social-curiosity

This project explores computational social intrinsic motivation (SIM) in a simple multi-agent reinforcement learning setting. Two agents in a GridWorld must cooperate to open a door and collect coins.

We compare a baseline (extrinsic rewards only) against an agent augmented with a social curiosity reward — a bonus when an agent's proximity enables its teammate to explore new states. This improves coordination and sample efficiency.

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✨ Highlights

• Custom 5×5 GridWorld with two agents, pressure plates, door, and coins.
• Social curiosity intrinsic reward: rewards agents when their teammate explores a new state nearby.
• Dual implementation approach:
  • Tabular Q-learning for clarity and speed (CPU only)
  • Deep RL (PPO) with PettingZoo for modern baseline
• Easy comparison: baseline vs SIM performance curves for both implementations

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🏗️ Project Structure

social-curiosity/
├── src/                    # Shared utilities and common code
├── tabular/               # Tabular Q-learning implementation
│   ├── src/               # Tabular-specific code
│   └── config/            # Tabular configuration
├── deep/                  # Deep RL implementation
│   ├── src/               # Deep-specific code
│   └── config/            # Deep configuration
├── results/               # Experiment results
├── plots/                 # Generated plots
├── pyproject.toml         # Project dependencies
└── README.md              # This file

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🚀 Quickstart

Installation

# Python 3.10+ required
python -m venv .venv && source .venv/bin/activate

# Install with uv (recommended)
uv sync

# Or with pip
pip install .

Tabular Implementation

# Baseline (extrinsic only)
python tabular/src/train.py --run_name baseline --intrinsic_coef 0.0

# With social curiosity intrinsic motivation
python tabular/src/train.py --run_name sim --intrinsic_coef 0.2

# Compare curves
python tabular/src/plot_runs.py

Deep Learning Implementation

# Baseline (extrinsic only)
python deep/src/train.py --run_name baseline --intrinsic_coef 0.0

# With social curiosity intrinsic motivation
python deep/src/train.py --run_name sim --intrinsic_coef 0.2

# Compare curves
python deep/src/plot_runs.py

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📦 Dependencies

Core dependencies include:

• numpy, matplotlib - Core numerical and plotting
• gymnasium, pettingzoo - Multi-agent environments
• stable-baselines3, torch - Deep reinforcement learning
• wandb, tensorboard - Experiment tracking

See pyproject.toml for complete dependency list.

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📊 Results & Plots

Experiment results are saved in results/ directory, organized by implementation type:

• results/tabular/ - Tabular Q-learning results
• results/deep/ - Deep RL results

Generated plots are saved in plots/ directory with the same structure.

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🔧 Development

Install development dependencies:

uv sync --group dev

Run tests:

pytest

Format code:

black .
isort .
ruff check --fix

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📊 Experimental Results Summary

Key Findings

Social Curiosity Impact:

• Tabular Q-Learning: Social curiosity showed mixed results, with some experiments showing significant improvements (+266.3% in extended runs) while others showed performance degradation (-72.9%)
• Deep RL (PPO): Demonstrated minimal impact from social curiosity (+0.5%), suggesting the mechanism may require different tuning for neural network-based approaches
• Exploration Patterns: Social curiosity influenced agent exploration, with both agents exploring more states in SIM-augmented conditions

Implementation Comparison:

• Deep RL Performance: Achieved higher final rewards (13.32 vs 9.20 average) compared to tabular methods
• Tabular Advantages: Offers interpretability and precise state tracking for social curiosity mechanisms
• Success Rates: Both implementations achieved near-perfect success rates (90-100%) in extended training

Performance Highlights

Implementation	Best Final Reward	Success Rate	Episodes	Social Curiosity Impact
Tabular Q-Learning	14.51	100%	50,000	Mixed (±72.9% to +266.3%)
Deep RL (PPO)	13.36	86%	1,000	Minimal (+0.5%)

Generated Visualizations

The project includes comprehensive plots and analysis:

• Learning Curves: Performance progression for baseline vs SIM conditions
• Exploration Analysis: State coverage and agent movement patterns
• Implementation Comparison: Side-by-side performance metrics
• Reward Distribution: Intrinsic vs extrinsic reward contributions

📈 View Results

• 📋 Comprehensive Report: Detailed analysis with methodology, results, and discussion
• 📊 Generated Plots: Visual comparisons and performance metrics
• 📁 Experiment Data: Raw training logs and performance metrics

🔍 Key Insights

1. Social Curiosity Effectiveness: The mechanism shows promise in tabular settings but requires refinement for deep learning approaches
2. Implementation Trade-offs: Tabular methods provide clarity while deep RL offers scalability
3. Exploration Enhancement: Social curiosity successfully promotes coordinated exploration patterns
4. Reward Signal Balance: Careful tuning of intrinsic vs extrinsic reward ratios is crucial

For detailed methodology, complete results, and in-depth analysis, see the comprehensive report.