Learning-to-Communicate-PyTorch is an open-source PyTorch implementation that trains sender and receiver agents to develop communication protocols. It supports referential games and navigation tasks, providing modular code for custom environment integration and reproducible research in emergent multi-agent communication.
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Learning-to-Communicate-PyTorch
Learning-to-Communicate-PyTorch is an open-source PyTorch implementation that trains sender and receiver agents to develop communication protocols. It supports referential games and navigation tasks, providing modular code for custom environment integration and reproducible research in emergent multi-agent communication.
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What is Learning-to-Communicate-PyTorch?
This repository implements emergent communication in multi-agent reinforcement learning using PyTorch. Users can configure sender and receiver neural networks to play referential games or cooperative navigation, encouraging agents to develop a discrete or continuous communication channel. It offers scripts for training, evaluation, and visualization of learned protocols, along with utilities for environment creation, message encoding, and decoding. Researchers can extend it with custom tasks, modify network architectures, and analyze protocol efficiency, fostering rapid experimentation in emergent agent communication.
Who will use Learning-to-Communicate-PyTorch?
- Multi-agent reinforcement learning researchers
- AI developers studying emergent communication
- Graduate students in AI and ML
- Academic labs exploring agent coordination
How to use the Learning-to-Communicate-PyTorch?
- Step1: Clone the repository: git clone https://github.com/minqi/learning-to-communicate-pytorch.git
- Step2: Install dependencies: pip install -r requirements.txt
- Step3: Configure the task and network in config files
- Step4: Run training: python train.py --config configs/referential_game.yaml
- Step5: Evaluate protocols: python evaluate.py --checkpoint path/to/model
- Step6: Visualize results and messages using provided plotting scripts
Platform
- Linux
- Mac
Learning-to-Communicate-PyTorch's Core Features & Benefits
The Core Features
- Referential communication game implementation
- Cooperative navigation task support
- Modular PyTorch network architectures
- Discrete and continuous message channels
- Training, evaluation, and visualization scripts
The Benefits
- Reproducible emergent communication research
- Easy customization for new tasks
- Clear separation of sender and receiver modules
- Open-source community contributions
- Lightweight and dependency-minimal
Learning-to-Communicate-PyTorch's Main Use Cases & Applications
- Benchmarking emergent protocols in referential games
- Studying discrete vs continuous communication channels
- Developing custom multi-agent coordination tasks
- Educational use in advanced AI courses
FAQs of Learning-to-Communicate-PyTorch
Learning-to-Communicate-PyTorch Company Information
Learning-to-Communicate-PyTorch Reviews
5/5
Learning-to-Communicate-PyTorch's Main Competitors and alternatives?
OpenAI emergent communication codebases
PyMARL
MAVA
MADDPG
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