Summary of On Stateful Value Factorization in Multi-agent Reinforcement Learning, by Enrico Marchesini et al.
On Stateful Value Factorization in Multi-Agent Reinforcement Learning
by Enrico Marchesini, Andrea Baisero, Rupali Bhati, Christopher Amato
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper introduces a new paradigm for designing scalable multi-agent reinforcement learning algorithms, known as Value Factorization. The current methods lack a theoretical foundation, with stateless functions being used in theory but state information being utilized in practice. To address this mismatch, the authors formally analyze the theory of using states instead of histories and introduce DuelMIX, an algorithm that learns distinct per-agent utility estimators to improve performance. The results are demonstrated on StarCraft II micromanagement and Box Pushing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines can work together to make good decisions. Currently, some methods for doing this are not well thought out and may not be the best way to do things. The authors look at why this might be the case and propose a new way of working called DuelMIX. This method learns different ways to make decisions for each machine, which can help it work better. The results show that this approach works well on some tasks. |
Keywords
» Artificial intelligence » Reinforcement learning
