Terra Nova: A Comprehensive Challenge Environment for Intelligent Agents

2025-11-19

AI Summary

Existing RL benchmarks struggle to jointly capture the coupled challenges of partial observability, credit assignment, representation learning, and enormous action spaces. Method: This paper introduces the Civilization V–inspired Comprehensive Challenge Environment (CCE)—a single, unified, high-fidelity simulation that compels agents to concurrently address all these strongly coupled challenges over extended interactions, avoiding policy fragmentation and shallow adaptation. It features a dynamic partially observable state space, hierarchical action abstraction, cross-temporal credit attribution, and scalable representation learning interfaces. Contribution/Results: We formally define, for the first time, the evaluation paradigm of “deep reasoning under multi-challenge coupling”; release the first open-source RL benchmark supporting long-horizon planning and continual adaptation; and empirically demonstrate that CCE effectively discriminates agents with genuine deep reasoning capabilities from those relying on superficial transfer strategies.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Adversarial Agents
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We introduce Terra Nova, a new comprehensive challenge environment (CCE) for reinforcement learning (RL) research inspired by Civilization V. A CCE is a single environment in which multiple canonical RL challenges (e.g., partial observability, credit assignment, representation learning, enormous action spaces, etc.) arise simultaneously. Mastery therefore demands integrated, long-horizon understanding across many interacting variables. We emphasize that this definition excludes challenges that only aggregate unrelated tasks in independent, parallel streams (e.g., learning to play all Atari games at once). These aggregated multitask benchmarks primarily asses whether an agent can catalog and switch among unrelated policies rather than test an agent's ability to perform deep reasoning across many interacting challenges.

Problem

• Addresses multiple canonical RL challenges simultaneously
• Requires integrated long-horizon understanding across variables
• Excludes aggregated multitask benchmarks with independent policies

Innovation

• Integrated environment with multiple simultaneous RL challenges
• Long-horizon reasoning across interacting variables required
• Excludes simple multitask aggregation of independent problems