Learning to Steer Markovian Agents under Model Uncertainty

2024-07-14

arXiv.org

AI Summary

This work addresses the problem of designing dynamic rewards to guide Markovian agents toward desired policies in model-unknown multi-agent systems, without prior knowledge of learning mechanisms. We propose the first history-dependent, non-episodic reinforcement learning framework, establishing theoretical existence conditions for policy steerability. Our method integrates model-driven learning, historical state encoding, robust policy optimization, and Markov agent modeling, while introducing a novel objective function that jointly optimizes steering efficacy and reward cost. We theoretically prove the feasibility of reward-based guidance and empirically validate our algorithm: it achieves stable, efficient, and low-cost policy steering even under highly uncertain learning dynamics. The core contribution lies in eliminating the restrictive episodic assumption and dependence on prior system models—thereby establishing a new paradigm for black-box multi-agent cooperative control.

Technology Category

• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Designing incentives for an adapting population is a ubiquitous problem in a wide array of economic applications and beyond. In this work, we study how to design additional rewards to steer multi-agent systems towards desired policies emph{without} prior knowledge of the agents' underlying learning dynamics. Motivated by the limitation of existing works, we consider a new and general category of learning dynamics called emph{Markovian agents}. We introduce a model-based non-episodic Reinforcement Learning (RL) formulation for our steering problem. Importantly, we focus on learning a emph{history-dependent} steering strategy to handle the inherent model uncertainty about the agents' learning dynamics. We introduce a novel objective function to encode the desiderata of achieving a good steering outcome with reasonable cost. Theoretically, we identify conditions for the existence of steering strategies to guide agents to the desired policies. Complementing our theoretical contributions, we provide empirical algorithms to approximately solve our objective, which effectively tackles the challenge in learning history-dependent strategies. We demonstrate the efficacy of our algorithms through empirical evaluations.

Problem

• Design rewards for multi-agent systems without prior knowledge.
• Steer Markovian agents under model uncertainty using RL.
• Develop history-dependent strategies to achieve desired policies.

Innovation

• Model-based non-episodic Reinforcement Learning
• History-dependent steering strategy
• Novel objective function encoding desiderata