multiagent-env provides researchers and developers with a flexible Python framework to simulate and benchmark multi-agent reinforcement learning tasks. It offers a gym-style interface for creating and managing cooperative, competitive, and mixed scenarios, complete with customizable reward structures, observation spaces, and rendering options. The repository includes several example environments and supports easy integration with popular RL libraries.
AI Platforms & Frameworks
Development Environment
#multi-agent reinforcement learning
#Open Source Library
#cooperative scenarios
#competitive scenarios
#action observation spaces
#reward functions
#environment visualization
#Python RL integration
#multi-agent API
#Rapid Prototyping
#benchmarking algorithms
#modular design
#RL frameworks
#real-time rendering
#Easy Setup
#community contributions
#AI environments
#simulation tools
#Machine Learning Agents
#collaborative learning
...
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multiagent-env
multiagent-env provides researchers and developers with a flexible Python framework to simulate and benchmark multi-agent reinforcement learning tasks. It offers a gym-style interface for creating and managing cooperative, competitive, and mixed scenarios, complete with customizable reward structures, observation spaces, and rendering options. The repository includes several example environments and supports easy integration with popular RL libraries.
AI Platforms & Frameworks
Development Environment
#multi-agent reinforcement learning
#Open Source Library
#cooperative scenarios
#competitive scenarios
#action observation spaces
#reward functions
#environment visualization
#Python RL integration
#multi-agent API
#Rapid Prototyping
#benchmarking algorithms
#modular design
#RL frameworks
#real-time rendering
#Easy Setup
#community contributions
#AI environments
#simulation tools
#Machine Learning Agents
#collaborative learning
...
Featured
What is multiagent-env?
multiagent-env is an open-source Python library designed to simplify the creation and evaluation of multi-agent reinforcement learning environments. Users can define both cooperative and adversarial scenarios by specifying agent count, action and observation spaces, reward functions, and environmental dynamics. It supports real-time visualization, configurable rendering, and easy integration with Python-based RL frameworks such as Stable Baselines and RLlib. The modular design allows rapid prototyping of new scenarios and straightforward benchmarking of multi-agent algorithms.
Who will use multiagent-env?
- Multi-agent RL researchers
- Machine learning students
- Academic educators
- RL algorithm developers
- Open-source contributors
How to use the multiagent-env?
- Step1: Clone the repository from GitHub or install via pip.
- Step2: Import the environment module in your Python script.
- Step3: Instantiate a scenario by name or custom configuration.
- Step4: Reset the environment and run simulation steps to collect observations, actions, and rewards.
- Step5: Integrate with RL training loop for policy updates.
- Step6: Render environment or log metrics for analysis.
Platform
- mac
- windows
- linux
multiagent-env's Core Features & Benefits
The Core Features
- Gym-like multi-agent API
- Prebuilt cooperative and competitive scenarios
- Customizable action and observation spaces
- Configurable reward functions
- Environment rendering and visualization
- Easy integration with popular RL libraries
The Benefits
- Speeds up multi-agent RL prototyping
- Standardized interface for benchmarking
- Highly extensible and modular design
- Supports both cooperative and adversarial tasks
- Open-source with community contributions
multiagent-env's Main Use Cases & Applications
- Benchmarking predator-prey algorithms
- Evaluating cooperative navigation strategies
- Testing competitive zero-sum environments
- Developing new multi-agent coordination policies
- Academic coursework and demonstrations
FAQs of multiagent-env
multiagent-env Company Information
- khajash
multiagent-env Reviews
5/5
multiagent-env's Main Competitors and alternatives?
- PettingZoo
- OpenAI Gym Multi-Agent
- MAgent
- RLlib Multi-Agent
- Multi-Agent Particle Environment (MPE)
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