Summary of Civrealm: a Learning and Reasoning Odyssey in Civilization For Decision-making Agents, by Siyuan Qi et al.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
by Siyuan Qi, Shuo Chen, Yexin Li, Xiangyu Kong, Junqi Wang, Bangcheng Yang, Pring Wong, Yifan Zhong, Xiaoyuan Zhang, Zhaowei Zhang, Nian Liu, Wei Wang, Yaodong Yang, Song-Chun Zhu
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper introduces CivRealm, an environment inspired by the Civilization game, which challenges decision-making agents to generalize and reason in novel contexts. The environment is designed to simulate human history and society, requiring sophisticated learning and reasoning skills. Specifically, CivRealm presents a complex imperfect-information general-sum game with changing player numbers, open-ended stochastic environments, and the need for diplomacy and negotiation. The authors provide interfaces for two typical agent types: tensor-based agents that focus on learning and language-based agents that emphasize reasoning. Initial results show that RL-based agents perform reasonably well in mini-games but struggle to make progress in the full game, while LLM-based agents face similar challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CivRealm is a new environment designed for decision-making agents to learn and reason. Imagine playing a game like Civilization where you need to make smart decisions to win. The paper creates an environment that’s like this game, but instead of just winning or losing, the goal is to teach machines how to think and make good choices on their own. Two kinds of agents are tested in this environment: ones that focus on learning from experience and others that use language to reason about what to do. The results show that some machines can do pretty well in small challenges but struggle with bigger tasks, while others have similar struggles. |
