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Three complete generations of code
PPO learning · V3Pro decision refinement · Two generations of KSPlay GuanDan Service
Play online ↗ · Architecture · Web services · Dataset · Get started · Research
Not just a stronger move. A better plan for the rest of the hand.
GuanDan is a game of partnership, hidden information, and long-term control. The cheapest card to play now may break your best combination; a pass may give your teammate the lead.
DanKS learns to choose with the rest of the hand in mind. Developed by the Kingsoft AI Product Center, it brings structure-aware retrieval and PPO policy learning together—and now pairs the AI codebase with two generations of an open-source web game service. Study the agent, train a policy, build your own table, or simply sit down and play.
Take your seat. Challenge DanKS. No installation required.
One human player, one AI teammate, and two AI opponents. Play through your browser with a Chinese or English interface.
Watch a short gameplay preview
Full-size table preview · Social preview
Preserve useful options. Learn when to use them.
- Understand the position. Encode the visible hand, public history, legal actions, and team context.
- Look beyond the current play. Apply candidate actions and examine the combinations left in the residual hand.
- Choose from a compact, meaningful set. Rank structured candidates with a state- and candidate-conditioned Actor-Critic.
- Learn from what happens next. PPO and GAE connect a decision to its later consequences.
Retrieval identifies useful options; the learned policy decides which option fits the moment.
Same hand. Three choices. Different futures. Click to explore the decision example.
| AI generation | Focus | Explore |
|---|---|---|
| V1 | Structural retrieval and a NumPy candidate selector | Retrieval ranker |
| V2 | Expanded candidate generation and an ONNX selector | Action generator |
| V3 | Memory-aware neural selection, team-belief features, and PPO learning | Policy network · PPO training |
| V3Pro | V3 inference refinement: asset protection, equivalent-play rules, and verified endgame search | Policy · Integration guide |
V3Pro extends V3 as the separate DanKSPro package. It refines inference without replacing the network or retraining it. Endgame refinement covers admitted positions with at most 16 remaining cards across the table; for 11–16 cards, hidden-card allocations are additionally capped at 128.
The AI is only half the experience. Now the table is open source, too.
Both Service generations include the browser frontend, room backend, GuanDan referee, full source-built hand arrangement, and a standard external AI interface.
- Service V1 — the original table. A compact starting point with the classic CardKS experience.
- Service V2 — the redesigned table. A fixed-aspect desktop and mobile-landscape layout, modular interactions, and improved session recovery.
Explore Service V1 → · Explore Service V2 →
The service versions describe the web platform, independently of the AI generations. Both run locally with example rule-based bots; connect your own model through the HTTP AI interface. Trained weights and private AI serving infrastructure are not included.
| Your idea | Start here |
|---|---|
| Redesign the table or card interactions | V2 frontend |
| Extend rooms, game flow, or realtime updates | V2 backend |
| Customize hand arrangement | Go arranger |
| Connect a new AI | AI request/response contract |
| Find the right module to change | Service development guide |
Study complete matches, not just isolated moves.
The public KSCB GuanDan dataset, maintained in CardKS, contains 899 complete promotion matches, 10,218 rounds, and 840,194 decision points. Ordered round events make it useful for studying human decisions, partnership play, and hand structure over time.
Explore the data → · Source format →
The datasets/ directory links to the original release and provides download and reading examples. Data stays in CardKS; DanKS does not duplicate it. These are match records, not precomputed PPO inputs.
Use Python 3.12 and Go 1.23+. From a POSIX shell:
git clone https://github.com/Calix-L/DanKS.git
cd DanKS/services/v2
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
python scripts/build_arranger.py
python run.pyOpen http://127.0.0.1:8000/solo, create a table, and click Ready. Three example bots fill the other seats. Ctrl+C stops the service.
On Windows PowerShell, create the environment with py -3.12 -m venv .venv and activate it with .venv\Scripts\Activate.ps1. To use the original table, choose services/v1 instead.
In a separate Python 3.11+ environment, run from the repository root:
python3.11 -m venv .venv-ai
source .venv-ai/bin/activate
python -m pip install -e . -e versions/v3 -e versions/v3pro
python -m pip install torch==2.8.0
python examples/retrieval_quickstart.py --version v3
python examples/v3_model_smoke.py
python examples/v3pro_smoke.pyThe examples exercise retrieval, network inference, and V3Pro integration using synthetic inputs; model smoke runs use random initialization. For CPU/CUDA/NPU setup, V1/V2 installation, and PPO learner commands, follow the developer guide. For optional Linux/macOS C++ retrieval acceleration, run danks-build-native in your V3 environment.
DanKS/
├── versions/ # AI: V1, V2, V3, and the V3Pro extension
├── services/
│ ├── v1/ # KSPlay GuanDan Service · original table
│ └── v2/ # KSPlay GuanDan Service · redesigned table
├── guandan/engine/ # Shared AI-side GuanDan rules engine
├── examples/ # Executable engine, retrieval, model, and PPO examples
├── datasets/ # Public GuanDan data links and reading guide
├── assets/ # Brand, gameplay preview, and architecture illustrations
└── .github/ # Contribution and developer guides, CI
Each Service is independently runnable and keeps its own rules and hand-arrangement modules. The AI generations remain separate packages; use a dedicated environment for each generation.
Build a new agent. Create a better table. Explore a new idea in partnership play.
Contributions to algorithms, UI, portability, and documentation are welcome. Start with the contribution guide or open an issue.
Repositories: GitHub · AtomGit mirror
Research: CardKS
License: Apache-2.0 · Notices