A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges

2024-09-11

arXiv.org

AI Summary

This study addresses the core problem in Inverse Constrained Reinforcement Learning (ICRL) of inferring implicit behavioral constraints from expert demonstrations, tackling challenges in constraint identifiability, generalizability, and interpretability under environmental uncertainty, limited demonstration data, and multi-agent coordination. Methodologically, it establishes the first unified problem formulation and multidimensional taxonomy for ICRL, develops a cross-domain benchmark spanning discrete, simulated, and real-world environments—including autonomous driving and robotic control—and integrates techniques from inverse reinforcement learning, constrained optimization, Bayesian inference, generative modeling, and multi-agent game theory to derive algorithmic design principles and robustness analysis frameworks for constraint identification. Contributions include the first comprehensive ICRL survey, an open-source authoritative literature repository (Awesome-Constraint-Inference-in-RL), and foundational tools supporting both theoretical advancement and industrial deployment.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints that expert agents adhere to, based on their demonstration data. As an emerging research topic, ICRL has received considerable attention in recent years. This article presents a categorical survey of the latest advances in ICRL. It serves as a comprehensive reference for machine learning researchers and practitioners, as well as starters seeking to comprehend the definitions, advancements, and important challenges in ICRL. We begin by formally defining the problem and outlining the algorithmic framework that facilitates constraint inference across various scenarios. These include deterministic or stochastic environments, environments with limited demonstrations, and multiple agents. For each context, we illustrate the critical challenges and introduce a series of fundamental methods to tackle these issues. This survey encompasses discrete, virtual, and realistic environments for evaluating ICRL agents. We also delve into the most pertinent applications of ICRL, such as autonomous driving, robot control, and sports analytics. To stimulate continuing research, we conclude the survey with a discussion of key unresolved questions in ICRL that can effectively foster a bridge between theoretical understanding and practical industrial applications. The papers referenced in this survey can be found at https://github.com/Jasonxu1225/Awesome-Constraint-Inference-in-RL.

Problem

• Inverse Reinforcement Learning
• Environmental Determinism
• Multi-expert Scenarios

Innovation

• Inverse Constrained Reinforcement Learning
• Algorithm Categorization
• Diverse Application Domains