https://github.com/barathkumarpm/multi_agent_lunar_landing_sim
https://github.com/barathkumarpm/multi_agent_lunar_landing_sim
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/barathkumarpm/multi_agent_lunar_landing_sim
- Owner: BarathKumarpm
- Created: 2025-07-16T08:23:15.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-16T08:26:53.000Z (11 months ago)
- Last Synced: 2025-07-17T11:44:39.487Z (11 months ago)
- Language: Jupyter Notebook
- Size: 122 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 🛰️ Multi-Agent Lunar Lander Simulation using Reinforcement Learning
A simulation environment inspired by OpenAI Gym and PettingZoo that explores autonomous coordination between **two lunar landers** attempting simultaneous touchdowns. This project focuses on extending the traditional single-agent Lunar Lander task to a **multi-agent reinforcement learning (MARL)** scenario — introducing complexity, real-time cooperation, collision avoidance, and fuel optimization.
---
## 📘 1. Introduction
**Multi-Agent Lunar Lander** is an advanced version of the classical Lunar Lander environment built using **OpenAI Gymnasium (Box2D)** and the **PettingZoo** framework. Instead of controlling a single lander, this project introduces two landers that must coordinate to land safely — transforming the task from a single-agent control problem into a **multi-agent coordination challenge**.
Agents must manage:
- Independent and interdependent controls
- Thrust, orientation, fuel optimization
- Inter-agent interference and stability management
The simulation resembles **real-world scenarios** such as:
- Coordinated landings of autonomous spacecraft
- Drone fleet coordination
- Multi-robot system control in unstructured environments
---
## 🚩 1.2 Problem Statement
In future space missions, simultaneous landings of multiple landers on the Moon could help reduce costs via:
- Shared payload capacity
- Rideshare opportunities
- Reusability
However, this approach increases:
- Navigational complexity
- Development cost for hazard avoidance and control systems
- Risk of interference and failure
This simulation environment allows for experimentation and training of agents under such constraints, enabling researchers to optimize for cost, safety, and performance.
---
## 🎯 1.3 Objectives
The project’s objectives include:
- Simulating safe and fuel-efficient landings in a multi-agent scenario
- Designing adaptive control strategies using reinforcement learning
- Building agents that generalize across dynamic conditions and unforeseen environmental states
- Benchmarking various RL algorithms using performance metrics such as:
- Landing success
- Fuel efficiency
- Collision avoidance
- Time-to-land
---
## ✅ 1.4 Benefits of Simultaneous Multi-Lander Missions
- **Cost Efficiency**: Reduces need for redundant backup systems
- **Mission Reliability**: Promotes robust risk assessment and coordination
- **Improved Precision**: Helps test real-time autonomous landing under limited zone constraints
- **Scalability**: Encourages multi-lander, multi-mission automation
> Synchronization between landers is crucial — failure in coordination can increase mission time and operational costs.
---
## 🔭 1.5 Scope of the Project
This project:
- Converts OpenAI’s single-agent Lunar Lander into a **multi-agent PettingZoo-compatible environment**
- Simulates real-world lunar dynamics such as:
- Irregular terrain
- Varied soil types
- Light and gravity conditions
### Key Features:
- **Dual-agent control** with independent and shared policy learning
- **Custom reward functions** for balancing:
- Safe landing
- Fuel use
- Synchronization
- Collision avoidance
- **Parameter tuning** for different operation scenarios
- **Modular design** for research extensibility and reproducibility
---
## 🛠️ Technologies & Tools
- `OpenAI Gym`
- `PettingZoo`
- `Box2D`
- `Stable-Baselines3`
- `Python 3.x`
- `NumPy`, `Matplotlib`, etc.
---
## 📈 Real-World Inspiration
Inspired by **NASA's CADRE and AAMAS projects**, which explore decentralized coordination among autonomous robotic landers.
---
## 🤝 Contributions & Future Work
We welcome contributions! Potential extensions:
- Inter-agent communication modeling
- Competitive vs cooperative multi-agent settings
- Integration with real-world sensor data
---
## 📄 License
This project is open-sourced under the MIT License.
---
## 🌌 Final Note
Multi-Agent Lunar Lander provides a challenging yet promising platform for advancing reinforcement learning in space robotics, autonomous control, and multi-agent systems. Through research and collaboration, this project aims to serve as a testbed for future intelligent space missions.