MultiAgentSystems is an open-source Python library that provides implementations of popular multi-agent reinforcement learning algorithms such as MADDPG, QMIX, and VDN. It offers environment interfaces, agent communication modules, and performance evaluation tools. Developers can easily configure scenarios for cooperative or competitive tasks, conduct experiments, and visualize training metrics. The framework leverages PyTorch for model definition and GPU acceleration, facilitating scalable research and development of advanced multi-agent AI systems.
AI Platforms & Frameworks
Development Environment
Tool Libraries
#multi-agent systems
#reinforcement learning
#MARL tools
#MADDPG implementation
#QMIX framework
#decentralized execution
#OpenAI Gym compatibility
#agent communication
#GPU Acceleration
#PyTorch Integration
#benchmarking tools
#research acceleration
#cooperative navigation
#resource allocation simulation
#adversarial games
#experiment tracking
#hyperparameter tuning
#modular design
#convergence metrics
#reward shaping
...
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MultiAgentSystems
MultiAgentSystems is an open-source Python library that provides implementations of popular multi-agent reinforcement learning algorithms such as MADDPG, QMIX, and VDN. It offers environment interfaces, agent communication modules, and performance evaluation tools. Developers can easily configure scenarios for cooperative or competitive tasks, conduct experiments, and visualize training metrics. The framework leverages PyTorch for model definition and GPU acceleration, facilitating scalable research and development of advanced multi-agent AI systems.
AI Platforms & Frameworks
Development Environment
Tool Libraries
#multi-agent systems
#reinforcement learning
#MARL tools
#MADDPG implementation
#QMIX framework
#decentralized execution
#OpenAI Gym compatibility
#agent communication
#GPU Acceleration
#PyTorch Integration
#benchmarking tools
#research acceleration
#cooperative navigation
#resource allocation simulation
#adversarial games
#experiment tracking
#hyperparameter tuning
#modular design
#convergence metrics
#reward shaping
...
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What is MultiAgentSystems?
MultiAgentSystems is designed to simplify the process of building and evaluating multi-agent reinforcement learning (MARL) applications. The platform includes implementations of state-of-the-art algorithms like MADDPG, QMIX, VDN, and centralized training with decentralized execution. It features modular environment wrappers compatible with OpenAI Gym, communication protocols for agent interaction, and logging utilities to track metrics such as reward shaping and convergence rates. Researchers can customize agent architectures, tune hyperparameters, and simulate settings including cooperative navigation, resource allocation, and adversarial games. With built-in support for PyTorch, GPU acceleration, and TensorBoard integration, MultiAgentSystems accelerates experimentation and benchmarking in collaborative and competitive multi-agent domains.
Who will use MultiAgentSystems?
- Reinforcement learning researchers
- AI developers working on multi-agent systems
- Graduate students and educators
- Robotics and simulation engineers
How to use the MultiAgentSystems?
- Step1: Clone the repository via git clone https://github.com/furiousteabag/MultiAgentSystems.git
- Step2: Install dependencies with pip install -r requirements.txt
- Step3: Select an algorithm and environment config in the configs/ folder
- Step4: Run training: python train.py --config configs/maddpg.yaml
- Step5: Monitor progress using TensorBoard with tensorboard --logdir logs/
- Step6: Evaluate trained agents: python evaluate.py --checkpoint path/to/checkpoint
- Step7: Customize scenarios or agent architectures by editing config and model files
Platform
- mac
- windows
- linux
MultiAgentSystems's Core Features & Benefits
The Core Features
- Implementations of MADDPG, QMIX, VDN and more
- Modular environment wrappers for OpenAI Gym
- Agent communication and coordination modules
- Logging and TensorBoard integration
- GPU acceleration with PyTorch
The Benefits
- Accelerates MARL research and development
- Modular and extensible design
- Easy experiment replication and benchmarking
- Built-in evaluation and visualization tools
- Support for cooperative and competitive scenarios
MultiAgentSystems's Main Use Cases & Applications
- Cooperative navigation tasks for multi-robot systems
- Resource allocation and scheduling simulations
- Competitive game AI development
- Benchmarking and comparing MARL algorithms
FAQs of MultiAgentSystems
MultiAgentSystems Company Information
MultiAgentSystems Reviews
5/5
MultiAgentSystems's Main Competitors and alternatives?
- OpenAI MAgent
- PettingZoo
- RLlib
- EPyMARL
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