[Paper Review] Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning

El Mahdi El Mhamdi, Rachid Guerraoui+2 more|arXiv (Cornell University)|Apr 10, 2017

Data Stream Mining TechniquesComputer Science18 citations

TL;DR

This paper introduces dynamic safe interruptibility, a new framework for ensuring that decentralized multi-agent reinforcement learning (MARL) systems do not learn to avoid human interruptions. By using interruption-aware observation pruning via a processing function $P_{INT}$, the method preserves learning dynamics and infinite exploration even when agents are intermittently interrupted, ensuring safe behavior without requiring communication or coordination between agents.

ABSTRACT

In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to extit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learning process, agents may link these interruptions, that impact their reward, to specific states and deliberately avoid them. The situation is particularly challenging in a multi-agent context because agents might not only learn from their own past interruptions, but also from those of other agents. Orseau and Armstrong defined \emph{safe interruptibility} for one learner, but their work does not naturally extend to multi-agent systems. This paper introduces extit{dynamic safe interruptibility}, an alternative definition more suited to decentralized learning problems, and studies this notion in two learning frameworks: extit{joint action learners} and extit{independent learners}. We give realistic sufficient conditions on the learning algorithm to enable dynamic safe interruptibility in the case of joint action learners, yet show that these conditions are not sufficient for independent learners. We show however that if agents can detect interruptions, it is possible to prune the observations to ensure dynamic safe interruptibility even for independent learners.

Motivation & Objective

• Address the challenge of safe interruptibility in decentralized multi-agent reinforcement learning, where agents may learn to avoid interruptions by observing others.
• Overcome the limitations of single-agent safe interruptibility definitions, which fail in multi-agent settings due to co-adaptation and indirect learning from others.
• Propose a new definition—dynamic safe interruptibility—that ensures learning stability and optimal policy convergence despite interruptions.
• Establish sufficient conditions for dynamic safe interruptibility in joint action learners and show its feasibility in independent learners via interruption-aware processing.
• Demonstrate that interruption signals can be used to prune harmful training data, restoring safe learning dynamics in independent learners.

Proposed method

• Define dynamic safe interruptibility as a property ensuring that the expected policy learned under interruptions matches the optimal policy in the absence of interruptions.
• Introduce an interruption processing function $P_{INT}$ that removes observations from steps where any agent was interrupted, based on a signal $\Theta_t$.
• Use a neutral update rule and ensure the sequence $\epsilon$ of exploration probabilities remains compatible with interruptions.
• Leverage Lemma 1 to prove that transition probabilities conditionally on state and action are preserved in the pruned sequence, independent of interruption history.
• Apply this framework to independent learners by assuming agents receive interruption signals at each step, enabling them to distinguish between interrupted and normal transitions.
• Prove Theorem 4: independent learners with $P_{INT}$ and compatible $\epsilon$ sequences achieve dynamic safe interruptibility.

Experimental results

Research questions

• RQ1 Can safe interruptibility be meaningfully extended to decentralized multi-agent reinforcement learning systems where agents do not communicate?
• RQ2 Why do standard safe interruptibility definitions fail in multi-agent settings, particularly when agents co-adapt or observe each other’s interruptions?
• RQ3 Under what conditions can independent learners achieve dynamic safe interruptibility despite not sharing information?
• RQ4 How can interruption signals be used to modify learning dynamics so that agents do not learn to exploit or avoid interruptions?
• RQ5 Is it possible to preserve infinite exploration and one-step learning dynamics in interrupted MARL environments?

Key findings

• Dynamic safe interruptibility is a necessary and more realistic extension of safe interruptibility to decentralized multi-agent systems, where agents may learn from others’ interruptions.
• For joint action learners, sufficient conditions exist to achieve dynamic safe interruptibility, preserving optimal policy learning under interruptions.
• For independent learners, standard safe interruptibility is not sufficient due to indirect influence from other agents’ interruption patterns.
• With interruption signals available, pruning interrupted observations via $P_{INT}$ restores dynamic safe interruptibility in independent learners.
• Lemma 1 proves that conditional transition probabilities remain unchanged after pruning, ensuring that learning dynamics are not biased by interruption history.
• Theorem 4 establishes that independent learners with $P_{INT}$, a neutral update rule, and compatible $\epsilon$ sequences achieve dynamic safe interruptibility.