Datasets:
File size: 2,879 Bytes
4bd5225 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # SolarChain-Eval
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets.
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.
## Setup
```powershell
conda activate SolarChain-rl
pip install -e .
```
## Quick Smoke Run
```powershell
python scripts\evaluate.py --policies "static,random,myopic" --episodes 1
python scripts\make_figures.py --run-dir outputs\runs\<timestamp>_eval
```
## Full Baselines
```powershell
python scripts\run_all_baselines.py --timesteps 2048 --episodes 2
```
For longer paper runs, increase `--timesteps` and `--episodes`.
## Linux Paper Pipeline
For a Linux machine, use the bash workflow in `paper_pipeline/`:
```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
```
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.
## Outputs
Single-model training writes timestamped directories by default:
```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
```
Use `--no-timestamp --run-name <name>` only when you intentionally want a stable output path.
`run_all_baselines.py` writes a complete timestamped benchmark bundle:
```text
outputs/runs/<timestamp>/
```
Each evaluation bundle writes:
- `metrics.csv`
- `summary.json`
- `actions.csv`
- `city_hour_policy.csv`
- `config_snapshot.json`
Key reported metrics include `physics_violation_rate`, liquidity `max_drawdown`, `action_jitter`, `slippage_reduction_vs_static`, `spatial_fairness_index`, and `artificial_liquidity_MWh`.
The figure script writes:
- `learning_curves.png`
- `safety_utility_frontier.png`
- `city_hour_liquidity_heatmap.png`
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.
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.
See `IMPLEMENTATION_PLAN.md` for the Chinese implementation plan and experiment scope.
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