基于DouZero定制AI实战欢乐斗地主

Overview

DouZero_For_Happy_DouDiZhu: 将DouZero用于欢乐斗地主实战

Logo

  • 本项目基于DouZero
  • 环境配置请移步项目DouZero
  • 模型默认为WP,更换模型请修改start.py中的模型路径
  • 运行main.py即可
  • SL (baselines/sl/): 基于人类数据进行深度学习的预训练模型
  • DouZero-ADP (baselines/douzero_ADP/): 以平均分数差异(Average Difference Points, ADP)为目标训练的Douzero智能体
  • DouZero-WP (baselines/douzero_WP/): 以胜率(Winning Percentage, WP)为目标训练的Douzero智能体

说明

  • 将玩家角色设置为AI,需开局时手动输入玩家角色、初始手牌、三张底牌
  • 每轮手动输入其他两位玩家出的牌,AI给出出牌建议以及预计胜率
  • 暂未设计可视化界面,正考虑通过截屏自动识别开局手牌。
  • 欢乐斗地主窗口模式最大化运行,屏幕分辨率1920x1080。由于设计像素级操作,运行出错请检查截图区域坐标(位于MyPyQT_Form类中的__init__函数内)
  • 窗口移至右下角,避免遮挡手牌,历史牌,底牌区域。

使用步骤

  1. 确认环境正常,等待手牌出现、底牌出现、地主角色确认后,点击开始,耗时几秒完成识别。
  2. 窗口内显示识别结果,地主角色使用淡红色标出。识别完成自动开始记录出牌。
  3. 观察AI建议的出牌,在游戏中手动选择并打出。
  4. 游戏结束后弹出对话框提示输赢。
  5. 识别错误或无反应可通过结束按钮停止本局。至于游戏,就自己手动打完吧。

潜在Bug

  • 王炸时出牌特效时间较长,有一定几率导致只能识别出一个王。

鸣谢

  • 本项目基于DouZero
  • 借鉴了cardRecorder项目的部分代码以及模板图片,用于识别扑克牌

相关链接

Comments
  • 请问这是我的设置问题吗

    请问这是我的设置问题吗

    我首先按照要求安装了所需依赖,进入对局点击开始后提示输出

    等待下家出牌 等待下家出牌 等待下家出牌

    下家出牌: 44333 Traceback (most recent call last): File "c:\Users\11984\Downloads\DouZero_For_HappyDouDiZhu-master\main.py", line 170, in init_cards self.start() File "c:\Users\11984\Downloads\DouZero_For_HappyDouDiZhu-master\main.py", line 226, in start
    self.env.step(self.user_position, self.other_played_cards_env) TypeError: step() takes 1 positional argument but 3 were given

    以上这样的报错 图形界面卡死,图片附上

    report

    opened by jiahao2333 4
  • 开始以后闪退

    开始以后闪退

    看记录好像能识别出手牌,麻烦帮忙看看是为什么

    F:\Desktop\DouZero_For_HappyDouDiZhu-2.0>python main.py {'three_landlord_cards': [9, 8, 3], 'landlord_up': [17, 17, 14, 14, 13, 13, 12, 12, 10, 9, 8, 7, 7, 6, 5, 4, 3], 'landlord': [9, 9, 9, 10, 10, 10, 11, 11, 11, 11, 12, 12, 13, 13, 14, 14, 17, 17, 20, 30], 'landlord_down': [3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 8, 8, 8]} Traceback (most recent call last): File "C:\Users\liule\AppData\Local\Programs\Python\Python38\lib\site-packages\git_init_.py", line 83, in refresh() File "C:\Users\liule\AppData\Local\Programs\Python\Python38\lib\site-packages\git_init_.py", line 73, in refresh if not Git.refresh(path=path): File "C:\Users\liule\AppData\Local\Programs\Python\Python38\lib\site-packages\git\cmd.py", line 287, in refresh raise ImportError(err) ImportError: Bad git executable. The git executable must be specified in one of the following ways: - be included in your $PATH - be set via $GIT_PYTHON_GIT_EXECUTABLE - explicitly set via git.refresh()

    All git commands will error until this is rectified.

    This initial warning can be silenced or aggravated in the future by setting the $GIT_PYTHON_REFRESH environment variable. Use one of the following values: - quiet|q|silence|s|none|n|0: for no warning or exception - warn|w|warning|1: for a printed warning - error|e|raise|r|2: for a raised exception

    Example: export GIT_PYTHON_REFRESH=quiet

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last): File "main.py", line 164, in init_cards ai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position]) File "F:\Desktop\DouZero_For_HappyDouDiZhu-2.0\douzero\evaluation\deep_agent.py", line 25, in init self.model = load_model(position, model_path) File "F:\Desktop\DouZero_For_HappyDouDiZhu-2.0\douzero\evaluation\deep_agent.py", line 7, in load_model from douzero.dmc.models import model_dict File "F:\Desktop\DouZero_For_HappyDouDiZhu-2.0\douzero\dmc_init.py", line 1, in from .dmc import train File "F:\Desktop\DouZero_For_HappyDouDiZhu-2.0\douzero\dmc\dmc.py", line 12, in from .file_writer import FileWriter File "F:\Desktop\DouZero_For_HappyDouDiZhu-2.0\douzero\dmc\file_writer.py", line 25, in import git File "C:\Users\liule\AppData\Local\Programs\Python\Python38\lib\site-packages\git_init.py", line 85, in raise ImportError('Failed to initialize: {0}'.format(exc)) ImportError: Failed to initialize: Bad git executable. The git executable must be specified in one of the following ways: - be included in your $PATH - be set via $GIT_PYTHON_GIT_EXECUTABLE - explicitly set via git.refresh()

    All git commands will error until this is rectified.

    This initial warning can be silenced or aggravated in the future by setting the $GIT_PYTHON_REFRESH environment variable. Use one of the following values: - quiet|q|silence|s|none|n|0: for no warning or exception - warn|w|warning|1: for a printed warning - error|e|raise|r|2: for a raised exception

    Example: export GIT_PYTHON_REFRESH=quiet

    opened by 0xbba 3
  • 区域坐标能否解答下?

    区域坐标能否解答下?

    self.MyHandCardsPos = (414, 804, 1041, 59)  # 我的截图区域
            self.LPlayedCardsPos = (530, 470, 380, 160)  # 左边截图区域
            self.RPlayedCardsPos = (1010, 470, 380, 160)  # 右边截图区域
            self.LandlordFlagPos = [(1320, 300, 110, 140), (320, 720, 110, 140), (500, 300, 110, 140)]  # 地主标志截图区域(右-我-左)
            self.ThreeLandlordCardsPos = (817, 36, 287, 136)      # 地主底牌截图区域,resize成349x168
    

    我怎么用坐标拾取工具对比了下发现完全不对

    opened by daofeng2015 1
  • 由于分辨率导致的牌面识别瓶颈改进意见

    由于分辨率导致的牌面识别瓶颈改进意见

    使用win32gui库对游戏窗口进行坐标(0,0)、尺寸(默认尺寸)自动固定,如下: win32gui.SetWindowPos(hwnd, win32con.HWND_NOTOPMOST, 0, 0, 1440, 838, win32con.SWP_SHOWWINDOW)

    然后在此基础上制作配套pics,可极大降低由分辨率问题引起的各类找图问题。

    opened by null119 0
  • pos_duge报错

    pos_duge报错

    [ WARN:[email protected]] global D:\a\opencv-python\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp (239) cv::findDecoder imread_('QQ截图20220507102631.png'): can't open/read file: check file path/integrity Traceback (most recent call last): File "G:/python/code_py/douzero_huanledoudizhu/DouZero_For_HappyDouDiZhu/pos_debug.py", line 25, in cv2.imshow("test", img) cv2.error: OpenCV(4.5.5) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor' 想问一下这是什么情况

    opened by fengmianchen 0
Releases(v2.0)
A simple consistency training framework for semi-supervised image semantic segmentation

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation PseudoSeg is a simple consistency training framework for semi-supervised image semantic s

Google Interns 143 Dec 13, 2022
Tensorflow Implementation of Pixel Transposed Convolutional Networks (PixelTCN and PixelTCL)

Pixel Transposed Convolutional Networks Created by Hongyang Gao, Hao Yuan, Zhengyang Wang and Shuiwang Ji at Texas A&M University. Introduction Pixel

Hongyang Gao 95 Jul 24, 2022
Bag of Tricks for Natural Policy Gradient Reinforcement Learning

Bag of Tricks for Natural Policy Gradient Reinforcement Learning [ArXiv] Setup Python 3.8.0 pip install -r req.txt Mujoco 200 license Main Files main.

Brennan Gebotys 1 Oct 10, 2022
Western-3DSlicer-Modules - Point-Set Registrations for Ultrasound Probe Calibrations

Point-Set Registrations for Ultrasound Probe Calibrations -Undergraduate Thesis-

Matteo Tanzi 0 May 04, 2022
Code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization,

FSRA This repository contains the dataset link and the code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV

Dmmm 32 Dec 18, 2022
CARL provides highly configurable contextual extensions to several well-known RL environments.

CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments.

AutoML-Freiburg-Hannover 51 Dec 28, 2022
This repository attempts to replicate the SqueezeNet architecture and implement the same on an image classification task.

SqueezeNet-Implementation This repository attempts to replicate the SqueezeNet architecture using TensorFlow discussed in the research paper: "Squeeze

Rohan Mathur 3 Dec 13, 2022
Animation of solving the traveling salesman problem to optimality using mixed-integer programming and iteratively eliminating sub tours

tsp-streamlit Animation of solving the traveling salesman problem to optimality using mixed-integer programming and iteratively eliminating sub tours.

4 Nov 05, 2022
Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models

Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models, under review at ICLR 2017 requirements: T

Shuangfei Zhai 18 Mar 05, 2022
This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging" that has been accepted to NeurIPS 2021.

Dugh-NeurIPS-2021 This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroi

Ali Hashemi 5 Jul 12, 2022
Irrigation controller for Home Assistant

Irrigation Unlimited This integration is for irrigation systems large and small. It can offer some complex arrangements without large and messy script

Robert Cook 176 Jan 02, 2023
KE-Dialogue: Injecting knowledge graph into a fully end-to-end dialogue system.

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems This is the implementation of the paper: Learning Knowledge Bases with Par

CAiRE 42 Nov 10, 2022
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Contents Local and Global GAN Cross-View Image Translation Semantic Image Synthesis Acknowledgments Related Projects Citation Contributions Collaborat

Hao Tang 131 Dec 07, 2022
dyld_shared_cache processing / Single-Image loading for BinaryNinja

Dyld Shared Cache Parser Author: cynder (kat) Dyld Shared Cache Support for BinaryNinja Without any of the fuss of requiring manually loading several

cynder 76 Dec 28, 2022
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation

FIRA is a learning-based commit message generation approach, which first represents code changes via fine-grained graphs and then learns to generate commit messages automatically.

Van 21 Dec 30, 2022
Keras like implementation of Deep Learning architectures from scratch using numpy.

Mini-Keras Keras like implementation of Deep Learning architectures from scratch using numpy. How to contribute? The project contains implementations

MANU S PILLAI 5 Oct 10, 2021
Official PyTorch implementation of MAAD: A Model and Dataset for Attended Awareness

MAAD: A Model for Attended Awareness in Driving Install // Datasets // Training // Experiments // Analysis // License Official PyTorch implementation

7 Oct 16, 2022
Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"

CSDI This is the github repository for the NeurIPS 2021 paper "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

106 Jan 04, 2023
Learning and Building Convolutional Neural Networks using PyTorch

Image Classification Using Deep Learning Learning and Building Convolutional Neural Networks using PyTorch. Models, selected are based on number of ci

Mayur 126 Dec 22, 2022