Bird游戏。将介绍多种Q网络变体，如Dueling Q Network、Prioritized Q Network和2-Step Q Network。 * **第六部分：Ms Pac-Man游戏与深度Q学习** * 学习构建智能体，解决Ms Pac-Man游戏。将介绍更多Q网络变体，如Noisy Q Network、Double Q Network和N-Steps Q Network。 * **第七部分：股票交易与深度Q学习** * 学习如何将DRL应用于股票交易领域，构建一个用于股票交易的智能体。 **适用人群：** 对人工智能、机器学习和深度学习感兴趣，希望学习如何构建智能体解决实际问题的学员。
Welcome to Deep Reinforcement Learning using python!Have you ever asked yourself how smart robots are created?Reinforcement learning concerned with creating intelligent robots which is a sub-field of machine learning that achieved impressive results in the recent years where now we can build robots that can beat humans in very hard games like alpha-go game and chess game.Deep Reinforcement Learning means Reinforcement learning field plus deep learning field where deep learning it is also a a sub-field of machine learning which uses special algorithms called neural networks.In this course we will talk about Deep Reinforcement Learning and we will talk about the following things:-Section 1: An Introduction to Deep Reinforcement LearningIn this section we will study all the fundamentals of deep reinforcement learning. These include Policy , Value function , Q function and neural network.Section 2: Setting up the environmentIn this section we will learn how to create our virtual environment and installing all required packages.Section 3: Grid World Game & Deep Q-LearningIn this section we will learn how to build our first smart robot to solve Grid World Game.Here we will learn how to build and train our neural network and how to make exploration and exploitation.Section 4: Mountain Car game & Deep Q-LearningIn this section we will try to build a robot to solve Mountain Car game.Here we will learn how to build ICM module and RND module to solve sparse reward problem in Mountain Car game.Section 5: Flappy bird game & Deep Q-learningIn this section we will learn how to build a smart robot to solve Flappy bird game.Here we will learn how to build many variants of Q network like dueling Q network , prioritized Q network and 2 steps Q networkSection 6: Ms Pacman game & Deep Q-LearningIn this section we will learn how to build a smart robot to solve Ms Pacman game.Here we will learn how to build another variants of Q network like noisy Q network , double Q network and n-steps Q network.Section 7:Stock trading & Deep Q-LearningIn this section we will learn how to build a smart robot for stock trading.
