docs/README_FairRecovery.md

🏙️ FairRecovery++ — Post-Disaster City Recovery RL Environment

Train an LLM to make fair disaster recovery decisions — where helping wealthy zones first systematically abandons the most vulnerable people.

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🌊 The Problem

After disasters like the 2022 Bengaluru floods and 2023 Chennai floods, city authorities must allocate scarce resources (medical units, power crews, water tankers) across many damaged neighborhoods simultaneously — under tight budgets and time pressure.

The trap every naive AI falls into: optimising for speed means fixing the easiest zones first, which are almost always the wealthiest. Zone 0 (wealthy district, moderate damage) is faster to restore than Zone 4 (informal settlement, 92% damage, 96% vulnerable population). A greedy agent picks Zone 0 every time — and Zone 4 stays dark for days.

FairRecovery++ is an OpenEnv RL environment that teaches LLM agents to escape this trap: learn to jointly optimize service restoration and equitable distribution across vulnerable populations.

Primary Theme: 3.1 — Real-World Professional Tasks
Secondary Theme: 2 — Long-Horizon Planning & Instruction Following

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🎯 The Fairness Trap (Hard Scenario)

Zone	Damage	Service	Vulnerable Pop	Priority?
Zone 0 (wealthy)	35%	65%	8%	❌ Easy but low need
Zone 1	50%	50%	40%	Medium
Zone 2	60%	40%	55%	Medium
Zone 3 (poor)	72%	28%	72%	High
Zone 4 (informal)	92%	8%	96%	✅ Must prioritize

A greedy agent always picks Zone 0 (quick ROI, easy reward). A fairness-aware agent learns to prioritize Zone 4 despite lower immediate returns — because that's where 96% of the population is vulnerable.

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🏗️ Architecture

LLM Agent (GRPO trained)
        │
        ▼ FairRecoveryAction
┌───────────────────────────────┐
│  Safety Shield (shield.py)    │  ← blocks invalid actions before mutation
│  Stage validator              │
│  Budget enforcer              │
└──────────────┬────────────────┘
               │ valid action
        ▼
┌───────────────────────────────┐
│  FairRecoveryEnvironment      │  ← core OpenEnv Environment class
│  Multi-step protocol:         │
│  analyze → allocate →         │
│  execute → (×MAX_DAYS) →      │
│  submit                       │
└──────────────┬────────────────┘
               │ updated CityState
        ▼
┌───────────────────────────────┐
│  Reward Engine (RLVR)         │  ← no learned reward model
│  R_exec  (service improvement)│
│  R_fair  (disparity reduction)│
│  R_safe  (constraint penalty) │
└──────────────┬────────────────┘
               │ per-step reward
        ▼
┌───────────────────────────────┐
│  Composable Rubrics (RFC 004) │  ← FairnessRubric + UtilityRubric
│  Terminal episode scoring     │     + AnalysisRubric
│  Grader score ∈ (0.01, 0.99)  │
└───────────────────────────────┘

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🎮 What the Agent Sees, Does, and Gets Rewarded For

Observation (per step)

{
  "zones": [
    {"zone_id": 4, "damage": 0.92, "service": 0.08, "vulnerable_ratio": 0.96}
  ],
  "day": 2,
  "budget_left": 25.0,
  "step_stage": "allocate",
  "fairness_score": -0.61,
  "cumulative_reward": 0.142
}

Action (multi-step protocol — not just one choice)

// Step 1: analyze
{"action_type": "analyze", "critical_zones": [3, 4], "reasoning": "highest damage × vulnerability"}

// Step 2: allocate
{"action_type": "allocate", "allocations": [
  {"zone": 4, "resource": "medical"},
  {"zone": 3, "resource": "power"}
]}

// Step 3: execute (commits allocations, receives dense reward)
{"action_type": "execute"}

// After MAX_DAYS: submit (receives terminal bonus)
{"action_type": "submit"}

Reward System (RLVR — all verifiable, no learned model)

Component	Formula	Weight	What it teaches
R_exec	Avg service improvement this step	1.0	Restore services efficiently
R_fair	−(avg_service_normal − avg_service_vulnerable)	0.5	Don't leave vulnerable zones behind
R_safe	−0.1 × violations	0.5	Respect constraints
R_analysis	Overlap(chosen, top-k by damage×vuln)	0.1	Correctly identify critical zones
Terminal bonus	0.5×avg_svc + 0.5×(1+R_fair)	—	Long-horizon outcome

Grader score: 0.6 × avg_service + 0.4 × (1 + fairness) clamped to (0.01, 0.99)

Anti-Reward-Hacking Measures

• Stage ordering enforced by shield (can't skip analyze → go straight to execute)
• Budget overflow: allocations rejected + penalty, state NOT mutated
• Persistent ignore penalty: if vulnerable zones receive 0 resources for 2+ consecutive days
• Early-submit blocked until MIN_STEPS reached
• Step cap: force-terminate at MAX_STEPS_SAFETY_CAP

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📊 Training Results

Reward: Baseline vs Trained Agent

Bar chart: Avg Curriculum Reward, Avg Final Utility, Avg Final Fairness — baseline (grey) vs Sarvam-105B trained (blue) across 32 episodes.

Per-Episode Reward Heatmap

Each column is one episode. Trained agent (bottom row) shows consistently warmer (higher) rewards, especially in later episodes.

Reward Curve Over Training

4-episode moving average. Trained agent steadily improves above greedy baseline.

Key Numbers

Metric	Greedy Baseline	Sarvam-105B Trained	Δ
Avg Episode Reward	0.549	0.602	+9.8%
Avg Final Fairness	0.537	0.539	+0.4%
Strategy discovered	Always Zone 0	Medical-first equity	—

The trained agent spontaneously discovered a "medical-first equity" strategy: prioritize Zone 4 with medical resources (highest service impact for most vulnerable) before addressing easier zones.

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🚀 Quick Start

git clone https://github.com/joshua400/FairRecovery-PlusPlus
cd FairRecovery-PlusPlus
pip install -r requirements.txt
uvicorn server.app:app --reload

# Verify environment
curl http://localhost:8000/health

# Run a full episode
python inference.py --difficulty hard --episodes 3 --policy fairness_aware

Use as OpenEnv client

from client import FairRecoveryEnv

env = FairRecoveryEnv(base_url="https://Joshua1702-FairRecovery-PlusPlus.hf.space")
obs = env.reset(difficulty="hard")

for day in range(5):
    action = your_policy(obs)      # analyze → allocate → execute
    obs = env.step(action)
    print(f"Day {obs.day}: reward={obs.reward:+.3f} fair={obs.fairness_score:.3f}")

Run training (Colab)

Open train_COMPLETE.ipynb — runs on free Colab T4 in ~10 minutes.

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📁 Project Structure

fairrecovery_env/
├── constants.py          # REWARD_WEIGHTS, RESOURCE_COSTS, thresholds
├── models.py             # Pydantic v2 Action / Observation / State
├── state.py              # CityState + ZoneState (mutable world model)
├── tasks.py              # 3 scenarios: easy / medium / hard (fairness trap)
├── rewards.py            # 5-component RLVR reward engine (pure functions)
├── rubrics.py            # RFC 004 composable rubrics
└── shield.py             # Safety validator (blocks before mutation)

server/
├── fairrecovery_environment.py   # OpenEnv Environment class
└── app.py                        # FastAPI + OpenEnv integration

client.py                 # Typed HTTP client
inference.py              # Baseline policies (greedy, fairness-aware, random)
train_COMPLETE.ipynb      # GRPO training notebook (TRL + Unsloth)
generate_summary_plots.py # Reproduce all plots from episode_log.csv

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🔗 Materials

Resource	Link
🤗 Live Environment (HF Space)	https://huggingface.co/spaces/Joshua1702/FairRecovery-PlusPlus
💻 GitHub	https://github.com/joshua400/FairRecovery-PlusPlus
📓 Training Notebook	train_COMPLETE.ipynb (Colab-ready)
📝 HF Blog Post	[ADD LINK AFTER PUBLISHING]
🎥 Demo Video	[ADD LINK AFTER RECORDING]

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🌍 Why It Matters

Post-disaster recovery planning is a $200B/year global challenge. AI systems that optimize only for speed or total utility systematically disadvantage the most vulnerable populations — the elderly, disabled, and low-income communities who live in the hardest-hit zones.

FairRecovery++ is the first OpenEnv environment to encode intersectional fairness as a verifiable, first-class RL objective, making it a research-grade benchmark for safe and fair LLM agent training. The environment can be extended to vaccination rollout, hospital resource allocation, or any domain where efficiency vs equity trade-offs matter.

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OpenEnv Compliance Checklist

• ✅ openenv.yaml manifest present
• ✅ Environment base class used with try/import fallback
• ✅ reset() / step() / state() standard API
• ✅ Pydantic v2 typed Action / Observation / State
• ✅ Hosted on HF Spaces (Docker)
• ✅ GRPO training with TRL + Unsloth (see train_COMPLETE.ipynb)
• ✅ Training evidence: plots in assets/ and episode data in episode_log.csv
• ✅ Composable rubrics (OpenEnv RFC 004)
• ✅ Anti-reward-hacking: shield + stage gates + persistent ignore penalty

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Built for the Meta PyTorch OpenEnv Hackathon India 2026.