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
Rootski - Full codebase for rootski.io (without the data)

📣 Welcome to the Rootski codebase! This is the codebase for the application run

Eric 20 Nov 18, 2022
Black for Python docstrings and reStructuredText (rst).

Style-Doc Style-Doc is Black for Python docstrings and reStructuredText (rst). It can be used to format docstrings (Google docstring format) in Python

Telekom Open Source Software 13 Oct 24, 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
Part of Speech Tagging using Hidden Markov Model (HMM) POS Tagger and Brill Tagger

Part of Speech Tagging using Hidden Markov Model (HMM) POS Tagger and Brill Tagger In this project, our aim is to tune, compare, and contrast the perf

Chirag Daryani 0 Dec 25, 2021
Arabic speech recognition, classification and text-to-speech.

klaam Arabic speech recognition, classification and text-to-speech using many advanced models like wave2vec and fastspeech2. This repository allows tr

ARBML 177 Dec 27, 2022
Source code of the "Graph-Bert: Only Attention is Needed for Learning Graph Representations" paper

Graph-Bert Source code of "Graph-Bert: Only Attention is Needed for Learning Graph Representations". Please check the script.py as the entry point. We

14 Mar 25, 2022
Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Yu Zhang 50 Nov 08, 2022
Longformer: The Long-Document Transformer

Longformer Longformer and LongformerEncoderDecoder (LED) are pretrained transformer models for long documents. ***** New December 1st, 2020: Longforme

AI2 1.6k Dec 29, 2022
Uses Google's gTTS module to easily create robo text readin' on command.

Tool to convert text to speech, creating files for later use. TTRS uses Google's gTTS module to easily create robo text readin' on command.

0 Jun 20, 2021
Search with BERT vectors in Solr and Elasticsearch

Search with BERT vectors in Solr and Elasticsearch

Dmitry Kan 123 Dec 29, 2022
skweak: A software toolkit for weak supervision applied to NLP tasks

Labelled data remains a scarce resource in many practical NLP scenarios. This is especially the case when working with resource-poor languages (or text domains), or when using task-specific labels wi

Norsk Regnesentral (Norwegian Computing Center) 850 Dec 28, 2022
A Python 3.6+ package to run .many files, where many programs written in many languages may exist in one file.

RunMany Intro | Installation | VSCode Extension | Usage | Syntax | Settings | About A tool to run many programs written in many languages from one fil

6 May 22, 2022
Chinese version of GPT2 training code, using BERT tokenizer.

GPT2-Chinese Description Chinese version of GPT2 training code, using BERT tokenizer or BPE tokenizer. It is based on the extremely awesome repository

Zeyao Du 5.6k Jan 04, 2023
A Fast Command Analyser based on Dict and Pydantic

Alconna Alconna 隶属于ArcletProject, 在Cesloi内有内置 Alconna 是 Cesloi-CommandAnalysis 的高级版,支持解析消息链 一般情况下请当作简易的消息链解析器/命令解析器 文档 暂时的文档 Example from arclet.alcon

19 Jan 03, 2023
Unsupervised Language Modeling at scale for robust sentiment classification

** DEPRECATED ** This repo has been deprecated. Please visit Megatron-LM for our up to date Large-scale unsupervised pretraining and finetuning code.

NVIDIA Corporation 1k Nov 17, 2022
WikiPron - a command-line tool and Python API for mining multilingual pronunciation data from Wiktionary

WikiPron WikiPron is a command-line tool and Python API for mining multilingual pronunciation data from Wiktionary, as well as a database of pronuncia

213 Jan 01, 2023
Yet Another Neural Machine Translation Toolkit

YANMTT YANMTT is short for Yet Another Neural Machine Translation Toolkit. For a backstory how I ended up creating this toolkit scroll to the bottom o

Raj Dabre 121 Jan 05, 2023
✔👉A Centralized WebApp to Ensure Road Safety by checking on with the activities of the driver and activating label generator using NLP.

AI-For-Road-Safety Challenge hosted by Omdena Hyderabad Chapter Original Repo Link : https://github.com/OmdenaAI/omdena-india-roadsafety Final Present

Prathima Kadari 7 Nov 29, 2022
Lingtrain Aligner — ML powered library for the accurate texts alignment.

Lingtrain Aligner ML powered library for the accurate texts alignment in different languages. Purpose Main purpose of this alignment tool is to build

Sergei Averkiev 76 Dec 14, 2022
nlabel is a library for generating, storing and retrieving tagging information and embedding vectors from various nlp libraries through a unified interface.

nlabel is a library for generating, storing and retrieving tagging information and embedding vectors from various nlp libraries through a unified interface.

Bernhard Liebl 2 Jun 10, 2022