CommNet is an open-source PyTorch framework implementing the Communication Network (CommNet) architecture for cooperative multi-agent reinforcement learning. It provides configurable network layers, hidden-state sharing modules, training scripts, and evaluation tools to enable agents to learn collaborative policies through learned communication protocols.
AI Platforms & Frameworks
#multi-agent communication
#PyTorch library
#cooperative environments
#reinforcement learning
#Inter-agent coordination
#CommNet architecture
#Agent communication strategies
#Benchmarking agent policies
#OpenAI Gym-Integration
#modular codebase
#reproducible results
#navigation tasks
#Pursuit-evasion
#resource collection
#training scripts
#evaluation tools
#Communication module
#configurable agents
#Hidden state sharing
#Research Tools
...
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CommNet
CommNet is an open-source PyTorch framework implementing the Communication Network (CommNet) architecture for cooperative multi-agent reinforcement learning. It provides configurable network layers, hidden-state sharing modules, training scripts, and evaluation tools to enable agents to learn collaborative policies through learned communication protocols.
AI Platforms & Frameworks
#multi-agent communication
#PyTorch library
#cooperative environments
#reinforcement learning
#Inter-agent coordination
#CommNet architecture
#Agent communication strategies
#Benchmarking agent policies
#OpenAI Gym-Integration
#modular codebase
#reproducible results
#navigation tasks
#Pursuit-evasion
#resource collection
#training scripts
#evaluation tools
#Communication module
#configurable agents
#Hidden state sharing
#Research Tools
...
Featured
What is CommNet?
CommNet is a research-oriented library that implements the CommNet architecture, allowing multiple agents to share hidden states at each timestep and learn to coordinate actions in cooperative environments. It includes PyTorch model definitions, training and evaluation scripts, environment wrappers for OpenAI Gym, and utilities for customizing communication channels, agent counts, and network depths. Researchers and developers can use CommNet to prototype and benchmark inter-agent communication strategies on navigation, pursuit–evasion, and resource-collection tasks.
Who will use CommNet?
- Multi-agent RL researchers
- Reinforcement learning engineers
- AI graduate students
- Academic research labs
- Developers building collaborative AI systems
How to use the CommNet?
- Step1: Clone the repository from GitHub.
- Step2: Install dependencies via pip install -r requirements.txt.
- Step3: Configure environment and agent parameters in config files.
- Step4: Run training scripts (e.g., python train_commnet.py).
- Step5: Monitor progress via provided logging utilities.
- Step6: Evaluate trained models with python evaluate.py.
- Step7: Customize network architecture or environment wrappers as needed.
Platform
- mac
- windows
- linux
CommNet's Core Features & Benefits
The Core Features
- PyTorch implementation of CommNet architecture
- Hidden-state communication module between agents
- Configurable network layers and agent counts
- Training and evaluation scripts
- Environment wrappers for OpenAI Gym
- Logging and checkpoint utilities
The Benefits
- Simplifies inter-agent communication research
- Modular and extensible codebase
- Easy integration with custom environments
- Accelerates benchmarking of multi-agent policies
- Reproducible training and evaluation
CommNet's Main Use Cases & Applications
- Cooperative navigation and foraging tasks
- Multi-agent pursuit–evasion games
- Collaborative target tracking
- Resource collection in grid worlds
- Benchmarking communication protocols
FAQs of CommNet
CommNet Company Information
- KornbergFresnel
CommNet Reviews
5/5
CommNet's Main Competitors and alternatives?
- OpenAI MADDPG
- QMIX
- VDN
- BiCNet
- MAgent
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