Title: Automating Environments For Measuring Agentic Learning
Primary area: applications to robotics, autonomy, planning
Keywords: Learning; Language Agent

Abstract: Humans naturally adapt to diverse environments by learning underlying rules through direct interaction with the real world, which naturally contains all environments as subsets.
For agents to achieve human-like adaptability, they must develop the ability to discover and adapt to unknown environmental rules through exposure to diverse environments. 
However, current agents rely on limited human-provided environments with human-specified learning strategies.
To address environment scarcity, we propose AutoEnv, an automated framework that generates diverse environments with distinct rule distributions, achieving 90\% execution success at \$4.12 per environment. We construct AutoEnv-36, a dataset of 36 heterogeneous environments with 358 levels with varied reward, observation, and state dynamics.
To address learning strategy limitations, we formalize the agentic learning process as discrete, composable components including selection strategies, optimization signals, and target components. 
Experimental validation across 7 language models and 8 optimization methods confirms the quality of AutoEnv-36 while demonstrating that environment-adaptive learning significantly improves performance: 14\% improvement when using only two learning methods, and 32\% improvement when using eight methods. This finding establishes the critical importance of environment-specific learning adaptation and validates the synergy between diverse environments and adaptive learning strategies for developing scalable agent capabilities.