Terra Nova: A Comprehensive Challenge Environment for Intelligent Agents

• URL: http://arxiv.org/abs/2511.15378v1
• Date: Wed, 19 Nov 2025 12:10:10 GMT
• Title: Terra Nova: A Comprehensive Challenge Environment for Intelligent Agents
• Authors: Trevor McInroe,
• Abstract summary: We introduce Terra Nova, a new comprehensive challenge environment (CCE) for reinforcement learning (RL) research inspired by Civilization V.<n>A CCE is a single environment in which multiple canonical RL challenges arise simultaneously.<n>These aggregated multitask benchmarks primarily asses whether an agent can catalog and switch among unrelated policies.
• Score: 2.518870792757066
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The Cell Must Go On: Agar.io for Continual Reinforcement Learning [9.034912115190034]
  Continual reinforcement learning (RL) concerns agents that are expected to learn continually, rather than converge to a policy that is then fixed for evaluation.<n>We introduce AgarCL, a research platform for continual RL that allows for a progression of increasingly sophisticated behaviour.
  arXiv  Detail & Related papers  (2025-05-23T20:09:27Z)
• On Generalization Across Environments In Multi-Objective Reinforcement Learning [6.686583184622338]
  We formalize the concept of generalization in Multi-Objective Reinforcement Learning (MORL) and how it can be evaluated.<n>We contribute a novel benchmark featuring diverse multi-objective domains with parameterized environment configurations.<n>Our baseline evaluations of state-of-the-art MORL algorithms on this benchmark reveals limited generalization capabilities, suggesting significant room for improvement.
  arXiv  Detail & Related papers  (2025-03-02T08:50:14Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents [63.79739920174535]
  We introduce CivRealm, an environment inspired by the Civilization game. CivRealm stands as a unique learning and reasoning challenge for decision-making agents.
  arXiv  Detail & Related papers  (2024-01-19T09:14:11Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models [83.63242931107638]
  We propose four characteristics of generally intelligent agents. We argue that active engagement with objects in the real world delivers more robust signals for forming conceptual representations. We conclude by outlining promising future research directions in the field of artificial general intelligence.
  arXiv  Detail & Related papers  (2023-07-07T13:58:16Z)
• Centralized control for multi-agent RL in a complex Real-Time-Strategy game [0.0]
  Multi-agent Reinforcement learning (MARL) studies the behaviour of multiple learning agents that coexist in a shared environment. MARL is more challenging than single-agent RL because it involves more complex learning dynamics. This project provides the end-to-end experience of applying RL in the Lux AI v2 Kaggle competition.
  arXiv  Detail & Related papers  (2023-04-25T17:19:05Z)
• Autonomous Reinforcement Learning: Formalism and Benchmarking [106.25788536376007]
  Real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world. Common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms.
  arXiv  Detail & Related papers  (2021-12-17T16:28:06Z)
• NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment [71.11505407453072]
  We propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment. The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment. This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community: Natural Language Understanding and Generation (NLU/G) and Reinforcement Learning (RL)
  arXiv  Detail & Related papers  (2021-10-13T07:13:44Z)
• Explore and Control with Adversarial Surprise [78.41972292110967]
  Reinforcement learning (RL) provides a framework for learning goal-directed policies given user-specified rewards. We propose a new unsupervised RL technique based on an adversarial game which pits two policies against each other to compete over the amount of surprise an RL agent experiences. We show that our method leads to the emergence of complex skills by exhibiting clear phase transitions.
  arXiv  Detail & Related papers  (2021-07-12T17:58:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.