A Model-Based Approach to Imitation Learning through Multi-Step Predictions

2025-04-18

AI Summary

Imitation learning suffers from two interrelated challenges: error accumulation and train-deployment distributional shift, leading to poor generalization. To address these, we propose a multi-step state prediction framework inspired by model predictive control (MPC), establishing the first model-based imitation learning paradigm that unifies multi-step forward dynamics modeling with behavior cloning. Our method explicitly models system dynamics and jointly optimizes multi-step state predictions and action outputs. We theoretically derive upper bounds on sample complexity and policy error convergence. Empirically, evaluated on standard benchmarks, our approach significantly outperforms conventional behavior cloning. It demonstrates superior robustness to both distributional shift and observation noise, validating its practical effectiveness while maintaining theoretical rigor.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Behavior Learning & Control

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Imitation learning is a widely used approach for training agents to replicate expert behavior in complex decision-making tasks. However, existing methods often struggle with compounding errors and limited generalization, due to the inherent challenge of error correction and the distribution shift between training and deployment. In this paper, we present a novel model-based imitation learning framework inspired by model predictive control, which addresses these limitations by integrating predictive modeling through multi-step state predictions. Our method outperforms traditional behavior cloning numerical benchmarks, demonstrating superior robustness to distribution shift and measurement noise both in available data and during execution. Furthermore, we provide theoretical guarantees on the sample complexity and error bounds of our method, offering insights into its convergence properties.

Problem

• Addresses compounding errors in imitation learning
• Improves generalization across training and deployment distributions
• Enhances robustness to data noise and distribution shifts

Innovation

• Model-based imitation learning framework
• Multi-step state predictions integration
• Superior robustness to distribution shift