marvinandClaude Opus 5.5
9ffee8b952
- New docs/09-limits-and-performance.md: principles, all protection layers, what players see, configuration reference, measurements, tuning guide, monitoring and how to test - Server chapter summarises the limits and links to chapter 9; circuit breaker description and AI_MAX_INFLIGHT default corrected - Architecture: new files in the layout, design decisions for load handling; docs index lists chapter 9 - Protocol, AI and operations chapters link to chapter 9; test counts - Root and component READMEs describe the limits and settings 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. Work is bounded:AI_WORKERS(1) parallel decisions, at mostAI_MAX_QUEUE(64) requests in progress (more are answered "overloaded"), expired requests are dropped, and fromAI_DEGRADE_QUEUE(8) waiting requests — or when the model is too slow — the heuristic decides instead of the model (see../docs/09-limits-and-performance.md)
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, AI_WORKERS, AI_MAX_QUEUE, AI_DEGRADE_QUEUE
pytest