Summary of Efficient Training in Multi-agent Reinforcement Learning: a Communication-free Framework For the Box-pushing Problem, by David Ge et al.
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem
by David Ge, Hao Ji
First submitted to arxiv on: 19 Nov 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 proposed Shared Pool of Information (SPI) model enables autonomous agents to perform complex tasks and adapt to dynamic environments without a central controller. Prior research often relies on reinforcement learning to enable agents to gain skills for task completion, such as in box-pushing environments. However, when agents push from opposing directions during exploration, they tend to exert equal forces on the box, resulting in minimal displacement and inefficient training. The SPI model facilitates coordination among agents by making information accessible to all, reducing force conflicts and enhancing exploration efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers created a new way for autonomous agents to work together without a boss. They call it Shared Pool of Information (SPI). When agents want to do something, they need to learn how to do it. Sometimes, two agents trying to achieve the same goal will push with equal force, making progress slow. SPI helps agents share information and coordinate their efforts so they can move things more efficiently. |
Keywords
» Artificial intelligence » Reinforcement learning
