AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning

2025-11-24

AI Summary

Existing agent evaluation frameworks predominantly rely on static, single-domain environments, lacking systematic metrics for assessing generalization across heterogeneous environments. Method: This paper introduces AutoEnv—a novel framework for automatically generating diverse, standardized benchmark environments. It models environments as decomposable probability distributions to enable low-cost, controllable, large-scale generation of heterogeneous environments; formalizes agent learning in a modular fashion to support fine-grained evaluation; and integrates factorized modeling, LLM-augmented assessment, and a three-stage “select–optimize–evaluate” learning paradigm. Contribution/Results: We release AutoEnv-36, comprising 36 distinct environments and 358 levels. Empirical analysis reveals diminishing returns in performance gains as the number of training environments increases. While adaptive method selection improves cross-environment generalization, its marginal benefits also diminish, highlighting fundamental scalability constraints in current generalization strategies.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Humans naturally adapt to diverse environments by learning underlying rules across worlds with different dynamics, observations, and reward structures. In contrast, existing agents typically demonstrate improvements via self-evolving within a single domain, implicitly assuming a fixed environment distribution. Cross-environment learning has remained largely unmeasured: there is no standard collection of controllable, heterogeneous environments, nor a unified way to represent how agents learn. We address these gaps in two steps. First, we propose AutoEnv, an automated framework that treats environments as factorizable distributions over transitions, observations, and rewards, enabling low-cost (4.12 USD on average) generation of heterogeneous worlds. Using AutoEnv, we construct AutoEnv-36, a dataset of 36 environments with 358 validated levels, on which seven language models achieve 12-49% normalized reward, demonstrating the challenge of AutoEnv-36. Second, we formalize agent learning as a component-centric process driven by three stages of Selection, Optimization, and Evaluation applied to an improvable agent component. Using this formulation, we design eight learning methods and evaluate them on AutoEnv-36. Empirically, the gain of any single learning method quickly decrease as the number of environments increases, revealing that fixed learning methods do not scale across heterogeneous environments. Environment-adaptive selection of learning methods substantially improves performance but exhibits diminishing returns as the method space expands. These results highlight both the necessity and the current limitations of agent learning for scalable cross-environment generalization, and position AutoEnv and AutoEnv-36 as a testbed for studying cross-environment agent learning. The code is avaiable at https://github.com/FoundationAgents/AutoEnv.

Problem

• Developing automated framework for generating heterogeneous environments
• Formalizing agent learning process across diverse environmental conditions
• Measuring cross-environment generalization limitations of current learning methods

Innovation

• Automated framework generates heterogeneous environment distributions
• Formalizes agent learning as component-centric three-stage process
• Proposes environment-adaptive selection method for cross-environment generalization