Heterogeneous Adversarial Play in Interactive Environments

2025-10-21

AI Summary

Traditional self-play frameworks assume agent symmetry, making them ill-suited for the inherent task and capability asymmetries in open-ended learning. To address this, we propose the Heterogeneous Adversarial Learning framework (HAP), which formalizes asymmetric teaching as a bidirectional min-max optimization process—enabling automatic curriculum generation without predefined task hierarchies. HAP dynamically synthesizes learner-adaptive task sequences via a teacher-student adversarial mechanism, real-time performance feedback modulation, and co-evolution of strategies. Extensive experiments across multiple domains demonstrate that HAP significantly accelerates learning convergence and improves final performance, achieving state-of-the-art results. Moreover, HAP exhibits strong generalization across both artificial agents and human learners, underscoring its broad applicability in adaptive educational and reinforcement learning settings.

Technology Category

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

Application Category

• 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
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

Self-play constitutes a fundamental paradigm for autonomous skill acquisition, whereby agents iteratively enhance their capabilities through self-directed environmental exploration. Conventional self-play frameworks exploit agent symmetry within zero-sum competitive settings, yet this approach proves inadequate for open-ended learning scenarios characterized by inherent asymmetry. Human pedagogical systems exemplify asymmetric instructional frameworks wherein educators systematically construct challenges calibrated to individual learners' developmental trajectories. The principal challenge resides in operationalizing these asymmetric, adaptive pedagogical mechanisms within artificial systems capable of autonomously synthesizing appropriate curricula without predetermined task hierarchies. Here we present Heterogeneous Adversarial Play (HAP), an adversarial Automatic Curriculum Learning framework that formalizes teacher-student interactions as a minimax optimization wherein task-generating instructor and problem-solving learner co-evolve through adversarial dynamics. In contrast to prevailing ACL methodologies that employ static curricula or unidirectional task selection mechanisms, HAP establishes a bidirectional feedback system wherein instructors continuously recalibrate task complexity in response to real-time learner performance metrics. Experimental validation across multi-task learning domains demonstrates that our framework achieves performance parity with SOTA baselines while generating curricula that enhance learning efficacy in both artificial agents and human subjects.

Problem

• Addresses limitations of symmetric self-play in asymmetric learning scenarios
• Operationalizes adaptive pedagogical mechanisms for autonomous curriculum synthesis
• Establishes bidirectional feedback between task generation and learner performance

Innovation

• Adversarial teacher-student co-evolution through minimax optimization
• Bidirectional feedback system for dynamic task complexity calibration
• Automatic curriculum learning without predefined task hierarchies