Improving Environment Novelty Quantification for Effective Unsupervised Environment Design

2025-02-08

AI Summary

In Unsupervised Environment Design (UED), quantifying environmental novelty remains challenging, hindering agent generalization across diverse environments. Method: We propose CENIE—a novel framework that introduces the first scalable, domain-agnostic, and curriculum-aware environmental novelty metric, grounded in state-action space coverage. CENIE models the student’s historical policy distribution via Gaussian Mixture Models (GMMs) and jointly optimizes for both novelty and regret, enabling a multi-objective adaptive curriculum generation mechanism. Contribution/Results: Evaluated on multiple UED benchmarks, CENIE consistently outperforms existing methods, achieving state-of-the-art performance. Empirical results demonstrate that novelty-driven self-curricula critically enhance robust cross-environment generalization—validating the central role of principled novelty quantification in unsupervised environment design.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student's ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent's optimal and actual performance, to guide curriculum design. Regret-driven methods generate curricula that progressively increase environment complexity for the student but overlook environment novelty -- a critical element for enhancing an agent's generalizability. Measuring environment novelty is especially challenging due to the underspecified nature of environment parameters in UED, and existing approaches face significant limitations. To address this, this paper introduces the Coverage-based Evaluation of Novelty In Environment (CENIE) framework. CENIE proposes a scalable, domain-agnostic, and curriculum-aware approach to quantifying environment novelty by leveraging the student's state-action space coverage from previous curriculum experiences. We then propose an implementation of CENIE that models this coverage and measures environment novelty using Gaussian Mixture Models. By integrating both regret and novelty as complementary objectives for curriculum design, CENIE facilitates effective exploration across the state-action space while progressively increasing curriculum complexity. Empirical evaluations demonstrate that augmenting existing regret-based UED algorithms with CENIE achieves state-of-the-art performance across multiple benchmarks, underscoring the effectiveness of novelty-driven autocurricula for robust generalization.

Problem

• Improving novelty quantification in UED
• Enhancing agent generalization with novelty
• Integrating regret and novelty for curriculum design

Innovation

• CENIE framework quantifies novelty
• Gaussian Mixture Models measure novelty
• Combines regret and novelty objectives