  The generalization of decision-making agents encompasses two fundamental
elements: learning from past experiences and reasoning in novel contexts.
However, the predominant emphasis in most interactive environments is on
learning, often at the expense of complexity in reasoning. In this paper, we
introduce CivRealm, an environment inspired by the Civilization game.
Civilization's profound alignment with human history and society necessitates
sophisticated learning, while its ever-changing situations demand strong
reasoning to generalize. Particularly, CivRealm sets up an
imperfect-information general-sum game with a changing number of players; it
presents a plethora of complex features, challenging the agent to deal with
open-ended stochastic environments that require diplomacy and negotiation
skills. Within CivRealm, we provide interfaces for two typical agent types:
tensor-based agents that focus on learning, and language-based agents that
emphasize reasoning. To catalyze further research, we present initial results
for both paradigms. The canonical RL-based agents exhibit reasonable
performance in mini-games, whereas both RL- and LLM-based agents struggle to
make substantial progress in the full game. Overall, CivRealm stands as a
unique learning and reasoning challenge for decision-making agents. The code is
available at https://github.com/bigai-ai/civrealm.
