YRC-Bench: A Benchmark for Learning to Coordinate with Experts

TL;DR

YRC-Bench introduces a benchmark for training AI agents to recognize when to seek expert help in new environments without prior expert interaction, promoting safer and more reliable autonomous decision-making.

Contribution

The paper presents YRC-Bench, an open-source benchmark for the novel YRC-0 problem, enabling research on unsupervised learning to coordinate with experts in diverse environments.

Findings

Proposed a validation strategy for YRC-0

Developed a proposer-validator diagnostic framework

Provided baseline implementations and evaluation pipeline

Abstract

When deployed in the real world, AI agents will inevitably face challenges that exceed their individual capabilities. A critical component of AI safety is an agent's ability to recognize when it is likely to fail in a novel situation and to yield control to a more capable expert system. Leveraging such expert assistance can significantly improve safety and performance in such situations. Since expert assistance is costly, a central challenge is determining when to consult an expert. In this paper, we explore a novel variant of this problem, termed YRC-0, in which an agent must learn to collaborate with an expert in new environments in an unsupervised manner--that is, without interacting with the expert during training. This setting motivates the development of low-cost, robust approaches for training expert-leveraging agents. To support research in this area, we introduce YRC-Bench, an open-source benchmark that instantiates YRC-0 across diverse environments. YRC-Bench provides a standardized Gym-like API, simulated experts, an evaluation pipeline, and implementations of popular baselines. Toward tackling YRC-0, we propose a validation strategy and use a proposer-validator decomposition as a diagnostic framework to evaluate a range of learning methods, offering insights that can inform future research. Codebase: https://github.com/modanesh/YRC-Bench