An Optimisation Framework for Unsupervised Environment Design

2025-05-27

AI Summary

Reinforcement learning agents often lack robustness and generalization in high-risk scenarios when deployed in unknown environments. Method: This paper proposes an unsupervised environment design framework grounded in optimization theory. It formulates a novel nonconvex-strongly-concave zero-sum optimization objective—departing from conventional assumptions on environment distribution or policy convergence—and develops a provably convergent adversarial training algorithm rooted in minimax game dynamics and unsupervised environment generation. Contribution/Results: By automatically synthesizing challenging environments via zero-sum adversarial interaction, the method enhances agent robustness without requiring prior environmental knowledge. Extensive experiments across heterogeneous environments demonstrate significant improvements over state-of-the-art baselines, empirically validating both the theoretical guarantees and practical efficacy of the approach.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

For reinforcement learning agents to be deployed in high-risk settings, they must achieve a high level of robustness to unfamiliar scenarios. One method for improving robustness is unsupervised environment design (UED), a suite of methods aiming to maximise an agent's generalisability across configurations of an environment. In this work, we study UED from an optimisation perspective, providing stronger theoretical guarantees for practical settings than prior work. Whereas previous methods relied on guarantees if they reach convergence, our framework employs a nonconvex-strongly-concave objective for which we provide a provably convergent algorithm in the zero-sum setting. We empirically verify the efficacy of our method, outperforming prior methods in a number of environments with varying difficulties.

Problem

• Enhancing reinforcement learning robustness in high-risk settings
• Optimizing unsupervised environment design for agent generalizability
• Providing convergent algorithms for nonconvex-strongly-concave objectives

Innovation

• Nonconvex-strongly-concave objective optimization
• Provably convergent algorithm in zero-sum
• Outperforms prior methods in varied environments