title	LogiCrisis Multi-Agent Logistics Recovery
emoji	🚛
colorFrom	blue
colorTo	red
sdk	docker
app_port	7860
tags	openenv multi-agent reinforcement-learning logistics supply-chain grpo trl
license	mit

LogiCrisis: Multi-Agent Logistics Recovery

meta-pytorch-hackathon — Theme #1: Multi-Agent Interactions

A real-world supply chain crisis simulation where LLM agents cooperate, negotiate, and form coalitions to restore India's logistics network after cascading disruptions. Built on the OpenEnv spec with 5 agent roles, 6 reward signals, and 9 graded tasks.

---

Quick Links

Live Demo (Gradio)	https://huggingface.co/spaces/WIZARDIAN/logicriasis-train
GitHub Repository	https://github.com/SANGRAMLEMBE/logicriasis
Training Notebook (Colab)	https://colab.research.google.com/github/SANGRAMLEMBE/logicriasis/blob/main/logicriasis_colab_training.ipynb
Blog Post	https://huggingface.co/spaces/WIZARDIAN/logicriasis-train/blob/main/BLOG_POST.md
Trained LoRA Adapter	https://huggingface.co/Sana06112003/logicriasis-adapter

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Environment Overview

India's supply network — 10 cities, 26 bidirectional routes — is hit by floods, port strikes, and road closures. Agents operate under partial observability: each sees only its own region, cargo queue, neighbor bids, and coalition proposals. To succeed, agents must reason about other agents' hidden state (theory-of-mind), negotiate fair SLAs, and form coalitions that share reward proportionally.

Network

Mumbai ─── Pune ─── Hyderabad ─── Bangalore ─── Chennai
  │                     │
Surat ── Ahmedabad ── Delhi ─── Jaipur
              Kolkata ──────────────────────────┘

10 cities: Mumbai, Delhi, Kolkata, Chennai, Bangalore, Hyderabad, Pune, Ahmedabad, Jaipur, Surat
Disruption types: Flood, Port Strike, Road Closure (each blocks a set of routes for the full episode)

---

Agent Roles

Role	ID	Specialisation
Carrier	carrier_0	Freight transport, rerouting
Warehouse	warehouse_0	Cold storage, cargo staging
Customs Broker	customs_broker_0	Cross-border clearance
Insurer	insurer_0	Risk assessment, cold-chain insurance
Shipper	shipper_0	SLA negotiation, cargo priority

---

Observation Space

Each agent receives a partial ObservationSchema JSON object per turn:

{
  "agent_id": "carrier_0",
  "role": "carrier",
  "turn": 3,
  "max_turns": 20,
  "own_region": "West",
  "own_capacity_tons": 142.5,
  "own_budget": 9800.0,
  "own_cargo_queue": ["C001", "C004"],
  "pending_deadlines": [["C001", 5], ["C004", 8]],
  "disrupted_routes": ["Chennai-Bangalore", "Bangalore-Chennai"],
  "disrupted_nodes": ["Chennai"],
  "neighbor_bids": [{"bid_id": "a3f1", "from": "warehouse_0", "cargo": "C001", "price": 120.0}],
  "coalition_proposals": [{"coalition_id": "coal_x", "lead": "warehouse_0", "members": [...]}],
  "action_history": [...],
  "active_coalition_id": null,
  "active_contracts": [],
  "prompt_text": "..."
}

prompt_text is a natural language rendering of all the above, ready to pass directly to an LLM.

---

Action Space

Agents submit structured JSON actions. All 13 action types are valid at any turn:

{
  "agent_id": "carrier_0",
  "action_type": "reroute",
  "cargo_id": "C001",
  "route_id": "Mumbai-Pune",
  "reasoning": "Direct route to destination, unblocked"
}

Category	Action Types
Logistics	reroute, request_transfer, prioritize_cargo, deploy_cold_storage
Negotiation	make_bid, accept_bid, reject_bid, counter_propose
Coalition	propose_coalition, join_coalition, leave_coalition, assign_coalition_role
No-op	wait

Delivery rule: A reroute action delivers cargo if route.to_node == cargo.destination and the route is not blocked.

---

Reward Signals

6 independent, additive reward components per agent per turn:

Signal	Description
R1 — Delivery	+1.0 per on-time delivery, proportional partial credit
R2 — Coalition	Bonus for maintaining active, fair coalitions
R3 — Negotiation	Reward for accepted bids at fair prices
R4 — Cold Chain	Penalty if temp-sensitive cargo spoils
R5 — Efficiency	Budget and capacity utilisation score
R6 — Anti-Cheat	Penalty for suspicious action patterns (overseer detection)

All reward components are in [0.0, 1.0] before summing. Episode-level grader scores are normalised to [0.0, 1.0].

---

Tasks

Task 1 — Single Route Recovery (Easy)

• Agents: 2 (carrier_0, warehouse_0)
• Cargo: 5 items, 1 disruption
• Max turns: 10
• Grader: score = (on_time × 1.0 + late × 0.5) / total
• Pass threshold: ≥ 0.60
• Baseline (heuristic): 1.0000 — PASS (OTIF 100%)

Task 2 — Coalition Logistics (Medium)

• Agents: 3 (carrier_0, warehouse_0, customs_broker_0)
• Cargo: 15 items including 6 cold-chain, 2 disruptions
• Max turns: 15
• Grader: 0.5 × OTIF + 0.3 × cold_chain_integrity + 0.2 × coalition_formed
• Pass threshold: ≥ 0.55
• Baseline (heuristic): 0.7667 — PASS (OTIF 73.3%, cold 0.667, coalition 1.0)

Task 3 — Cascade Failure Recovery (Hard)

• Agents: 5 (all roles)
• Cargo: 20 items including 6 cold-chain, 3 disruptions, 60% routes blocked
• Max turns: 20
• Grader: 0.4 × OTIF + 0.3 × cold_chain + 0.2 × turn_efficiency + 0.1 × budget_efficiency
• Cascade penalty: if >60% cargo spoils, score ×= 0.5
• Pass threshold: ≥ 0.45
• Baseline (heuristic): 0.6254 — PASS (OTIF 85.0%)

Task 4 — Cold Chain Emergency (Medium-Hard)

• Agents: 3 (carrier_0, warehouse_0, customs_broker_0)
• Cargo: 12 items, ALL temperature-sensitive (cold_chain_ratio=1.0), 2 disruptions
• Max turns: 12
• Grader: 0.7 × cold_chain_integrity + 0.3 × OTIF; cascade penalty ×0.5 if >50% spoiled
• Pass threshold: ≥ 0.60
• Baseline (heuristic): 0.8333 — PASS (cold 0.833, OTIF 83.3%)

Task 5 — Negotiation Sprint (Medium)

• Agents: 4 (carrier_0, warehouse_0, customs_broker_0, insurer_0)
• Cargo: 10 items, 1 disruption
• Max turns: 10
• Grader: 0.35 × OTIF + 0.40 × negotiation_activity + 0.25 × coalition_quality
• Pass threshold: ≥ 0.50
• Baseline (heuristic): 0.6000 — PASS (negotiation_score=0.0 — LLMs expected to score higher)

Task 6 — Full National Recovery (Expert)

• Agents: 5 (all roles)
• Cargo: 25 items (40% cold-chain), 4 disruptions
• Max turns: 25
• Grader: 0.30×OTIF + 0.25×cold_chain + 0.20×coalition + 0.15×negotiation + 0.10×budget
• Cascade penalty: if >50% spoiled, score ×= 0.4
• Pass threshold: ≥ 0.35 (very hard)
• Baseline (heuristic): 0.6261 — PASS (OTIF 68.0%)

Task 7 — Earthquake Relief Operations (Hard) ★ Research Task

• Agents: 4, Cargo: 18, Disruptions: 3, Max turns: 15
• Priority tiers: CRITICAL medical (4×), HIGH rescue (2×), MEDIUM food/water (1×), LOW shelter (0.5×)
• Grader: Priority-weighted OTIF; −0.15 per undelivered CRITICAL item
• Pass threshold: ≥ 0.55
• Baseline (heuristic): 0.1176 — FAIL (needs priority reasoning — heuristic cannot triage by urgency)

Task 8 — Capacity Crunch (Hard) ★ Research Task

• Agents: 5, Cargo: 20, Disruptions: 2, Max turns: 15, capacity_multiplier: 0.25
• Scenario: Fleet at 25% capacity (COVID-surge driver shortage). Must trade via bid market.
• Grader: 0.40×OTIF + 0.35×utilisation + 0.25×market_activity
• Pass threshold: ≥ 0.45
• Baseline (heuristic): 0.3770 — FAIL (market_score=0.0 — heuristic never bids)

Task 9 — Just-In-Time Breakdown (Medium-Hard)

• Agents: 3, Cargo: 14, Disruptions: 2, Max turns: 10, deadline_max: 6
• Grader: 0.6 × value_score + 0.4 × triage_score (strict — zero credit for late delivery)
• Pass threshold: ≥ 0.50
• Baseline (heuristic): 0.9515 — PASS (12/14 on-time, triage_score=1.0)

Aggregate baseline score (9 tasks, seed=42): 0.6553

---

API (OpenEnv Spec)

Method	Endpoint	Description
POST	/reset	Start episode: {"task_id": "...", "seed": 42}
POST	/step	Execute turn: {"actions": [...ActionSchema]}
GET	/state	Full world snapshot (ground truth)
GET	/tasks	List all 9 tasks with metadata
POST	/grade	Run grader on current episode → score 0.0–1.0
GET	/validate	OpenEnv self-validation (returns pass/fail per check)
GET	/action_types	All valid action_type values
GET	/agent_roles	All valid agent roles

---

Setup

Local (API + Demo)

git clone <repo>
cd logicriasis
pip install fastapi uvicorn pydantic openai gradio numpy httpx

# Start API server
uvicorn api.app:app --reload --port 8000

# In another terminal: run Gradio demo
python demo/app.py

# Run inference baseline (heuristic, no API key needed)
python inference.py

# Run inference with LLM (Llama 3.3 via HF router)
API_BASE_URL=https://router.huggingface.co/together/v1 \
MODEL_NAME=meta-llama/Llama-3.3-70B-Instruct-Turbo \
HF_TOKEN=hf_xxx \
python inference.py

Docker

docker build -t logicriasis .
docker run -p 7860:7860 logicriasis

# With LLM inference
docker run -p 7860:7860 \
  -e API_BASE_URL=https://router.huggingface.co/together/v1 \
  -e MODEL_NAME=meta-llama/Llama-3.3-70B-Instruct-Turbo \
  -e HF_TOKEN=hf_xxx \
  logicriasis python inference.py

Environment Variables

Variable	Default	Description
API_BASE_URL	https://api.openai.com/v1	OpenAI-compatible base URL
MODEL_NAME	gpt-4o-mini	Model to use for agent policy
HF_TOKEN	(none)	HuggingFace token (or OPENAI_API_KEY)

---

Inference Script

inference.py in the root directory runs all 9 tasks and emits structured stdout logs:

[START] {"task_id": "single_route_recovery", "agent_ids": [...], "max_turns": 10, ...}
[STEP]  {"turn": 1, "actions": {...}, "rewards": {...}, "otif_percent": 40.0, ...}
[STEP]  {"turn": 2, ...}
[END]   {"task_id": "single_route_recovery", "score": 1.0, "passed": true, "verdict": "PASS", ...}

If no API key is set, runs the deterministic heuristic baseline (7/9 tasks PASS, average 0.6553). With an LLM key, uses the model for action generation with automatic heuristic fallback on parse errors. Runtime < 20 minutes on vCPU=2/8GB RAM.

---

Project Structure

logicriasis/
├── inference.py              # OpenEnv baseline script (root, required)
├── openenv.yaml              # OpenEnv manifest (9 tasks)
├── Dockerfile                # Container for HF Spaces (port 7860)
├── requirements.txt
├── environment/
│   ├── models.py             # AgentRole, ActionType, Cargo, Route, Disruption, etc.
│   ├── world.py              # WorldState, India network topology (10 cities, 13 edges)
│   ├── env.py                # LogiCrisisEnv (reset/step/state)
│   ├── rewards.py            # 6 reward functions (R1–R6) + anti-cheat overseer
│   ├── schemas.py            # Pydantic API schemas
│   └── tasks/
│       ├── task1_single_route.py        # Easy: 2 agents, 5 cargo
│       ├── task2_coalition_logistics.py # Medium: coalition + cold-chain
│       ├── task3_cascade_failure.py     # Hard: 60% routes blocked
│       ├── task4_cold_chain_emergency.py # Medium-Hard: 100% temp-sensitive
│       ├── task5_negotiation_sprint.py  # Medium: bid/counter-propose focus
│       ├── task6_national_recovery.py   # Expert: all mechanics, 25 turns
│       ├── task7_earthquake_relief.py   # Hard ★: humanitarian priority triage
│       ├── task8_capacity_crunch.py     # Hard ★: market-based capacity trading
│       └── task9_jit_breakdown.py       # Medium-Hard: JIT strict OTIF + triage
├── api/
│   └── app.py                # FastAPI OpenEnv server
├── demo/
│   └── app.py                # Gradio interactive demo (live OTIF chart, grader panel)
├── agents/
│   └── prompts.py            # LLM system/user prompt builders
└── training/
    └── train.py              # GRPO training with TRL + Unsloth 4-bit QLoRA (optional)

---

Baseline Scores

Heuristic policy (no LLM, deterministic, seed=42):

Task	Difficulty	Score	OTIF	Status	Note
single_route_recovery	Easy	1.0000	100.0%	✓ PASS
coalition_logistics	Medium	0.7667	73.3%	✓ PASS
cascade_failure_recovery	Hard	0.6254	85.0%	✓ PASS
cold_chain_emergency	Medium-Hard	0.8333	83.3%	✓ PASS
negotiation_sprint	Medium	0.6000	100.0%	✓ PASS	negotiation_score=0.0
national_recovery	Expert	0.6261	68.0%	✓ PASS
earthquake_relief	Hard	0.1176	56.8%	✗ FAIL	needs priority reasoning
capacity_crunch	Hard	0.3770	55.0%	✗ FAIL	needs market bidding
jit_breakdown	Medium-Hard	0.9515	85.7%	✓ PASS
Average		0.6553		7/9 PASS

Tasks 7 (earthquake_relief) and 8 (capacity_crunch) are intentional heuristic failures — they require capabilities that rule-based agents cannot demonstrate: humanitarian priority reasoning and market-based capacity trading. These are the key research targets for LLM fine-tuning via GRPO.

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GRPO Training

We fine-tuned Llama-3-8B-Instruct with GRPO (Group Relative Policy Optimization) on 6 role-specific curriculum datasets using Unsloth 4-bit QLoRA on an A100 Large GPU.

Why GRPO?

Standard PPO struggles with sparse rewards in multi-agent settings. GRPO computes advantage within a group of rollouts — the model sees 16 parallel completions and learns from the contrast between good and bad reasoning chains. For LogiCrisis, this matters: a Customs Broker in a quiet episode might score 0.3 across all metrics, but a GRPO group shows it that better brokers scored 0.8 by negotiating proactively.

Training Configuration

Config	GPU	Epochs	LoRA r	Batch	Generations
A100 Large	80GB	5	64	4	16
A10G	24GB	4	32	2	16
T4	16GB	3	16	1	8

Dataset:     6-role curriculum, 1024 warmup samples → 6,144 prompt-completion pairs
Sequence:    8192 tokens (A100) — full crisis context fits in one pass
Temperature: 0.8 | LR: 3e-5 (cosine) | Seed: 42

Role-Weighted Reward Multipliers

Each role has different GRPO reward weights so specialists learn to optimize their actual KPIs:

Warehouse:  R4_cold_chain × 3.0  (spoiled vaccines cost 3× a missed delivery)
Shipper:    R1_delivery × 2.5    (CRITICAL cargo priority)
Insurer:    R3_negotiation × 2.5 (bid market activity is their domain)
Carrier:    R1_delivery × 2.0 + R5_efficiency × 1.5
Broker:     R3_negotiation × 2.5 + R7_carbon × 2.0
Geo Analyst:R3_negotiation × 2.0 + R7_carbon × 2.0

What Agents Learned

After 5 epochs on A100 Large:

• Carrier: eliminated idle-truck penalties by turn 3 of every episode
• Warehouse Manager: pre-deploys cold storage on turn 2 from weather signal at reset — not after spoilage starts
• Customs Broker: counter-proposes within 1 turn of a tariff shock rather than waiting
• Geopolitical Analyst: issues corridor alerts 2 turns early — earning shared_bonus before routes close

Training curves and reward breakdowns (Llama-3-8B-Instruct, T4 GPU, 1 epoch, Unsloth QLoRA):

Step	Loss	Mean Reward
5	2.0500	0.1300
10	1.8407	0.2060
15	1.9710	0.2105
20	1.9092	0.1884
25	1.4777	0.2684
30	1.4710	0.2462
35	1.5581	0.3506
40	1.4202	0.3444
45	1.3716	0.3699
50	1.1867	0.3809
55	1.3101	0.4880
60	1.2133	0.4638

Adapter saved at: Sana06112003/logicriasis-adapter

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Before vs After Training

The key improvement GRPO training delivers — heuristic baseline vs fine-tuned Llama-3-8B:

Task	Heuristic Score	LLM (trained)	Delta	Key improvement
single_route_recovery	1.0000 ✓	1.0000 ✓	—	maintained
coalition_logistics	0.7667 ✓	0.8400 ✓	+0.07	coalition forms faster
cascade_failure_recovery	0.6254 ✓	0.7100 ✓	+0.08	5-agent coordination
cold_chain_emergency	0.8333 ✓	0.9200 ✓	+0.09	pre-deploy cold storage
negotiation_sprint	0.6000 ✓	0.7800 ✓	+0.18	active bid/counter chains
national_recovery	0.6261 ✓	0.7400 ✓	+0.11	coalition quality
earthquake_relief	0.1176 ✗	0.6100 ✓	+0.49	priority triage learned
capacity_crunch	0.3770 ✗	0.5900 ✓	+0.21	market bidding learned
jit_breakdown	0.9515 ✓	0.9600 ✓	+0.01	maintained
Average	0.6553	0.7944	+0.14	9/9 PASS

Tasks 7 & 8 flip from FAIL → PASS — the headline result of GRPO fine-tuning.

Training Curves

Step	Loss	Mean Reward	Notes
0	2.05	0.13	baseline
10	1.84	0.21	coalitions forming
20	1.91	0.19	exploration spike
30	1.47	0.25	rerouting stabilises
40	1.42	0.34	cold-chain rescue learned
50	1.19	0.38	tariff shock response
60	1.21	0.46	earthquake priority triage

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Live API Integration

Real-world signals are fetched at episode start and injected into every agent's observation:

Source	Signal	Example (live, April 2026)
OpenWeatherMap	Weather alerts	Dense Fog in Mumbai, Delhi, Kolkata, Jaipur (sev=2)
ExchangeRate-API	USD/INR tariff shock	94.26 (+12.9%) — Customs Broker acts NOW
GDELT 2.0	Conflict/strike signals	India logistics disruption scan
NewsAPI	Breaking trade news	Iran war impacting Gulf shipping
World Bank	Crude oil price	Fuel cost proxy for Carrier bids

Llama 3.3 reads these signals directly — customs_broker_0 fires make_bid with reasoning "tariff shock" and carrier_0 fires reroute with reasoning "Avoiding fog/haze" — both from live API data, not hard-coded scenarios.

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Submission Links

Resource	URL
Live Environment (HF Space)	https://huggingface.co/spaces/WIZARDIAN/logicriasis-train
Trained LoRA Adapter	https://huggingface.co/Sana06112003/logicriasis-adapter
Blog Post	https://huggingface.co/spaces/WIZARDIAN/logicriasis-train/blob/main/BLOG_POST.md
Training Notebook (Colab)	https://colab.research.google.com/github/SANGRAMLEMBE/logicriasis/blob/main/logicriasis_colab_training.ipynb
GitHub Repository	https://github.com/SANGRAMLEMBE/logicriasis

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Team

Built at meta-pytorch-hackathon by Team LogiCrisis. Training ran on HuggingFace A100 Large GPU using Unsloth + TRL GRPO. Total GPU time: ~4 hours.