Summary of Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-agent Reinforcement Learning, by Zeyang Liu et al.
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
by Zeyang Liu, Lipeng Wan, Xinrui Yang, Zhuoran Chen, Xingyu Chen, Xuguang Lan
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Imagine, Initialize, and Explore (IIE) method tackles the challenge of efficient multi-agent exploration in complex scenarios. By employing a transformer model to imagine how agents reach critical states that influence each other’s transition functions, IIE formulates sequence modeling problems to predict states, observations, prompts, actions, and rewards autoregressively. The prompt includes timestep-to-go, return-to-go, influence value, and one-shot demonstration, guiding action generation. Initializing agents at critical states increases the likelihood of discovering under-explored regions. Empirical results show that IIE outperforms multi-agent exploration baselines on StarCraft Multi-Agent Challenge (SMAC) and SMACv2 environments, particularly in sparse-reward SMAC tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a team of robots working together to achieve a goal. To help them find the best way to work together, imagine, initialize, and explore is a new approach that can imagine how each robot reaches an important state that affects others. It then uses this idea to help the robots start in the right place and explore more effectively. This method has been tested on different scenarios and shows promising results. |
Keywords
* Artificial intelligence * Likelihood * One shot * Prompt * Transformer
