Can hypernetworks improve multi-agent RL efficiency?
HyperMARL: Adaptive Hypernetworks for Multi-Agent RL
This paper introduces HyperMARL, a new method for training multi-agent AI systems that balances the advantages of shared and specialized learning. Traditional methods either share all learning parameters between agents (efficient but limits diverse behaviors) or give each agent unique parameters (allows diversity but less efficient). HyperMARL uses hypernetworks to generate agent-specific parameters based on agent IDs or learned embeddings, dynamically adapting to homogeneous or heterogeneous behaviors as needed.
For LLM-based multi-agent systems, HyperMARL offers a way to control agent specialization without modifying learning objectives or predefining diversity levels. This could be crucial for scenarios requiring diverse agent roles while maintaining efficient parameter sharing. The decoupling of agent and state-based gradients through hypernetworks could also contribute to reducing policy gradient variance, a common issue in multi-agent reinforcement learning. The research demonstrates the potential of hypernetworks for building more scalable and robust multi-agent systems, especially with increasing numbers of agents.