| # SolarChain-Eval |
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| SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets. |
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| This repository is an independent benchmark implementation for evaluating autonomous economic governors in decentralized energy markets. It does not modify `SolarSave`; any reusable prototype logic or data from that project is copied here first and evolved inside this repository. |
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| ## Setup |
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| ```powershell |
| conda activate SolarChain-rl |
| pip install -e . |
| ``` |
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| ## Quick Smoke Run |
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| ```powershell |
| python scripts\evaluate.py --policies "static,random,myopic" --episodes 1 |
| python scripts\make_figures.py --run-dir outputs\runs\<timestamp>_eval |
| ``` |
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| ## Full Baselines |
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| ```powershell |
| python scripts\run_all_baselines.py --timesteps 2048 --episodes 2 |
| ``` |
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| For longer paper runs, increase `--timesteps` and `--episodes`. |
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| ## Linux Paper Pipeline |
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| For a Linux machine, use the bash workflow in `paper_pipeline/`: |
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| ```bash |
| bash paper_pipeline/00_setup_linux.sh |
| bash paper_pipeline/01_smoke_check.sh |
| PAPER_RUN_ID=paper_final TIMESTEPS=300000 EPISODES=20 bash paper_pipeline/02_run_paper_experiments.sh |
| ``` |
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| Each invocation of `02_run_paper_experiments.sh` creates a separate `outputs/paper_runs/<paper_run_id>/` directory with metadata, run pointers, figures, and a `PAPER_RESULTS.md` manifest. See `paper_pipeline/README.md` for the full process and the mapping from output files to paper tables and figures. |
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| ## Outputs |
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| Single-model training writes timestamped directories by default: |
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| ```text |
| outputs/runs/<timestamp>_ppo_train/ppo_model.zip |
| outputs/runs/<timestamp>_sac_train/sac_model.zip |
| outputs/runs/<timestamp>_dqn_train/dqn_model.zip |
| ``` |
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| Use `--no-timestamp --run-name <name>` only when you intentionally want a stable output path. |
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| `run_all_baselines.py` writes a complete timestamped benchmark bundle: |
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| ```text |
| outputs/runs/<timestamp>/ |
| ``` |
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| Each evaluation bundle writes: |
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| - `metrics.csv` |
| - `summary.json` |
| - `actions.csv` |
| - `city_hour_policy.csv` |
| - `config_snapshot.json` |
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| Key reported metrics include `physics_violation_rate`, liquidity `max_drawdown`, `action_jitter`, `slippage_reduction_vs_static`, `spatial_fairness_index`, and `artificial_liquidity_MWh`. |
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| The figure script writes: |
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| - `learning_curves.png` |
| - `safety_utility_frontier.png` |
| - `city_hour_liquidity_heatmap.png` |
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| In the end, this repository produces reproducible benchmark evidence for the paper: trained RL policies, six-policy comparison metrics, trustworthiness metrics, ablation-ready runs with `--no-physics-penalty`, and the three core workshop figures. |
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| The `--no-physics-penalty` ablation keeps logging unsafe backed supply while removing the reward penalty. This is intended to expose whether a policy exploits rejected or above-`P_max` generation to create artificial liquidity. |
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| See `IMPLEMENTATION_PLAN.md` for the Chinese implementation plan and experiment scope. |
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