marvinandClaude Opus 5.5
87dfcd55b7
Running games always continue; new work is refused when there is no
capacity; abuse is rejected immediately.
- Docker: CPU/memory/pids caps per container (overridable in .env);
uvicorn message size, queue and concurrency limits
- Server abuse limits: connections total/per IP (close 1013), message
rate per connection (token bucket, close 1008 on floods), lobby
creation rate per IP
- Load monitor: event loop lag, CPU of the own cgroup quota and
throttling, with hysteresis (normal/busy/overloaded, severe lag
overloads immediately)
- Admission control: create_lobby and start_game refused while
overloaded or MAX_ACTIVE_GAMES are running; server_status message
broadcast on changes; /health reports load but stays ok
- AI bridge: timeout sent with requests, in-flight cap, circuit breaker
- AI service: bounded workers and queue ("overloaded"), drops expired
requests, degrades to the heuristic agent when busy or too slow
- Website: busy notices, Create/Start disabled when not accepting
games, slow retry when the server is full
- Tests for limiters, load levels, admission control and AI service
limits; docs updated
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
wizard_ai
AI player for the Wizard server. The actual RL model is still to be implemented; this package provides everything around it.
agents/—Agentinterface (act(observation) -> action) plusRandomAgent,HeuristicAgent(baseline / fallback) andModelAgent(wraps aPolicy)model/encoding.py— observation → fixed-size feature vector (FEATURE_SIZE), and the fixed action space (21 bids + 60 cards) with legal-action masks; works for 3–6 playersmodel/policy.py— the place to plug in the model: subclassPolicy, implementselect,update,save_weights/load, register with@register_policy.UniformPolicyis a placeholder so the pipeline runs end to end.training/env.py— turn-based multi-agent environment aroundwizard_game(reward = score change per round / 100)training/self_play.py— self-play loop collecting per-player trajectories and callingPolicy.update, with checkpointingtraining/evaluate.py— play agents against each other (mean score, win rate)service/client.py— production: connects tows://wizard_server:8083/internal/ai, answersdeciderequests, reconnects automatically
pip install -r requirements-dev.txt
python -m wizard_ai train --episodes 5000 --out models/latest
python -m wizard_ai evaluate --agents model,heuristic,random,random --games 200
python -m wizard_ai serve # env: SERVER_URL, AI_SHARED_SECRET, AGENT, MODEL_PATH
pytest