Learning to Coordinate with Experts

When deployed in dynamic environments, AI agents will inevitably encounter challenges that exceed their individual capabilities. Leveraging assistance from expert agents-whether human or AI-can significantly enhance safety and performance in such situations. However, querying experts is often costly, necessitating the development of agents that can efficiently request and utilize expert guidance. In this paper, we introduce a fundamental coordination problem called Learning to Yield and Request Control (YRC), where the objective is to learn a strategy that determines when to act autonomously and when to seek expert assistance. We consider a challenging practical setting in which an agent does not interact with experts during training but must adapt to novel environmental changes and expert interventions at test time. To facilitate empirical research, we introduce YRC-Bench, an open-source benchmark featuring diverse domains. YRC-Bench provides a standardized Gym-like API, simulated experts, evaluation pipeline, and implementation of competitive baselines. Towards tackling the YRC problem, we propose a novel validation approach and investigate the performance of various learning methods across diverse environments, yielding insights that can guide future research.

Getting By Goal Misgeneralization With a Little Help From a Mentor

While reinforcement learning (RL) agents often perform well during training, they can struggle with distribution shift in real-world deployments. One particularly severe risk of distribution shift is goal misgeneralization, where the agent learns a proxy goal that coincides with the true goal during training but not during deployment. In this paper, we explore whether allowing an agent to ask for help from a supervisor in unfamiliar situations can mitigate this issue. We focus on agents trained with PPO in the CoinRun environment, a setting known to exhibit goal misgeneralization. We evaluate multiple methods for determining when the agent should request help and find that asking for help consistently improves performance. However, we also find that methods based on the agent's internal state fail to proactively request help, instead waiting until mistakes have already occurred. Further investigation suggests that the agent's internal state does not represent the coin at all, highlighting the importance of learning nuanced representations, the risks of ignoring everything not immediately relevant to reward, and the necessity of developing ask-for-help strategies tailored to the agent's training algorithm.