A reproducible multi-agent AI research lab built around a deterministic bee-colony simulator. Combines a matched-seed benchmark, an interactive Strategy Arena, and an honest robustness audit pipeline — supporting behavior cloning, PPO, and centralized training with decentralized execution (CTDE) while keeping training, validation, and test seeds strictly isolated.
一个基于确定性蜂群模拟器的可复现多智能体 AI 研究平台。集成匹配种子基准、交互式策略竞技场和诚实鲁棒性审计流水线,支持行为克隆、PPO 及集中训练-分散执行(CTDE),并严格隔离训练、验证和测试种子。
- Objective目标
-
Provide a self-contained, hardware-portable environment for comparing multi-agent RL policies under reproducible, seed-isolated conditions.
提供自包含、硬件可移植的环境,用于在可复现、种子隔离条件下对比多智能体强化学习策略。
- Key Engineering核心工程
-
- Current stage M16 phase 1: all ML commands support explicit CPU / Apple MPS / NVIDIA CUDA device selection — no silent fallback; run manifests record the hardware backend for honest cross-machine audit.当前阶段 M16 阶段一:所有 ML 命令支持显式指定 CPU / Apple MPS / NVIDIA CUDA,杜绝静默回退;运行清单记录硬件后端,支持跨机器的诚实审计。
- M6 BC+PPO and M7 CTDE smoke workflows validated on Apple M1 Pro GPU.M6 BC+PPO 与 M7 CTDE 冒烟工作流已在 Apple M1 Pro GPU 上验证通过。
- Matched-seed arena enforces identical environmental conditions across policy comparisons.匹配种子竞技场确保策略对比时环境条件完全一致。
- Stack技术栈
- Python
- PyTorch
- PPO
- CTDE
- TypeScript
- JavaScript
- HTML
- License许可证
- Apache 2.0
- behavior-cloning
- ctde
- multi-agent-reinforcement-learning
- ppo
- pytorch
- reproducible-research
- simulation
An open-source bilingual playbook for software teams and open-source communities. Provides a one-page Collaboration Protocol, practical guides for discussions, meetings, feedback, and retrospectives, plus copy-ready Markdown templates — so every session ends with a decision, an owner, and a date.
面向软件团队和开源社区的开源双语协作手册。提供一页式协作约定、涵盖讨论、会议、反馈与复盘的实践方法,以及可直接复制的 Markdown 模板——让每次会话以决定、负责人和日期收尾。
- Objective目标
-
Turn circling discussions into clear decisions and accountable owners by giving teams a minimal shared protocol and ready-to-use templates.
通过提供最简共识约定和即用模板,将反复循环的讨论转化为明确决定与有担当的负责人。
- Key Engineering核心工程
-
- Published as a live guide and downloadable PDF at alanthssss.github.io/collaboration/.以在线指南和可下载 PDF 的形式发布于 alanthssss.github.io/collaboration/。
- Full bilingual (English / Simplified Chinese) coverage across all templates and guides.所有模板与指南均提供完整的英文 / 简体中文双语版本。
- Situation-indexed template table lets teams jump directly to the relevant template (discussion, meeting, decision, retrospective).按场景索引的模板表,帮助团队快速定位所需模板(讨论、会议、决策、复盘)。
- License许可证
- CC BY 4.0
- bilingual
- collaboration
- decision-making
- playbook
- templates
- retrospective
- teamwork
mcata · Merge Boost
TypeScript
A themeable merge roguelike puzzle game — and a full AI benchmark framework — built with React, Vite, and TypeScript. Players merge tiles on a 4×4 grid across escalating stages, collecting Boosts and navigating Hazards, while the underlying numeric engine serves as a reproducible benchmark target for AI agents.
一款可换肤的合并 Roguelike 益智游戏,同时内置完整 AI 基准框架,基于 React、Vite 和 TypeScript 构建。玩家在 4×4 网格上合并方块,经历逐渐升级的关卡、收集 Boost 并应对 Hazard;底层数值引擎作为可复现的 AI 基准目标。
- Key Engineering核心工程
-
- Pluggable theme system drives tile visuals independently of the numeric merge engine, enabling cosmetic changes without breaking benchmark compatibility.可插拔主题系统独立于数值合并引擎驱动方块视觉,支持外观变更而不影响基准兼容性。
- Run-level Rule modifiers (chosen before each run) vary corner bonuses, spawn frequencies, output scaling, and step budgets.每局开始前可选的规则修改器,调整角落奖励、生成频率、产出缩放和步数预算。
- Six Style archetypes with in-run upgrade paths enable deep build diversity.六种流派各有局内升级路径,支持丰富的构建多样性。
- Hazard stages (Entropy Tax, Collapse Field) stress-test both player and AI strategies.Hazard 关卡(熵税、崩溃场)对玩家和 AI 策略均构成压力测试。
- License许可证
- GPL-3.0
- benchmark
- game
- reactjs
- roguelike-game
An automation-first benchmark and experimentation platform that uses the 2048 game as a compact, deterministic environment for comparing search and reinforcement learning algorithms. Designed as a small engineering system covering the full model lifecycle — training, evaluation, registry, quality gates, deployment, observability, and rollback.
以 2048 游戏为紧凑确定性环境,用于对比搜索算法与强化学习算法的自动化优先基准与实验平台。系统覆盖完整模型生命周期:训练、评估、注册、质量门控、部署、可观测性与回滚。
- Objective目标
-
Demonstrate that AI experiments can be compared, reproduced, released, and operated safely — connecting Env → Train → Eval → Registry → Quality Gate → Deploy → Observe → Rollback in a single coherent system.
证明 AI 实验可安全比较、复现、发布与运维——将 Env → Train → Eval → Registry → Quality Gate → Deploy → Observe → Rollback 串联为单一连贯系统。
- Key Engineering核心工程
-
- Models carry immutable manifests; candidates must pass offline quality checks before promotion.模型附带不可变清单;候选模型须通过离线质量检查后方可晋升。
- Stable/Canary workloads expose metrics and traces, run in isolated Kubernetes environments, and support automated rollback.Stable / Canary 工作负载暴露指标与追踪数据,运行于隔离的 Kubernetes 环境,支持自动回滚。
- Multi-interface developer experience: CLI, TUI, and Web frontends.多界面开发体验:CLI、TUI 和 Web 前端。
- Dockerized, with Shell-scripted automation covering the full benchmark workflow.已 Docker 化,Shell 脚本自动化覆盖完整基准工作流。
- Stack技术栈
- Python
- Playwright
- Docker
- Kubernetes
- Shell
- HTML
- JavaScript
- License许可证
- GPL-3.0
- algorithms
- benchmark
- playwright
- reinforcement-learning
- performance
A setup-friendly, Ansible-inspired lightweight distributed infrastructure operations tool. Designed for ease of adoption with a single binary distribution and minimal configuration overhead.
一款易于上手、受 Ansible 启发的轻量分布式基础设施运维工具。以单一二进制分发,配置开销极小,降低采用门槛。
- Key Engineering核心工程
-
- Ships as a single binary, removing runtime dependencies for distributed ops tasks.以单一二进制交付,消除分布式运维任务的运行时依赖。
- Dockerfile and Makefile included for reproducible builds and container deployment.包含 Dockerfile 和 Makefile,支持可复现构建与容器部署。
- ansible-like
- distributed
- golang
- infra
- lightweight
- ops
A lightweight, cross-platform CLI tool for monitoring network service connectivity. Provides infrastructure and middleware health-check capabilities from the command line.
一款轻量跨平台 CLI 工具,用于监控网络服务连通性,提供基础设施和中间件健康检查能力。
- Key Engineering核心工程
-
- Cross-platform: runs on Linux, macOS, and Windows via Batchfile scripting.跨平台:通过 Batchfile 脚本支持 Linux、macOS 和 Windows。
- Go Template-driven output for flexible, scriptable reporting.Go Template 驱动输出,支持灵活的脚本化报告。
- infrastructure
- middleware
- network
- cli