Summary of Choices Are More Important Than Efforts: Llm Enables Efficient Multi-agent Exploration, by Yun Qu et al.
Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration
by Yun Qu, Boyuan Wang, Yuhang Jiang, Jianzhun Shao, Yixiu Mao, Cheems Wang, Chang Liu, Xiangyang Ji
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 In this paper, researchers propose an innovative approach to efficient multi-agent exploration in reinforcement learning. The LEMAE method leverages a Large Language Model (LLM) to provide informative task-relevant guidance for agents to explore their environment effectively. By grounding linguistic knowledge from the LLM into symbolic key states, LEMAE reduces redundant explorations and increases reward density using Subspace-based Hindsight Intrinsic Reward (SHIR). The paper also introduces the Key State Memory Tree (KSMT) to track transitions between key states in a specific task. This approach outperforms existing state-of-the-art methods on challenging benchmarks like SMAC and MPE, achieving a 10x acceleration in certain scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way for computers to learn from experience, but it can be hard when there are many agents working together. A new method called LEMAE helps these agents explore their environment more efficiently by using a special kind of computer program called a Large Language Model (LLM). This LLM gives the agents helpful hints about what to do next, which makes them learn faster and avoid repeating things they’ve already tried. The researchers also came up with some new ideas for how the agents can use this information to make better decisions. |
Keywords
» Artificial intelligence » Grounding » Large language model » Reinforcement learning
