SweetGold v1.0.0

SweetGold v1.0.0

SweetGold v1.0.0 is a reproducible, interactive multi-agent AI experimentation
platform. It connects deterministic simulation, rule baselines, imitation
learning, reinforcement learning, model promotion, failure audits and a
side-by-side product experience.

Highlights

• Compare two strategies from the same initial world and stochastic seed.
• Replay every server-side frame without changing the live match.
• Train BC, PPO and CTDE policies through configuration-driven pipelines.
• Reject seed leakage before experiments begin.
• Promote models only when confidence, yield and safety gates pass.
• Preserve failed robustness results in a structured audit registry.
• Run the simulation and rule baselines without third-party dependencies.

Start the Strategy Arena

python3 main.py play --port 8080

Open http://127.0.0.1:8080.

To enable locally generated learned strategies:

python3 -m venv .venv-ml
.venv-ml/bin/pip install -r requirements-ml.txt
.venv-ml/bin/python main.py pipeline-m8 \
  --config experiments/m8-coordination.json

The arena verifies registered checkpoint hashes before loading them.

What v1.0 claims

SweetGold demonstrates an end-to-end multi-agent experimentation workflow and
a scientifically honest promotion process. The accepted M8 model improved over
its local behavior-cloning baseline while eliminating resource-contention
invalid actions.

What v1.0 does not claim

The learned policy is not a generally robust agent. M10 and M11 found severe
survival degradation under harsh weather and reduced energy. Those candidates
were rejected, their results remain visible in registry/audits.json, and
their final seeds must not be reused for tuning.

The built-in server is intended for local experiments and demonstrations. It
does not provide authentication, multi-user isolation or production service
guarantees.

Verification

python3 main.py --version
python3 -m unittest discover -s tests -v
python3 main.py benchmark --episodes 30 --controllers greedy assignment

Optional ML smoke pipelines are executed by GitHub Actions.

Upgrade notes

This is the first versioned release. Generated runs/, models/ and data/
directories remain intentionally excluded from Git.