Summary of Air: Unifying Individual and Collective Exploration in Cooperative Multi-agent Reinforcement Learning, by Guangchong Zhou et al.
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning
by Guangchong Zhou, Zeren Zhang, Guoliang Fan
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); 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 Adaptive Exploration via Identity Recognition (AIR), a novel approach for cooperative multi-agent reinforcement learning (MARL) value-based agents. MARL’s absence of an explicit policy hinders individual exploration based on uncertainty or collective exploration through behavioral diversity among agents. The proposed AIR consists of two adversarial components: a classifier that recognizes agent identities from their trajectories, and an action selector that adaptively adjusts the mode and degree of exploration. This approach allows for both individual and collective exploration during training, as theoretically proven and experimentally validated across various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about helping robots learn to work together by figuring out who’s doing what. Right now, it’s hard for these “value-based agents” (like robots) to explore new ways of working together because they don’t have a clear plan. The researchers propose a way to make them better at exploring and learning from each other. They show that this approach works well in different scenarios. |
Keywords
» Artificial intelligence » Reinforcement learning
