Summary of Multi-agent Reinforcement Learning with a Hierarchy Of Reward Machines, by Xuejing Zheng et al.
Multi-Agent Reinforcement Learning with a Hierarchy of Reward Machines
by Xuejing Zheng, Chao Yu
First submitted to arxiv on: 8 Mar 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 explores cooperative Multi-Agent Reinforcement Learning (MARL) problems, utilizing Reward Machines (RMs) to specify reward functions. By leveraging prior knowledge of high-level events in a task, MAHRM improves learning efficiency. The proposed approach differs from existing works that use RMs for task decomposition and policy learning in simple domains or with independent agents. Instead, MAHRM handles complex scenarios where events among agents occur concurrently and are highly interdependent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how many computers can work together to learn new things. It uses a special tool called Reward Machines (RMs) to make sure the computers know what they’re working towards. The researchers want to see if this tool can help the computers learn more efficiently when they have to work together in complex situations. |
Keywords
* Artificial intelligence * Reinforcement learning
