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
Abhijith Neil Abraham 2 Nov 05, 2021
Official Pytorch implementation of Test-Agnostic Long-Tailed Recognition by Test-Time Aggregating Diverse Experts with Self-Supervision.

This repository is the official Pytorch implementation of Test-Agnostic Long-Tailed Recognition by Test-Time Aggregating Diverse Experts with Self-Supervision.

vanint 101 Dec 30, 2022
NLP codes implemented with Pytorch (w/o library such as huggingface)

NLP_scratch NLP codes implemented with Pytorch (w/o library such as huggingface) scripts ├── models: Neural Network models ├── data: codes for dataloa

3 Dec 28, 2021
A crowdsourced dataset of dialogues grounded in social contexts involving utilization of commonsense.

A crowdsourced dataset of dialogues grounded in social contexts involving utilization of commonsense.

Alexa 62 Dec 20, 2022
Include MelGAN, HifiGAN and Multiband-HifiGAN, maybe NHV in the future.

Fast (GAN Based Neural) Vocoder Chinese README Todo Submit demo Support NHV Discription Include MelGAN, HifiGAN and Multiband-HifiGAN, maybe include N

Zhengxi Liu (刘正曦) 134 Dec 16, 2022
A Lightweight NLP Data Loader for All Deep Learning Frameworks in Python

LineFlow: Framework-Agnostic NLP Data Loader in Python LineFlow is a simple text dataset loader for NLP deep learning tasks. LineFlow was designed to

TofuNLP 177 Jan 04, 2023
Creating a Feed of MISP Events from ThreatFox (by abuse.ch)

ThreatFox2Misp Creating a Feed of MISP Events from ThreatFox (by abuse.ch) What will it do? This will fetch IOCs from ThreatFox by Abuse.ch, convert t

17 Nov 22, 2022
Code for "Generating Disentangled Arguments with Prompts: a Simple Event Extraction Framework that Works"

GDAP The code of paper "Code for "Generating Disentangled Arguments with Prompts: a Simple Event Extraction Framework that Works"" Event Datasets Prep

45 Oct 29, 2022
PyKaldi is a Python scripting layer for the Kaldi speech recognition toolkit.

PyKaldi is a Python scripting layer for the Kaldi speech recognition toolkit. It provides easy-to-use, low-overhead, first-class Python wrappers for t

922 Dec 31, 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
Easy, fast, effective, and automatic g-code compression!

Getting to the meat of g-code. Easy, fast, effective, and automatic g-code compression! MeatPack nearly doubles the effective data rate of a standard

Scott Mudge 97 Nov 21, 2022
Semi-automated vocabulary generation from semantic vector models

vec2word Semi-automated vocabulary generation from semantic vector models This script generates a list of potential conlang word forms along with asso

9 Nov 25, 2022
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

LancoPKU 105 Jan 03, 2023
Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision Training Efficiency We show the training efficiency of our DSLP model b

Chenyang Huang 37 Jan 04, 2023
The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.

Neural Machine Translation communication system The model is basically direct to convert one source language to another targeted language using encode

Nishant Banjade 7 Sep 22, 2022
Türkçe küfürlü içerikleri bulan bir yapay zeka kütüphanesi / An ML library for profanity detection in Turkish sentences

"Kötü söz sahibine aittir." -Anonim Nedir? sinkaf uygunsuz yorumların bulunmasını sağlayan bir python kütüphanesidir. Farkı nedir? Diğer algoritmalard

KaraGoz 4 Feb 18, 2022
Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

Yan Yuanmeng 478 Dec 25, 2022
PyTorch implementation of NATSpeech: A Non-Autoregressive Text-to-Speech Framework

A Non-Autoregressive Text-to-Speech (NAR-TTS) framework, including official PyTorch implementation of PortaSpeech (NeurIPS 2021) and DiffSpeech (AAAI 2022)

760 Jan 03, 2023
Resources for "Natural Language Processing" Coursera course.

Natural Language Processing course resources This github contains practical assignments for Natural Language Processing course by Higher School of Eco

Advanced Machine Learning specialisation by HSE 1.1k Jan 01, 2023
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel

537 Jan 05, 2023