HF_BLOG_POST.md

Every industrial building wastes 20–30% of its energy because control systems can't handle real-time pricing, equipment faults, and grid stress simultaneously. GridMind-RL is an OpenEnv-compatible RL environment that makes LLMs trainable on this problem.

The Problem

Industrial buildings consume ~40% of global electricity. Most still use naive "always-on" HVAC policies. The capability gap is clear:

• LLMs can understand complex pricing curves, fault alerts, and natural language instructions
• But no environment exists to train them on real building energy management
• Existing RL environments are mostly grid-worlds or toy games — not genuine industrial problems

GridMind-RL closes this gap by simulating a complete building energy system where agents must:

• Navigate 24-hour price volatility (off-peak vs peak: 4¢ to 32¢/kWh)
• Maintain comfort (19–23°C) while minimizing cost
• Respond to grid stress emergencies
• Handle equipment faults (chiller failure, sensor malfunction, grid outages, tariff spikes)
• Parse and follow natural language objective cards

The Environment

GridMind-RL is a 96-step episode (24 simulated hours at 15-minute resolution) with:

Field	Value
Observation	13 fields: temperature, storage, price, stress, carbon, faults, HVAC efficiency, instruction card
Actions	HVAC level (0–1), thermal charge (−1 to 1), batch slot (0–4), load shed (0–0.5)
Reward	9-component weighted sum: cost, temperature, grid, deadline, efficiency, stability, carbon, instruction, fault_mitigation
Tasks	4 types: cost minimization, temperature management, demand response, instruction following

Four Hackathon Themes in One Environment

Track 1 — Multi-Agent Interactions: A coordinator LLM reads /feeder to see fleet-wide demand across 3 buildings, then sets per-building price multipliers via /coordinate to orchestrate behavior.

Track 2 — Long-Horizon Planning & Instruction Following: Task 4 presents a natural language objective card like "Keep total energy cost under $2.50 while maintaining 19–23°C." Agents must plan across all 96 steps.

Track 3 — World Modeling: The /simulate endpoint lets agents ask "what if?" before acting. When HVAC efficiency is low or faults are active, the agent simulates the proposed action and revises if the predicted reward is poor.

Track 4 — Fault Handling: Four fault types inject unpredictability:

• Chiller failure: HVAC drops to 20% capacity
• Grid outage: Price ×3, stress = 1.0
• Sensor fault: Temperature readings jitter ±5°C
• Tariff spike: Emergency 4× price surge

Track 5 — Self-Improvement: Curriculum learning auto-advances the agent from task 1 to task 4 when performance thresholds are met.

Results

Heuristic baseline scores (fixed policy, no learning) across all 4 tasks:

Policy	Task 1	Task 2	Task 3	Task 4
Heuristic Baseline	0.506	0.459	0.600	0.492

The GRPO fine-tuned model shows improvement over the zero-shot LLM baseline. The training curve below shows the learning trajectory:

Training

GridMind-RL uses GRPO (Group Relative Policy Optimization) via HuggingFace TRL with Unsloth 4-bit LoRA fine-tuning of Qwen2.5-0.5B-Instruct. The training script connects to the live environment via HTTP, running 8-step rollouts and using the /grade endpoint (episode-level score 0.0–1.0) as the primary reward signal.

# Training runs against the live environment
python scripts/train_unsloth.py --steps 500 --output-csv results/training_log.csv

Or run the Colab notebook: gridmind_grpo_colab.ipynb

How to Try It

# Quick health check
curl https://prajwal782007-gridmind.hf.space/health

# Run a heuristic baseline
python inference.py --fast-mode --task 3 --episodes 5

# Run the LLM agent
python inference.py --task 3 --episodes 5

Live environment: https://prajwal782007-gridmind.hf.space
Dashboard: https://prajwal782007-gridmind.hf.space/dashboard

Code: github.com/LO-Kyu/gridmind

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GridMind-RL was built for the Meta PyTorch OpenEnv Hackathon Grand Finale, April 25–26, 2026, at Scaler School of Technology, Bangalore.