Reproduce “Multi-Agent Policy Transfer via Task Relationship Modeling” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Rongjun Qin, Feng Chen, Tonghan Wang, Lei Yuan, Xiaoran Wu, Zongzhang Zhang, Chongjie Zhang, Yang Yu
The MATTAR framework addresses multi-agent transfer learning by explicitly modeling relationships between tasks via learned dynamic representations, which then condition population-invariant policies. This approach achieves superior transfer performance on StarCraft II, enabling solutions for previously intractable "super hard" tasks such as an 80% win rate on MMM6 compared to near 0% from scratch, and also improves multi-task learning.
Key insights
- Explicitly modeling relationships between tasks through learned dynamic representations enables more efficient and principled knowledge transfer in multi-agent reinforcement learning.
- Task characteristics can be effectively captured by their underlying dynamics (transition and reward functions), which can be learned through a forward model.
- An "Alternative-Fixed Training Scheme" is crucial for learning meaningful task representations and preventing degenerate solutions in this context.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.