Scholarlink

This chapter introduces the fascinating field of inverse reinforcement learning (IRL), a paradigm that aims to infer the underlying reward function from observed behavior. We explore the motivation behind IRL, its formulation, and various strategies for solving IRL problems. The chapter also draws connections between IRL and generative adversarial networks (GANs), highlighting the interdisciplinary nature of this field. We will also motivate insights into how IRL can be used to understand and replicate complex behaviors in various domains, from robotics to human behavior modeling.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Inverse Reinforcement Learning Problem

  • 章节

  • 发表日期：

    2025年

查看全文
  • Baihan Lin

摘要

This chapter introduces the fascinating field of inverse reinforcement learning (IRL), a paradigm that aims to infer the underlying reward function from observed behavior. We explore the motivation behind IRL, its formulation, and various strategies for solving IRL problems. The chapter also draws connections between IRL and generative adversarial networks (GANs), highlighting the interdisciplinary nature of this field. We will also motivate insights into how IRL can be used to understand and replicate complex behaviors in various domains, from robotics to human behavior modeling.