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强化学习实战系列(PyTorch版)

唐宇迪 唐

强化学习系列课程主要包括经典算法原理讲解与案例实战两大部分。通俗讲解当下主流强化学习算法思想，结合实例解读算法整理应用流程并结合案例展开代码实战。整体风格通俗易懂，适合准备入门强化学习并进阶提升的同学们。课程目录界面提供全部所需PPT,数据，代码！

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What's inside

Learning objectives

  • 掌握强化学习基本思想及其应用领域
  • 掌握强化学习主流算法原理
  • 掌握强化学习算法数学推导过程及其证明
  • 熟练使用pytorch框架构建强化学习模型
  • 熟练使用openai环境训练强化学习算法模型
  • 熟练基本强化学习算法进行实际项目构建
  • 掌握dqn，a3c等主流强化学习算法及其数学原理

Syllabus

掌握强化学习基本思想及其应用
课程介绍
一张图通俗解释强化学习
强化学习的指导依据
Read more
强化学习AI游戏
应用领域简介
强化学习工作流程
计算机眼中的状态与行为
本章课件下载
掌握PPO算法原理及其数学推导与证明
基本情况介绍
与环境交互得到所需数据
要完成的目标分析
策略梯度推导
baseline方法
OnPolicy与OffPolicy策略
importance sampling的作用
PPO算法整体思路解析
掌握策略梯度算法训练方法及其应用实例
Critic的作用与效果
PPO2版本公式解读
参数与网络结构定义
得到动作结果
奖励获得与计算
参数迭代与更新
掌握Q-learning算法原理及其应用
算法原理通俗解读
目标函数与公式解析
Qlearning算法实例解读
Q值迭代求解
DQN简介
掌握DQN算法及其应用实例
整体任务流程演示
探索与action获取
计算target值
训练与更新
掌握DQN算法改进效果
DoubleDqn要解决的问题
DuelingDqn改进方法
Dueling整体网络架构分析
MultiSetp策略
连续动作处理方法
掌握A3C算法原理及其公式推导
AC算法回顾与知识点总结
优势函数解读与分析
计算流程实例
A3C整体架构分析
损失函数整理
掌握A3C算法建模流程及其实现方法
整体流程与环境配置
启动游戏环境
要计算的指标回顾
初始化局部模型并加载参数
与环境交互得到训练数据
训练网络模型
掌握CNN算法原理及其参数
积神经网络应用领域
卷积的作用
卷积特征值计算方法
得到特征图表示
步长与卷积核大小对结果的影响
边缘填充方法
特征图尺寸计算与参数共享
池化层的作用
整体网络架构
VGG网络架构
残差网络Resnet
感受野的作用
掌握PyTorch框架基本使用方法
PyTorch框架发展趋势简介
框架安装方法（CPU与GPU版本）
PyTorch基本操作简介
自动求导机制
线性回归DEMO-数据与参数配置
线性回归DEMO-训练回归模型
常见tensor格式
Hub模块简介
掌握PyTorch框架图像识别常用方法
卷积网络参数定义
网络流程解读
Vision模块功能解读
分类任务数据集定义与配置
图像增强的作用
数据预处理与数据增强模块
Batch数据制作
迁移学习的目标
迁移学习策略
加载训练好的网络模型
优化器模块配置
实现训练模块
训练结果与模型保存
加载模型对测试数据进行预测
额外补充-Resnet论文解读
额外补充-Resnet网络架构解读

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Effort
10.5 total hours
Via
Udemy
Instructor
唐宇迪 唐
Language
中文
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