When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

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 . Figure 1: Autonomous agents struggle to make progress without external interventions when they are stuck in an irreversible state. Reinforcement learning agents therefore need active monitoring throughout training to detect and intervene when the agent reaches an irreversible state. Enabling the agents to detect irreversible states and proactively request for help can substantially reduce the human monitoring required for training agents.