[Paper Review] When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

Annie Xie, Fahim Tajwar+2 more|arXiv (Cornell University)|Oct 19, 2022

Reinforcement Learning in Robotics4 citations

TL;DR

This paper proposes PAINT, a proactive intervention framework for autonomous reinforcement learning that reduces human monitoring by learning to detect and avoid irreversible states. By using a label-efficient binary search to query reversibility labels only when necessary and combining this with a classifier to predict irreversible states, PAINT achieves up to 15× fewer interventions—requiring only ~100 interventions over 3 million steps—while maintaining high sample and intervention efficiency in continuous control tasks.

ABSTRACT

A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge to such autonomy is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. While standard agents require constant monitoring to decide when to intervene, we aim to design proactive agents that can request human intervention only when needed. To this end, we propose an algorithm that efficiently learns to detect and avoid states that are irreversible, and proactively asks for help in case the agent does enter them. On a suite of continuous control environments with unknown irreversible states, we find that our algorithm exhibits better sample- and intervention-efficiency compared to existing methods. Our code is publicly available at https://sites.google.com/view/proactive-interventions

Motivation & Objective

• To reduce human monitoring in reinforcement learning by enabling agents to autonomously detect and request help when entering irreversible states.
• To minimize the number of reversibility labels required during training, which are typically costly to obtain from human supervisors.
• To design a framework that combines efficient label acquisition with proactive intervention, improving autonomy in real-world RL settings with irreversible dynamics.
• To evaluate the robustness of the method under noisy labels and stochastic transitions, ensuring practical deployability.

Proposed method

• Proposes a reversibility-aware Q-value function that penalizes visits to irreversible states, encouraging the agent to avoid them during learning.
• Employs a binary search-based labeling scheme that determines reversibility of states in a trajectory using at most O(log T) queries, compared to O(T) in standard safe RL.
• Introduces a batch labeling strategy, where reversibility labels are queried only at the end of extended episodes, reducing real-time supervision.
• Trains a classifier to predict reversibility of states using labeled data, enabling proactive intervention requests when high-uncertainty or high-risk states are detected.
• Incorporates confidence-based and ensemble-based uncertainty estimation to further reduce label queries by querying only when prediction confidence is low.
• Implements a robust variant that uses a sliding window of neighboring states to average labels, improving tolerance to noisy human-provided reversibility labels.

Experimental results

Research questions

• RQ1 Can a reinforcement learning agent learn to detect irreversible states with minimal human supervision?
• RQ2 How can reversibility labels be acquired efficiently to reduce the number of human queries during training?
• RQ3 To what extent can a classifier predict irreversible states to enable proactive intervention, reducing the need for real-time monitoring?
• RQ4 How robust is the method to noisy reversibility labels and stochastic environment dynamics?
• RQ5 Can the framework maintain high sample and intervention efficiency across diverse continuous control tasks with unknown irreversible states?

Key findings

• PAINT reduces the number of required interventions to approximately 100 over 3 million training steps, representing up to 15× fewer interventions than prior algorithms.
• The label-efficient binary search scheme reduces reversibility label queries from O(T) to O(log T) per trajectory, significantly lowering human labeling burden.
• Using confidence-based querying, PAINT reduces label count to fewer than 100 labels—down from 3,000 in standard PAINT—while maintaining high success rates.
• With ensemble-based uncertainty querying, PAINT achieves high performance using only around 750 labels when the uncertainty threshold is set to 0.0001.
• The robust variant of PAINT tolerates up to 20% false positive or false negative labels with a window size of 10, maintaining 60–80% success rates.
• PAINT remains robust under noisy transitions up to a noise level of σ = 0.5, but fails when noise exceeds the action magnitude (σ = 1.0).