mpmg 0.1.0

A Minimum Price Markov Game modular environment

Minimum Price Markov Game (MPMG) Environment

Overview

mpmg is a modular environment designed for studying the Minimum Price Markov Game (MPMG), a concept in game theory and algorithmic game theory. It provides an easy-to-use framework for conducting experiments with multiple agents using collusion and cooperation dynamics. This environment is useful for researchers and developers interested in game theory, reinforcement learning, and multi-agent systems.

Features

• Customizable Multi-Agent Environment: Supports different numbers of agents and heterogeneous vs. homogeneous settings.
• Test Framework: Visualize action frequencies and average rewards over time for custom MPMG configurations.

Project Structure

mpmg/
├── mpmg/                  # Main package directory
│   ├── __init__.py        # Package initialization
│   └── mpmg_env.py        # Environment implementation
├── tests/                 # Unit and integration tests
│   ├── __init__.py        # Package initialization for tests (optional)
│   └── test_mpmg_logic.py # Tests and scenario validation
├── .gitignore             # Ignored files for git
├── README.md              # Project description and usage guide
├── requirements.txt       # Project dependencies
├── setup.py               # Installation script
└── LICENSE                # License information

Installation

To install the package locally, run the following command from the root directory:

pip install -e .

This installs the package in "editable" mode, meaning any changes made in the source code will immediately reflect in the installed package.

Requirements

• Python 3.6+
• NumPy

Dependencies can be installed from requirements.txt:

pip install -r requirements.txt

Usage

To use the mpmg package, import the MPMGEnv class and create an instance of the environment:

from mpmg import MPMGEnv

# Create an instance of the environment
env = MPMGEnv(n_agents=2, sigma_beta=0.0)

# Reset the environment
state = env.reset(seed=42)

# Take a step in the environment
actions = [1, 0]  # Example actions for each agent
rewards, next_state, done = env.step(actions)

The MPMGEnv class provides methods for resetting the environment, taking steps, and observing the state, rewards, and dynamics of multi-agent interactions.

Running Tests

Unit tests are available in the tests/ directory. You can run the tests using pytest:

pytest tests/

Tests

The test scenarios provided in test_mpmg_logic.py also generate logs of action frequencies and average rewards for each agent.

Scenarios

The environment supports the following scenarios for testing and analysis:

• Equiprobable Actions: Agents choose actions with equal probability.
• Full Defection: All agents choose to defect.
• Full Cooperation: All agents cooperate.

These scenarios can be run using the test script provided in tests/test_mpmg_logic.py.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request for improvements, bug fixes, or new features.

Author

Igor Sadoune - [e-mail]