Improving Regret Approximation for Unsupervised Dynamic Environment Generation

2026-01-21

AI Summary

This work addresses the challenge in unsupervised environment design (UED) where existing methods struggle to generate effective training curricula that enhance the generalization of reinforcement learning agents under complex environmental parameters, primarily due to poor credit assignment and inaccurate regret approximation. To overcome these limitations, we propose DEGen, a dynamic environment generation framework that introduces a novel regret approximation metric termed Maximised Negative Advantage (MNA). MNA provides denser and more informative reward signals to the generator, enabling more accurate identification of high-difficulty levels. By integrating MNA into DEGen, the framework substantially improves credit assignment during curriculum generation. Empirical results demonstrate that DEGen consistently outperforms state-of-the-art UED methods in large-scale environments, achieving significant gains in zero-shot performance and generalization capability.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Unsupervised Environment Design (UED) seeks to automatically generate training curricula for reinforcement learning (RL) agents, with the goal of improving generalisation and zero-shot performance. However, designing effective curricula remains a difficult problem, particularly in settings where small subsets of environment parameterisations result in significant increases in the complexity of the required policy. Current methods struggle with a difficult credit assignment problem and rely on regret approximations that fail to identify challenging levels, both of which are compounded as the size of the environment grows. We propose Dynamic Environment Generation for UED (DEGen) to enable a denser level generator reward signal, reducing the difficulty of credit assignment and allowing for UED to scale to larger environment sizes. We also introduce a new regret approximation, Maximised Negative Advantage (MNA), as a significantly improved metric to optimise for, that better identifies more challenging levels. We show empirically that MNA outperforms current regret approximations and when combined with DEGen, consistently outperforms existing methods, especially as the size of the environment grows. We have made all our code available here: https://github.com/HarryMJMead/Dynamic-Environment-Generation-for-UED.

Problem

• Unsupervised Environment Design
• Regret Approximation
• Credit Assignment
• Reinforcement Learning
• Curriculum Generation

Innovation

• Unsupervised Environment Design
• Regret Approximation
• Maximised Negative Advantage
• Dynamic Environment Generation
• Reinforcement Learning