Patrolling-Zoo is a Python library that delivers a suite of configurable multi-agent patrolling environments for reinforcement learning. It features multiple pre-built scenarios, customizable graph and grid maps, variable agent counts, and reward functions. Compatible with PettingZoo and standard Gym APIs, researchers and developers can seamlessly integrate it into their RL pipelines to train, evaluate, and benchmark patrolling policies across diverse settings.
AI Platforms & Frameworks
#multi-agent reinforcement learning
#patrolling tasks
#Python libraries
#robotics research
#security monitoring
#grid environments
#graph environments
#agent coordination
#benchmarking techniques
#surveillance simulations
#customizable scenarios
#reinforcement learning integration
#safety operations
#area coverage
#automated monitoring
#research acceleration
#flexible configurations
#reward settings
#observation spaces
#environment customization
...
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Patrolling-Zoo
Patrolling-Zoo is a Python library that delivers a suite of configurable multi-agent patrolling environments for reinforcement learning. It features multiple pre-built scenarios, customizable graph and grid maps, variable agent counts, and reward functions. Compatible with PettingZoo and standard Gym APIs, researchers and developers can seamlessly integrate it into their RL pipelines to train, evaluate, and benchmark patrolling policies across diverse settings.
AI Platforms & Frameworks
#multi-agent reinforcement learning
#patrolling tasks
#Python libraries
#robotics research
#security monitoring
#grid environments
#graph environments
#agent coordination
#benchmarking techniques
#surveillance simulations
#customizable scenarios
#reinforcement learning integration
#safety operations
#area coverage
#automated monitoring
#research acceleration
#flexible configurations
#reward settings
#observation spaces
#environment customization
...
Featured
What is Patrolling-Zoo?
Patrolling-Zoo offers a flexible framework enabling users to create and experiment with multi-agent patrolling tasks in Python. The library includes a variety of grid-based and graph-based environments, each simulating surveillance, monitoring, and coverage scenarios. Users can configure the number of agents, map size, topology, reward functions, and observation spaces. Through compatibility with PettingZoo and Gym APIs, it supports seamless integration with popular reinforcement learning algorithms. This environment facilitates benchmarking and comparing MARL techniques under consistent settings. By providing standard scenarios and tools to customize new ones, Patrolling-Zoo accelerates research in autonomous robotics, security surveillance, search-and-rescue operations, and efficient area coverage using multi-agent coordination strategies.
Who will use Patrolling-Zoo?
- Multi-agent reinforcement learning researchers
- Robotics and surveillance developers
- Graduate students in AI and robotics
- Algorithm engineers benchmarking patrolling policies
How to use the Patrolling-Zoo?
- Step1: Install via pip with 'pip install patrolling-zoo'
- Step2: Import environment using 'from patrolling_zoo.envs import make'
- Step3: Create an environment instance: 'env = make("grid_small", num_agents=3)'
- Step4: Initialize RL algorithm and environment loop
- Step5: Run training and evaluation loops and collect performance metrics
- Step6: Customize maps, agent count, or reward functions as needed
Platform
- mac
- windows
- linux
Patrolling-Zoo's Core Features & Benefits
The Core Features
- Multiple pre-built grid and graph patrolling scenarios
- Customizable map topology, size, and agent count
- Configurable reward and observation settings
- Compatibility with PettingZoo and Gym APIs
- Standardized benchmarking interfaces
The Benefits
- Accelerates MARL research in patrolling tasks
- Easy integration with existing RL pipelines
- Flexible environment customization
- Reproducible and comparable results
- Broad applicability to robotics and surveillance
Patrolling-Zoo's Main Use Cases & Applications
- Benchmarking multi-agent reinforcement learning algorithms
- Testing autonomous surveillance and security patrol strategies
- Simulating search-and-rescue coordinated agent behaviors
- Evaluating area coverage and graph exploration techniques
FAQs of Patrolling-Zoo
Patrolling-Zoo Company Information
Patrolling-Zoo Reviews
5/5
Patrolling-Zoo's Main Competitors and alternatives?
- PettingZoo (other multi-agent environments)
- MAgent
- GridWorld
- OpenAI Gym
- Flatland (railway traffic simulation)
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