Summary of Hypermarl: Adaptive Hypernetworks For Multi-agent Rl, by Kale-ab Abebe Tessera et al.
HyperMARL: Adaptive Hypernetworks for Multi-Agent RL
by Kale-ab Abebe Tessera, Arrasy Rahman, Stefano V. Albrecht
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes HyperMARL, a new parameter sharing approach in cooperative multi-agent reinforcement learning (MARL) that enables agents to learn specialised or homogeneous behaviours without compromising sample efficiency or computational complexity. By using hypernetworks to generate agent-specific actor and critic parameters, HyperMARL decouples observation- and agent-conditioned gradients, reducing policy gradient variance and facilitating specialisation within FuPS while mitigating cross-agent interference. The proposed method consistently performs competitively across multiple MARL benchmarks involving up to twenty agents, achieving comparable behavioural diversity levels as non-parameter sharing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HyperMARL is a new way for computers to learn together in situations where different “agents” need to work together to solve problems. This can be useful for things like self-driving cars or robots that need to work together to achieve a goal. Right now, there are two main ways to do this kind of learning: one method shares information between agents, which helps them learn quickly but can cause problems if they start doing the same thing; the other method doesn’t share information and lets each agent learn its own way, but it takes longer and uses more computing power. The new HyperMARL method tries to find a balance between these two approaches by using special “hypernetworks” that help agents learn their own ways of doing things without getting in each other’s way. |
Keywords
» Artificial intelligence » Reinforcement learning
