Learning based AI for playing multi-round Koi-Koi hanafuda card games. Have fun.

Overview

Koi-Koi AI

Learning based AI for playing multi-round Koi-Koi hanafuda card games.

Play Interface

Platform

  • Python
  • PyTorch
  • PySimpleGUI (for the interface playing vs AI)

About Koi-Koi Hanafuda Card Games

Hanafuda is a kind of traditional Japanese playing cards. A hanafuda deck contains 48 cards divided by 12 suits corresponding to 12 months, which are also divided into four rank-like categories with different importance. Koi-Koi is a kind of two-player hanafuda card game. The goal of Koi-Koi is to collect cards by matching the cards by suit, and forming specific winning hands called Yaku from the acquired pile to earn points from the opponent.

Hanafuda Deck

Rules & Yaku List

Koi-Koi is consisted by multiple rounds and both players start with equal points. In every round, two players discard and draw to pair and collect cards by turn until someone forms Yakus successfully. Then, he can end this round to receive points from the opponent, or claim koi-koi and continues this round to earn more yakus and points. The detailed rules and Yaku list of this project is the same as PC game KoiKoi-Japan on Steam.

Yaku List

Network Model & Dataset

The model is a card-state self-attention based neural network with multiple Transformer encoder layers, and is trained by supervised learning. The dataset of 200 eight-round Koi-Koi game records is also provided.

Model

Future Work

Try to improve the performance with reinforcement learning...

Owner
Sanghai Guan
业精于勤荒于嬉
Sanghai Guan
💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Official PyTorch implementation and EmoCause evaluatio

Hyunwoo Kim 51 Jan 06, 2023
PyTorch code for the ICCV'21 paper: "Always Be Dreaming: A New Approach for Class-Incremental Learning"

Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning PyTorch code for the ICCV 2021 paper: Always Be Dreaming: A New Approach f

49 Dec 21, 2022
Sparse-dense operators implementation for Paddle

Sparse-dense operators implementation for Paddle This module implements coo, csc and csr matrix formats and their inter-ops with dense matrices. Feel

北海若 3 Dec 17, 2022
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

Couler What is Couler? Couler aims to provide a unified interface for constructing and managing workflows on different workflow engines, such as Argo

Couler Project 781 Jan 03, 2023
'Aligned mixture of latent dynamical systems' (amLDS) for stimulus decoding probabilistic manifold alignment across animals. P. Herrero-Vidal et al. NeurIPS 2021 code.

Across-animal odor decoding by probabilistic manifold alignment (NeurIPS 2021) This repository is the official implementation of aligned mixture of la

Pedro Herrero-Vidal 3 Jul 12, 2022
NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models

NaturalCC NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models for many software engineering tasks,

159 Dec 28, 2022
ULMFiT for Genomic Sequence Data

Genomic ULMFiT This is an implementation of ULMFiT for genomics classification using Pytorch and Fastai. The model architecture used is based on the A

Karl 276 Dec 12, 2022
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

DongGeun-Yoon 19 Jun 07, 2022
Unofficial Pytorch Implementation of WaveGrad2

WaveGrad 2 — Unofficial PyTorch Implementation WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis Unofficial PyTorch+Lightning Implementati

MINDs Lab 104 Nov 29, 2022
Pytorch implementation of ICASSP 2022 paper Attention Probe: Vision Transformer Distillation in the Wild

Attention Probe: Vision Transformer Distillation in the Wild Jiahao Wang, Mingdeng Cao, Shuwei Shi, Baoyuan Wu, Yujiu Yang In ICASSP 2022 This code is

IIGROUP 6 Sep 21, 2022
Exponential Graph is Provably Efficient for Decentralized Deep Training

Exponential Graph is Provably Efficient for Decentralized Deep Training This code repository is for the paper Exponential Graph is Provably Efficient

3 Apr 20, 2022
A different spin on dataclasses.

dataklasses Dataklasses is a library that allows you to quickly define data classes using Python type hints. Here's an example of how you use it: from

David Beazley 752 Nov 18, 2022
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure

ViViT is a collection of numerical tricks to efficiently access curvature from the generalized Gauss-Newton (GGN) matrix based on its low-rank structure. Provided functionality includes computing

Felix Dangel 12 Dec 08, 2022
Codes of the paper Deformable Butterfly: A Highly Structured and Sparse Linear Transform.

Deformable Butterfly: A Highly Structured and Sparse Linear Transform DeBut Advantages DeBut generalizes the square power of two butterfly factor matr

Rui LIN 8 Jun 10, 2022
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 04, 2023
Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Introduction Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach Datasets: WebFG-496

21 Sep 30, 2022
Official Repository for the paper "Improving Baselines in the Wild".

iWildCam and FMoW baselines (WILDS) This repository was originally forked from the official repository of WILDS datasets (commit 7e103ed) For general

Kazuki Irie 3 Nov 24, 2022
用强化学习DQN算法,训练AI模型来玩合成大西瓜游戏,提供Keras版本和PARL(paddle)版本

用强化学习玩合成大西瓜 代码地址:https://github.com/Sharpiless/play-daxigua-using-Reinforcement-Learning 用强化学习DQN算法,训练AI模型来玩合成大西瓜游戏,提供Keras版本、PARL(paddle)版本和pytorch版本

72 Dec 17, 2022
Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

Self-Supervised-MVS This repository is the official PyTorch implementation of our AAAI 2021 paper: "Self-supervised Multi-view Stereo via Effective Co

hongbin_xu 127 Jan 04, 2023