没有合适的资源?快使用搜索试试~ 我知道了~
首页Deep Reinforcement Learning Hands-On Apply modern RL methods,
Deep Reinforcement Learning Hands-On Apply modern RL methods,
需积分: 10 187 浏览量
更新于2023-05-27
评论
收藏 12.81MB PDF 举报
This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, includ。
资源详情
资源评论
资源推荐


DeepReinforcementLearning
Hands-On

TableofContents
DeepReinforcementLearningHands-On
Whysubscribe?
PacktPub.com
Contributors
Abouttheauthor
Aboutthereviewers
PacktisSearchingforAuthorsLikeYou
Preface
Whothisbookisfor
Whatthisbookcovers
Togetthemostoutofthisbook
Downloadtheexamplecodefiles
Downloadthecolorimages
Conventionsused
Getintouch
Reviews
1.WhatisReinforcementLearning?
Learning–supervised,unsupervised,andreinforcement
RLformalismsandrelations
Reward
Theagent
Theenvironment
Actions
Observations
Markovdecisionprocesses
Markovprocess
Markovrewardprocess
Markovdecisionprocess
Summary
2.OpenAIGym
Theanatomyoftheagent
Hardwareandsoftwarerequirements
OpenAIGymAPI

Actionspace
Observationspace
Theenvironment
Creationoftheenvironment
TheCartPolesession
TherandomCartPoleagent
TheextraGymfunctionality–wrappersandmonitors
Wrappers
Monitor
Summary
3.DeepLearningwithPyTorch
Tensors
Creationoftensors
Scalartensors
Tensoroperations
GPUtensors
Gradients
Tensorsandgradients
NNbuildingblocks
Customlayers
Finalglue–lossfunctionsandoptimizers
Lossfunctions
Optimizers
MonitoringwithTensorBoard
TensorBoard101
Plottingstuff
Example–GANonAtariimages
Summary
4.TheCross-EntropyMethod
TaxonomyofRLmethods
Practicalcross-entropy
Cross-entropyonCartPole
Cross-entropyonFrozenLake
Theoreticalbackgroundofthecross-entropymethod
Summary
5.TabularLearningandtheBellmanEquation
Value,state,andoptimality

TheBellmanequationofoptimality
Valueofaction
Thevalueiterationmethod
Valueiterationinpractice
Q-learningforFrozenLake
Summary
6.DeepQ-Networks
Real-lifevalueiteration
TabularQ-learning
DeepQ-learning
Interactionwiththeenvironment
SGDoptimization
Correlationbetweensteps
TheMarkovproperty
ThefinalformofDQNtraining
DQNonPong
Wrappers
DQNmodel
Training
Runningandperformance
Yourmodelinaction
Summary
7.DQNExtensions
ThePyTorchAgentNetlibrary
Agent
Agent'sexperience
Experiencebuffer
Gymenvwrappers
BasicDQN
N-stepDQN
Implementation
DoubleDQN
Implementation
Results
Noisynetworks
Implementation
Results
剩余751页未读,继续阅读



















TsiuHinghiok
- 粉丝: 5
- 资源: 64
上传资源 快速赚钱
我的内容管理 收起
我的资源 快来上传第一个资源
我的收益
登录查看自己的收益我的积分 登录查看自己的积分
我的C币 登录后查看C币余额
我的收藏
我的下载
下载帮助

会员权益专享
最新资源
- ARM Cortex-A(armV7)编程手册V4.0.pdf
- ABB机器人保养总结解析.ppt
- 【超详细图解】菜鸡如何理解双向链表的python代码实现
- 常用网络命令的使用 ipconfig ping ARP FTP Netstat Route Tftp Tracert Telnet nslookup
- 基于单片机控制的DC-DC变换电路
- RS-232接口电路的ESD保护.pdf
- linux下用time(NULL)函数和localtime()获取当前时间的方法
- Openstack用户使用手册.docx
- KUKA KR 30 hA,KR 60 hA机器人产品手册.pdf
- Java programming with JNI
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈



安全验证
文档复制为VIP权益,开通VIP直接复制

评论0