Cross-environment agent learning effectiveness

Determine whether artificial agents can learn effectively across heterogeneous environments that differ in their underlying rule distributions over dynamics (state transitions), observations, and rewards, rather than only improving within a single domain, thereby achieving robust cross-environment agent learning.

Background

The paper argues that most existing agent-learning approaches demonstrate gains within a single environment family (e.g., coding, search, or games) and that agents typically assume a fixed environment distribution. In contrast, humans adapt across worlds with different rules governing dynamics, observations, and rewards, motivating the need to paper cross-environment learning.

To enable such paper, the authors introduce AutoEnv for generating heterogeneous environments and formalize agent learning as a component-centric process (Selection, Optimization, Evaluation). Despite these tools, they explicitly identify as an open question whether agents can truly learn across heterogeneous rule distributions to achieve robust cross-environment generalization.