Datasets:

global-nomad-nexus
/

solar-rl

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

conda activate SolarChain-rl
pip install -e .

Quick Smoke Run

python scripts\evaluate.py --policies "static,random,myopic" --episodes 1
python scripts\make_figures.py --run-dir outputs\runs\<timestamp>_eval

Full Baselines

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 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:

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:

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.