Multi Agent Reinforcement Learning for Cooperative Hunting Scenarios

Overview

This project explores the application of Multi-Agent Reinforcement Learning (MARL) techniques in cooperative hunting scenarios. Specifically, it aims to develop intelligent agents capable of collaborating to hunt down prey efficiently. The project leverages concepts from game theory to design and train agents that exhibit cooperative behaviors, ultimately optimizing hunting strategies in a simulated environment.

Motivation

Cooperative hunting scenarios are prevalent in various domains, including robotics, wildlife conservation, and military operations. Developing effective collaboration strategies among multiple agents is crucial for achieving mission objectives while maximizing efficiency and resource utilization. By applying MARL techniques, we aim to address the complexities of cooperative decision-making and coordination in dynamic environments.

Features

• Agent-based Simulation: Utilizes a simulated environment where multiple agents interact with each other and their surroundings.
• Reinforcement Learning: Implements reinforcement learning algorithms to enable agents to learn optimal hunting strategies through trial and error.
• Cooperative Behavior: Encourages agents to collaborate and coordinate their actions to achieve common objectives.
• Scalability: Designed to scale to scenarios involving varying numbers of agents and prey, allowing for flexible experimentation and analysis.

Implementation

The project is implemented using Python and popular libraries such as TensorFlow, PyTorch, and OpenAI Gym. It involves the following components:

1. Environment Setup: Defines the simulated hunting environment, including agents, prey, and the rules of interaction.
2. Agent Design: Constructs intelligent agents capable of perceiving the environment, making decisions, and taking actions based on learned policies.
3. Training Pipeline: Implements the training pipeline using MARL algorithms such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), or Multi-Agent Deep Deterministic Policy Gradient (MADDPG).
4. Evaluation: Evaluates the performance of trained agents in various cooperative hunting scenarios, analyzing metrics such as success rate, hunting efficiency, and resource consumption.

Contributing

Contributions to the project are welcome! If you have ideas for improvements, new features, or bug fixes, feel free to submit pull requests or open issues in the repository.

Acknowledgments

• OpenAI Gym: Provides the simulation environment for reinforcement learning experiments.
• TensorFlow and PyTorch: Frameworks for implementing machine learning algorithms.
• DeepMind: Pioneers in the field of reinforcement learning and multi-agent systems.