Summary of Efficient Adaptation in Mixed-motive Environments Via Hierarchical Opponent Modeling and Planning, by Yizhe Huang et al.
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
by Yizhe Huang, Anji Liu, Fanqi Kong, Yaodong Yang, Song-Chun Zhu, Xue Feng
First submitted to arxiv on: 12 Jun 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 The proposed Hierarchical Opponent modeling and Planning (HOP) algorithm is a novel multi-agent decision-making approach that efficiently adapts to co-players in mixed-motive environments. HOP consists of two modules: an opponent modeling module that infers others’ goals and learns goal-conditioned policies, and a planning module that employs Monte Carlo Tree Search (MCTS) to identify the best response. This algorithm improves efficiency by updating beliefs about others’ goals across and within episodes and guides planning using information from the opponent modeling module. Experimental results show HOP’s superior few-shot adaptation capabilities when interacting with unseen agents and excels in self-play scenarios. The emergence of social intelligence during experiments underscores the potential of HOP in complex multi-agent environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HOP is a new way for computers to play games or work together. Right now, it’s hard for computers to learn from each other if they’re playing different ways. HOP helps computers figure out what others are trying to do and make better choices based on that information. This makes the computer better at learning from its opponents and making good decisions. In experiments, HOP did very well at playing games with itself or against new opponents it had never seen before. |
Keywords
» Artificial intelligence » Few shot
