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Benchmarking agent policies

  • Open-source PyTorch-based framework implementing CommNet architecture for multi-agent reinforcement learning with inter-agent communication enabling collaborative decision-making.
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    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.
    CommNet 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
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