Summary of Grounded Answers For Multi-agent Decision-making Problem Through Generative World Model, by Zeyang Liu et al.
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
by Zeyang Liu, Xinrui Yang, Shiguang Sun, Long Qian, Lipeng Wan, Xingyu Chen, Xuguang Lan
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 A recent breakthrough in generative models has revolutionized various fields like image generation and chatbots. However, these models often struggle to provide reliable solutions for complex multi-agent decision-making problems due to their lack of human-like trial-and-error experience and reasoning. To overcome this limitation, researchers propose a novel paradigm that combines a language-guided simulator with the multi-agent reinforcement learning pipeline. The simulator acts as a world model, learning dynamics and rewards separately. Specifically, it includes an image tokenizer and causal transformer for generating interaction transitions autoregressively, while the reward model is a bidirectional transformer learned by maximizing the likelihood of expert demonstrations under language guidance. By utilizing this framework, the joint policy is trained, producing an image sequence as the answer through running the converged policy on the dynamics model. Experimental results demonstrate that this approach outperforms previous methods on the StarCraft Multi-Agent Challenge benchmark, particularly exceling in generating consistent interaction sequences and explainable reward functions at interaction states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve generative models for multi-agent decision-making problems by giving them human-like trial-and-error experience. The researchers created a special kind of simulator that learns from language guidance and expert demonstrations. This allows the model to better understand complex situations and come up with more realistic solutions. The results show that this new approach works well, even when tested on tasks it hasn’t seen before. |
Keywords
» Artificial intelligence » Image generation » Likelihood » Reinforcement learning » Tokenizer » Transformer
