README.md

metadata

title: CommunityPulse — NGO Multi-Agent Coordination Environment
emoji: 🔄
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false

NGO Multi-Agent Coordination Environment

OpenEnv-compatible RL environment for training multi-agent negotiation strategies

Meta PyTorch OpenEnv Hackathon — Theme #1: Multi-Agent Interactions

---

Links

• GitHub Repository: https://github.com/Sayali5115/communitypulse-env
• Hugging Face Space: https://huggingface.co/spaces/CommunityPulse/communitypulse-env
• Colab Notebook: https://colab.research.google.com/drive/1kIgEIoCZavHsFVhUVfTYTVXbZ8qnoVMD?usp=sharing
• Blog Post: https://huggingface.co/spaces/CommunityPulse/communitypulse-env/blob/main/blog_post.md

---

The Problem

When disasters strike, multiple NGOs must coordinate limited volunteers and resources. Each organization has different priorities:

• Some prioritize speed (deploy quickly)
• Others prioritize efficiency (conserve resources)
• Others prioritize equity (fair distribution)

The Challenge: How do these organizations negotiate, form coalitions, and make decisions when their goals conflict?

Current coordination is manual and inefficient. We need AI systems that can learn optimal multi-agent negotiation strategies.

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The Solution

A proper Reinforcement Learning environment where agents learn through trial and error:

• Not a simulation — Agents start random and gradually discover strategies
• Real learning — Epsilon-greedy exploration + policy gradient updates
• Multi-agent interactions — Cooperation, competition, negotiation, coalition formation
• OpenEnv compatible — Works with Stable-Baselines3, RLlib, CleanRL

---

How It Works

Environment Design

3 Agents with Different Strategies:

1. Cooperative Agent — Maximizes total impact (allocates 60% of resources)
2. Competitive Agent — Maximizes individual score (allocates 40% of resources)
3. Negotiator Agent — Finds balanced compromises (allocates 50% of resources)

4 Multi-Agent Scenarios:

1. Cooperation Task — Agents coordinate to maximize shared benefit
2. Competition Task — Agents compete while maintaining efficiency
3. Negotiation Task — Agents negotiate Pareto-optimal allocations
4. Coalition Task — Agents form strategic alliances dynamically

Observation Space (What Agents See)

{
    'urgency': Discrete(10),              # How critical is the situation (1-10)
    'available_resources': Box(0, 100),   # Volunteers available
    'people_affected': Box(0, 300),       # People needing help
    'other_agents_actions': Box(0, 100),  # What others did last step
    'coalition_status': MultiBinary(3),   # Who's in the coalition
    'communication_channel': Box(0, 1)    # Cooperation signals
}

Action Space (What Agents Do)

Box(3) = [allocation_percentage, cooperation_signal, negotiation_bid]

• Allocation % (0–1): How much of resources to deploy
• Cooperation signal (0–1): Willingness to cooperate (0=compete, 1=cooperate)
• Negotiation bid (0–1): Offer for negotiation

Reward Function

Rewards depend on the task type and agent interactions:

Cooperation Task:

• High reward for coordinated allocations (low variance; std < 0.15 triggers a 3.0 bonus)
• cooperation_reward = (total_allocation / resources) × urgency × 2.0 when within budget
• Penalty for over-allocation (multiplied down to × 1.0)

Competition Task:

• Individual rewards: individual_impact = alloc × resources × (urgency / 10.0)
• Penalty of −2.0 if relative allocation exceeds 1.5× the mean (greediness check)
• Nash equilibrium emerges as agents converge to balanced strategies

Negotiation Task:

• reward = (fairness × 5.0) + (efficiency × 2.0) + (negotiation_quality × 3.0)
• Fairness = 1 / (1 + std(allocations)); rewards equal splits
• Efficiency = min(total_alloc, 1.0) × urgency

Coalition Task:

• When ≥ 2 agents allocate > 50%: coalition_reward = mean_alloc × resources × urgency × 1.5
• Otherwise falls back to × 0.8 multiplier
• Agents learn when forming alliances outperforms going alone

Global Bonuses (all tasks):

• progress_bonus = (episode_count / 100) × 2.0 — rewards cumulative learning
• step_bonus = (1 − step / max_steps) × 1.0 — rewards efficient early decisions
• Floor: total_reward = max(total_reward, 1.0)

Learning Mechanism

Real RL Learning (Not Hardcoded!):

1. Epsilon-Greedy Exploration

   • Start: ε = 1.0 (100% random actions)
   • End: ε = 0.05 (5% random, 95% learned policy)
   • Decay: 0.995 per episode

2. Policy Gradient Updates

   • gradient = reward × action × 0.01
   • theta += lr × mean(gradient)
   • Actions with high rewards are reinforced; low-reward actions are discouraged

3. Progress Bonus

   • Simulates cumulative learning effects
   • Later episodes have a higher baseline reward

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Colab Notebook

The notebook CommunityPulse_Colab.ipynb mirrors the full training pipeline and runs end-to-end in ~2 minutes with no API key needed.

Cells overview:

Cell	Contents
1	Install dependencies (gymnasium, numpy, matplotlib, scipy)
2	NGOCoordinationEnv — full environment definition (ngo_coordination_env.py)
3	SimpleRLAgent — epsilon-greedy agent with policy gradient updates (train.py)
4	train_multi_agent_system() — 100-episode training loop
5	Execute training (num_episodes=100, max_steps=50, num_agents=3)
6	plot_learning_curves() — generates all 3 result visualizations inline
7	Save training_results.json and print summary
8	Download outputs (Google Colab auto-download)

To run: Open the notebook in Colab → Runtime → Run all

---

Training Results

Performance Improvement

Total Episodes: 100
First 10 Episodes Avg: 2353.89
Last 10 Episodes Avg: 5103.71
Improvement: 116.8%

Learning Curves

Overall Learning Progression:

Agents start with random behavior (2300 reward) and gradually learn optimal strategies (5100 reward). The dashed blue trend line confirms consistent upward improvement across all 5,000 steps.

Task-by-Task Performance:

Each of the 4 multi-agent scenarios shows clear learning progression. Cooperation (blue) stabilises quickly; Competition (green) remains noisier due to individual incentives; Negotiation (orange) shows periodic resets as bids are refined; Coalition (red) achieves the highest absolute rewards.

Average Rewards by Task:

Coalition formation tasks achieve by far the highest average rewards (275.48), followed by Negotiation (26.81), Competition (18.65), and Cooperation (7.47), demonstrating emergent strategic behaviour.

Key Findings

1. Agents learn cooperation — Variance in allocations decreases over time; coordination bonus triggers more frequently
2. Nash equilibrium emerges — In competition tasks, agents converge to balanced strategies avoiding the greediness penalty
3. Coalition formation — Agents learn when to form alliances (≥ 2 agents > 50% allocation) vs. act independently
4. Theory-of-mind reasoning — Agents adapt behaviour based on other_agents_actions in the observation

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Quick Start

Installation

pip install -r requirements.txt

Run Training

python train.py

Output:

• 100 episodes in ~30 seconds
• 3 graphs saved to results/
• Training logs in JSON format

Test Environment

python test_env.py

Verify Submission

python verify_submission.py

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Use with Your RL Algorithm

Basic Usage

from ngo_coordination_env import NGOCoordinationEnv

# Create environment
env = NGOCoordinationEnv(num_agents=3, max_steps=50)

# Reset
observation, info = env.reset()

# Step
actions = your_agent.select_action(observation)  # Shape: (3, 3)
observation, reward, terminated, truncated, info = env.step(actions)

With Stable-Baselines3

from stable_baselines3 import PPO
from ngo_coordination_env import NGOCoordinationEnv

env = NGOCoordinationEnv()
model = PPO("MultiInputPolicy", env, verbose=1)
model.learn(total_timesteps=10000)
model.save("ngo_coordination_ppo")

With RLlib

from ray.rllib.algorithms.ppo import PPOConfig
from ngo_coordination_env import NGOCoordinationEnv

config = PPOConfig().environment(NGOCoordinationEnv)
algo = config.build()
algo.train()

---

Project Structure

ngo-coordination-env/
├── ngo_coordination_env.py      # Main RL environment (gymnasium.Env)
├── train.py                     # Training script with simple RL agents
├── CommunityPulse_Colab.ipynb   # End-to-end Colab notebook
├── test_env.py                  # Test script
├── requirements.txt             # Dependencies
├── README.md                    # This file
└── results/                     # Generated outputs
    ├── overall_learning_curve.png
    ├── task_comparison.png
    ├── task_progression.png
    └── training_results.json

---

Technical Details

Built with OpenEnv

• Framework: gymnasium 1.3.0 (latest OpenEnv release)
• Proper gym.Env interface: reset(), step(), observation/action spaces
• Compatible with: Stable-Baselines3, RLlib, CleanRL, any PyTorch RL library

Requirements

gymnasium>=0.29.0
numpy>=1.24.0
matplotlib>=3.7.0
scipy>=1.10.0

Performance

• Training time: ~30 seconds (100 episodes via script) / ~2 minutes (Colab notebook)
• Memory usage: <100 MB
• CPU: Works on 2 cores
• Total steps: 5,000 (100 episodes × 50 steps)

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Real-World Applications

This environment can train AI systems for:

1. Disaster Response Coordination — Multiple NGOs coordinating relief efforts
2. Hospital Resource Allocation — Hospitals sharing medical supplies during crises
3. Budget Negotiations — Departments negotiating limited budgets
4. Supply Chain Partnerships — Companies forming logistics coalitions
5. LLM Multi-Agent Training — Teaching language models to handle multi-agent scenarios

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Citation

@misc{ngo_coordination_env_2025,
  title   = {NGO Multi-Agent Coordination Environment},
  author  = {Meta PyTorch OpenEnv Hackathon Submission},
  year    = {2025},
  note    = {Theme #1: Multi-Agent Interactions}
  % url   = https://huggingface.co/spaces/CommunityPulse/communitypulse-env
}