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The generalization of decision-making agents encompasses two fundamentalelements: learning from past experiences and reasoning in novel contexts.However, the predominant emphasis in most interactive environments is onlearning, often at the expense of complexity in reasoning. In this paper, weintroduce CivRealm, an environment inspired by the Civilization game.Civilization's profound alignment with human history and society necessitatessophisticated learning, while its ever-changing situations demand strongreasoning to generalize. Particularly, CivRealm sets up animperfect-information general-sum game with a changing number of players; itpresents a plethora of complex features, challenging the agent to deal withopen-ended stochastic environments that require diplomacy and negotiationskills. Within CivRealm, we provide interfaces for two typical agent types:tensor-based agents that focus on learning, and language-based agents thatemphasize reasoning. To catalyze further research, we present initial resultsfor both paradigms. The canonical RL-based agents exhibit reasonableperformance in mini-games, whereas both RL- and LLM-based agents struggle tomake substantial progress in the full game. Overall, CivRealm stands as aunique learning and reasoning challenge for decision-making agents. The code isavailable at https://github.com/bigai-ai/civrealm.

CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents