Dope Wars game engine on StarkNet L2 roll-up

Related tags

Text Data & NLPRYO
Overview

RYO

Dope Wars game engine on StarkNet L2 roll-up.

What

TI-83 drug wars built as smart contract system.

Background mechanism design notion here.

Initial exploration / walkthrough viability testing blog here.

Join in and learn about:

- Cairo. A turing-complete language for programs that become proofs.
- StarkNet. An Ethereum L2 rollup with:
    - L1 for data availability
    - State transitions executed by validity proofs that the EVM checks.

Setup

Clone this repo and use our docker shell to interact with starknet:

git clone [email protected]:dopedao/RYO.git
cd RYO
bin/shell starknet --version

The CLI allows you to deploy to StarkNet and read/write to contracts already deployed. The CLI communicates with a server that StarkNet runs, which bundles the requests, executes the program (contracts are Cairo programs), creates and aggregates validity proofs, then posts them to the Goerli Ethereum testnet. Learn more in the Cairo language and StarkNet docs here, which also has instructions for manual installation if you are not using docker.

If using VS-code for writing code, install the extension for syntax highlighting:

curl -LO https://github.com/starkware-libs/cairo-lang/releases/download/v0.4.0/cairo-0.4.0.vsix
code --install-extension cairo-0.4.0.vsix
code .

Dev

Flow:

  1. Compile the contract with the CLI
  2. Test using pytest
  3. Deploy with CLI
  4. Interact using the CLI or the explorer

File name prefixes are paired (e.g., contract, ABI and test all share comon prefix).

Compile

The compiler will check the integrity of the code locally. It will also produce an ABI, which is a mapping of the contract functions (used to interact with the contract).

bin/shell starknet-compile contracts/GameEngineV1.cairo \
    --output contracts/GameEngineV1_compiled.json \
    --abi abi/GameEngineV1_contract_abi.json

bin/shell starknet-compile contracts/MarketMaker.cairo \
    --output contracts/MarketMaker_compiled.json \
    --abi abi/MarketMaker_contract_abi.json

Test

bin/shell pytest testing/GameEngineV1_contract_test.py

bin/shell pytest testing/MarketMaker_contract_test.py

Deploy

bin/shell starknet deploy --contract contracts/GameEngineV1_compiled.json \
    --network=alpha

bin/shell starknet deploy --contract contracts/MarketMaker_compiled.json \
    --network=alpha

Upon deployment, the CLI will return an address, which can be used to interact with.

Check deployment status by passing in the transaction ID you receive:

bin/shell starknet tx_status --network=alpha --id=176230

PENDING Means that the transaction passed the validation and is waiting to be sent on-chain.

{
    "block_id": 18880,
    "tx_status": "PENDING"
}

Interact

CLI - Write (initialise markets). Set up item_id=5 across all 40 locations. Each pair has 10x more money than item quantity. All items have the same curve

bin/shell starknet invoke \
    --network=alpha \
    --address 0x01c721e3452005ddc95f10bf8dc86c98c32a224085c258024931ddbaa8a44557 \
    --abi abi/GameEngineV1_contract_abi.json \
    --function admin_set_pairs_for_item \
    --inputs 5 \
        40 \
        20 40 60 80 100 120 140 160 180 200 \
        220 240 260 280 300 320 340 360 380 400 \
        420 440 460 480 500 520 540 560 580 600 \
        620 640 660 680 700 720 740 760 780 800 \
        40 \
        200 400 600 800 1000 1200 1400 1600 1800 2000 \
        2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 \
        4200 4400 4600 4800 5000 5200 5400 5600 5800 6000 \
        6200 6400 6600 6800 7000 7200 7400 7600 7800 8000

Change 5 to another item_id in the range 1-10 to populate other curves.

CLI - Write (initialize user). Set up user_id=733 to have 2000 of item 5.

bin/shell starknet invoke \
    --network=alpha \
    --address 0x01c721e3452005ddc95f10bf8dc86c98c32a224085c258024931ddbaa8a44557 \
    --abi abi/GameEngineV1_contract_abi.json \
    --function admin_set_user_amount \
    --inputs 733 5 2000

CLI - Read (user state)

bin/shell starknet call \
    --network=alpha \
    --address 0x01c721e3452005ddc95f10bf8dc86c98c32a224085c258024931ddbaa8a44557 \
    --abi abi/GameEngineV1_contract_abi.json \
    --function check_user_state \
    --inputs 733

CLI - Write (Have a turn). User 733 goes to location 34 to sell (sell is 1, buy is 0) item 5, giving 100 units.

bin/shell starknet invoke \
    --network=alpha \
    --address 0x01c721e3452005ddc95f10bf8dc86c98c32a224085c258024931ddbaa8a44557 \
    --abi abi/GameEngineV1_contract_abi.json \
    --function have_turn \
    --inputs 733 34 1 5 100

Calling the check_user_state() function again reveals that the 100 units were exchanged for some quantity of money.

Alternatively, see and do all of the above with the Voyager browser here.

Game flow

admin ->
        initialise state variables
        lock admin power
user_1 ->
        have_turn(got_to_loc, trade_x_for_y)
            check if game finished.
            check user authentification.
            check if user allowed using game clock.
            add to random seed.
            user location update.
                decrease money count if new city.
            check for dealer dash (x %).
                check for chase dealer (x %).
                    item lost, no money gained.
            trade with market curve for location.
                decrease money/item, increase the other.
            check for any of:
                mugging (x %).
                    check for run (x %).
                        lose a percentage of money.
                gang war (x %).
                    check for fight (x %).
                        lose a percentage of money.
                cop raid (x %).
                    check for bribe (x %).
                        lose percentage of money & items held.
                find item (x %).
                    increase item balance.
                local shipment (x %).
                    increase item counts in suburb curves.
                warehouse seizure (x %).
                    decrease item counts in suburb curves.
            save next allowed turn as game_clock + n.
user2 -> (same as user_1)

Next steps

Building out parts to make a functional v1. Some good entry-level options for anyone wanting to try out Cairo.

  • Initialised multiple player states.
  • Turn rate limiting. Game has global clock that increments every time a turn occurs. User has a lockout of x clock ticks.
  • Game end criterion based on global clock.
  • Finish mappings/locations.json. Name places and implement different cost to travel for some locations.
    • Locations will e.g., be 10 cities [0, 9] each with 4 suburbs [0, 4].
    • E.g., locations 0, 11, 21, 31 are city 1. Locations 2, 12, 22, 32 are city 2. So location_id=27 is city 7, suburb 2. Free to travel to other suburbs in same city (7, 17, 37).
    • Need to create a file with nice city/subrub names for these in
  • Finish mappings/items.json. Populate and tweak the item names and item unit price. E.g., cocaine price per unit different from weed price per unit.
  • Finish mappings/initial_markets.csv. Create lists of market pair values to initialize the game with. E.g., for all 40 locations x 10 items = 400 money_count-item_count pairs as a separate file. A mapping of 600 units with 6000 money initialises a dealer in that location with 60 of the item at (6000/60) 100 money per item. This mapping should be in the ballpark of the value in items.json. The fact that values deviate, creates trade opportunities at the start of the game. (e.g., a location might have large quantity at lower price).
  • Refine both the likelihood (basis points per user turn) and impact (percentage change) that events have and treak the constanst at the top of contracts/GameEngineV1.cairo. E.g., how often should you get mugged, how much money would you lose.
  • Initialize users with money upon first turn. (e.g., On first turn triggers save of starting amount e.g., 10,000, then sets the flag to )
  • Create caps on maximum parameters (40 location_ids, 10k user_ids, 10 item_ids)
  • User authentication. E.g., signature verification.
  • Add health clock. E.g., some events lower health

Welcome:

  • PRs
  • Issues
  • Questions about Cairo
  • Ideas for the game
Trained T5 and T5-large model for creating keywords from text

text to keywords Trained T5-base and T5-large model for creating keywords from text. Supported languages: ru Pretraining Large version | Pretraining B

Danil 61 Nov 24, 2022
This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest

Rachford-Rice Contest This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest. Can you solve the Rachford-Rice problem for all t

13 Sep 20, 2022
AI-powered literature discovery and review engine for medical/scientific papers

AI-powered literature discovery and review engine for medical/scientific papers paperai is an AI-powered literature discovery and review engine for me

NeuML 819 Dec 30, 2022
Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow.

Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow. Documentation Proper documentation is available at

HUSEIN ZOLKEPLI 151 Jan 05, 2023
☀️ Measuring the accuracy of BBC weather forecasts in Honolulu, USA

Accuracy of BBC Weather forecasts for Honolulu This repository records the forecasts made by BBC Weather for the city of Honolulu, USA. Essentially, t

Max Halford 12 Oct 15, 2022
Interpretable Models for NLP using PyTorch

This repo is deprecated. Please find the updated package here. https://github.com/EdGENetworks/anuvada Anuvada: Interpretable Models for NLP using PyT

Sandeep Tammu 19 Dec 17, 2022
A Facebook Messenger Chatbot using NLP

A Facebook Messenger Chatbot using NLP This project is about creating a messenger chatbot using basic NLP techniques and models like Logistic Regressi

6 Nov 20, 2022
[ICLR 2021 Spotlight] Pytorch implementation for "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts."

RIDE: Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. by Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu and Stella X. Yu at UC

Xudong (Frank) Wang 205 Dec 16, 2022
Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE)

VK.com 847 Dec 19, 2022
ByT5: Towards a token-free future with pre-trained byte-to-byte models

ByT5: Towards a token-free future with pre-trained byte-to-byte models ByT5 is a tokenizer-free extension of the mT5 model. Instead of using a subword

Google Research 409 Jan 06, 2023
Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated

Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated. This engine can later be used for downstream tasks in NLP such as Q&A, summarization, generation

Diego 1 Mar 20, 2022
मराठी भाषा वाचविण्याचा एक प्रयास. इंग्रजी ते मराठीचा शब्दकोश. An attempt to preserve the Marathi language. A lightweight and ad free English to Marathi thesaurus.

For English, scroll down मराठी शब्द मराठी भाषा वाचवण्यासाठी मी हा ओपन सोर्स प्रोजेक्ट सुरू केला आहे. माझ्या मते, आपली भाषा हळूहळू आणि कोणाचाही लक्षात

मुक्त स्त्रोत 20 Oct 11, 2022
Sapiens is a human antibody language model based on BERT.

Sapiens: Human antibody language model ____ _ / ___| __ _ _ __ (_) ___ _ __ ___ \___ \ / _` | '_ \| |/ _ \ '

Merck Sharp & Dohme Corp. a subsidiary of Merck & Co., Inc. 13 Nov 20, 2022
Every Google, Azure & IBM text to speech voice for free

TTS-Grabber Quick thing i made about a year ago to download any text with any tts voice, over 630 voices to choose from currently. It will split the i

16 Dec 07, 2022
A python script that will use hydra to get user and password to login to ssh, ftp, and telnet

Hydra-Auto-Hack A python script that will use hydra to get user and password to login to ssh, ftp, and telnet Project Description This python script w

2 Jan 16, 2022
Linear programming solver for paper-reviewer matching and mind-matching

Paper-Reviewer Matcher A python package for paper-reviewer matching algorithm based on topic modeling and linear programming. The algorithm is impleme

Titipat Achakulvisut 66 Jul 05, 2022
PORORO: Platform Of neuRal mOdels for natuRal language prOcessing

PORORO: Platform Of neuRal mOdels for natuRal language prOcessing pororo performs Natural Language Processing and Speech-related tasks. It is easy to

Kakao Brain 1.2k Dec 21, 2022
DeepAmandine is an artificial intelligence that allows you to talk to it for hours, you won't know the difference.

DeepAmandine This is an artificial intelligence based on GPT-3 that you can chat with, it is very nice and makes a lot of jokes. We wish you a good ex

BuyWithCrypto 3 Apr 19, 2022
Library for fast text representation and classification.

fastText fastText is a library for efficient learning of word representations and sentence classification. Table of contents Resources Models Suppleme

Facebook Research 24.1k Jan 05, 2023