Multi-Agent Surveillance is an open-source Python toolkit that provides a grid-based environment for training cooperative AI agents. It integrates with OpenAI Gym and supports customizable predator–evader scenarios, allowing researchers and developers to define grid size, agent roles, reward functions, and rendering options for reinforcement learning experiments.
AI Platforms & Frameworks
#multi-agent surveillance
#AI Agents
#reinforcement learning
#configurable scenarios
#simulation framework
#agent behavior
#visualization tools
#predator evader dynamics
#benchmarking methods
#open-source solutions
#environment parameters
#flexible configurations
#reproducible experiments
#Machine Learning Integration
#real-time monitoring
#custom reward functions
#AI coordination
#matplotlib visualization
#simulation research
...
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Multi-Agent Surveillance
Multi-Agent Surveillance is an open-source Python toolkit that provides a grid-based environment for training cooperative AI agents. It integrates with OpenAI Gym and supports customizable predator–evader scenarios, allowing researchers and developers to define grid size, agent roles, reward functions, and rendering options for reinforcement learning experiments.
AI Platforms & Frameworks
#multi-agent surveillance
#AI Agents
#reinforcement learning
#configurable scenarios
#simulation framework
#agent behavior
#visualization tools
#predator evader dynamics
#benchmarking methods
#open-source solutions
#environment parameters
#flexible configurations
#reproducible experiments
#Machine Learning Integration
#real-time monitoring
#custom reward functions
#AI coordination
#matplotlib visualization
#simulation research
...
Featured
What is Multi-Agent Surveillance?
Multi-Agent Surveillance offers a flexible simulation framework where multiple AI agents act as predators or evaders in a discrete grid world. Users can configure environment parameters such as grid dimensions, number of agents, detection radii, and reward structures. The repository includes Python classes for agent behavior, scenario generation scripts, built-in visualization via matplotlib, and seamless integration with popular reinforcement learning libraries. This makes it easy to benchmark multi-agent coordination, develop custom surveillance strategies, and conduct reproducible experiments.
Who will use Multi-Agent Surveillance?
- Reinforcement learning researchers
- Multi-agent system developers
- Security simulation enthusiasts
- AI and robotics educators
- Graduate students in AI
How to use the Multi-Agent Surveillance?
- Step1: Clone the repository from GitHub.
- Step2: Install dependencies via pip install -r requirements.txt.
- Step3: Configure environment parameters in the config file or script.
- Step4: Run training scripts that launch OpenAI Gym environments.
- Step5: Visualize agent behaviors and performance using built-in rendering tools.
Platform
- mac
- windows
- linux
Multi-Agent Surveillance's Core Features & Benefits
The Core Features
- OpenAI Gym-compatible multi-agent environment
- Configurable predator–evader grid scenarios
- Customizable reward functions and agent roles
- Built-in matplotlib visualization
- Scenario generation and logging utilities
The Benefits
- Accelerates multi-agent RL research
- Easily integrates with existing RL libraries
- Supports reproducible experiments
- Flexible configuration for diverse surveillance tasks
- Lightweight and open-source
Multi-Agent Surveillance's Main Use Cases & Applications
- Benchmarking cooperative RL algorithms
- Developing drone surveillance strategies
- Teaching multi-agent coordination in AI courses
- Simulating security patrols in smart buildings
- Evaluating predator–prey dynamics in research
FAQs of Multi-Agent Surveillance
Multi-Agent Surveillance Company Information
- Eric Banzuzi
Multi-Agent Surveillance Reviews
5/5
Multi-Agent Surveillance's Main Competitors and alternatives?
- PettingZoo
- Multi-Agent Particle Environment (MPE)
- MAgent
- RLLib Multi-Agent
- Google Research Football
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