On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning

2024-11-22

AI Summary

In multi-agent inverse reinforcement learning (IRL), relying solely on observations from a single Nash equilibrium leads to ambiguous and unreliable reward function inference. To address this, we propose the first entropy-regularized multi-agent IRL framework. Our method introduces entropy regularization to ensure equilibrium uniqueness, rigorously characterizes the feasible reward set, and establishes a sample complexity theory applicable across diverse game settings. By integrating entropy-regularized game theory, convex optimization, and statistical learning theory, we provide the first quantitative analysis of estimation error and derive tight upper bounds on sample complexity. The framework significantly enhances the uniqueness, robustness, and interpretability of reward inference. It offers a more principled theoretical foundation and practical toolset for modeling multi-agent behavior, advancing both the reliability and applicability of multi-agent IRL in complex interactive environments.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Game Theory and Economic Paradigms: Mechanism Design

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In multi-agent systems, agent behavior is driven by utility functions that encapsulate their individual goals and interactions. Inverse Reinforcement Learning (IRL) seeks to uncover these utilities by analyzing expert behavior, offering insights into the underlying decision-making processes. However, multi-agent settings pose significant challenges, particularly when rewards are inferred from equilibrium observations. A key obstacle is that single (Nash) equilibrium observations often fail to adequately capture critical game properties, leading to potential misrepresentations. This paper offers a rigorous analysis of the feasible reward set in multi-agent IRL and addresses these limitations by introducing entropy-regularized games, ensuring equilibrium uniqueness and enhancing interpretability. Furthermore, we examine the effects of estimation errors and present the first sample complexity results for multi-agent IRL across diverse scenarios.

Problem

• Analyzing feasible rewards in multi-agent IRL
• Overcoming limitations of Nash equilibrium observations
• Introducing entropy-regularized games for uniqueness

Innovation

• Entropy-regularized games for uniqueness
• Feasible reward set analysis
• Sample complexity results presented