# IDIL: Imitation Learning of Intent-Driven Expert Behavior

arXiv: https://arxiv.org/abs/2404.16989 · HTML: https://mlsanity.com/paper/2404.16989/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 22 of 22)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [IQ-Learn: Inverse soft-Q Learning for Imitation](https://mlsanity.com/paper/2106.12142/) (2021)
- [A Divergence Minimization Perspective on Imitation Learning Methods](https://mlsanity.com/paper/1911.02256/) (2019)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Hierarchical Imitation and Reinforcement Learning](https://mlsanity.com/paper/1803.00590/) (2018)
- [Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets](https://mlsanity.com/paper/1705.10479/) (2017)
- [Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction](https://mlsanity.com/paper/1710.09483/) (2017)
- [DAC: The Double Actor-Critic Architecture for Learning Options](https://mlsanity.com/paper/1904.12691/) (2019)
- [Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information](https://mlsanity.com/paper/1810.01266/) (2018)
- [On the Sample Complexity of Stability Constrained Imitation Learning](https://mlsanity.com/paper/2102.09161/) (2021)
- [Generative Modeling of Multimodal Multi-Human Behavior](https://mlsanity.com/paper/1803.02015/) (2018)
- [Adversarial Option-Aware Hierarchical Imitation Learning](https://mlsanity.com/paper/2106.05530/) (2021)
- [Non-Adversarial Imitation Learning and its Connections to Adversarial Methods](https://mlsanity.com/paper/2008.03525/) (2020)
- [Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control](https://mlsanity.com/paper/2210.01969/) (2022)
- [Rethinking ValueDice: Does It Really Improve Performance?](https://mlsanity.com/paper/2202.02468/) (2022)
- [Provable Hierarchical Imitation Learning via EM](https://mlsanity.com/paper/2010.03133/) (2020)
- [Automated Task-Time Interventions to Improve Teamwork using Imitation Learning](https://mlsanity.com/paper/2303.00413/) (2023)
- [GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation](https://mlsanity.com/paper/2312.10802/) (2023)

## Built on by (most cited first, 5 of 5)

- [Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations](https://mlsanity.com/paper/2502.17618/) (2025)
- [Socratic: Enhancing Human Teamwork via AI-enabled Coaching](https://mlsanity.com/paper/2502.17643/) (2025)
- [Adaptive Querying for Reward Learning from Human Feedback](https://mlsanity.com/paper/2412.07990/) (2024)
- [Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detection](https://mlsanity.com/paper/2509.17068/) (2025)
- [Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications](https://mlsanity.com/paper/2602.18582/) (2026)

## Abstract (arXiv)

When faced with accomplishing a task, human experts exhibit intentional behavior. Their unique intents shape their plans and decisions, resulting in experts demonstrating diverse behaviors to accomplish the same task. Due to the uncertainties encountered in the real world and their bounded rationality, experts sometimes adjust their intents, which in turn influences their behaviors during task execution. This paper introduces IDIL, a novel imitation learning algorithm to mimic these diverse intent-driven behaviors of experts. Iteratively, our approach estimates expert intent from heterogeneous demonstrations and then uses it to learn an intent-aware model of their behavior. Unlike contemporary approaches, IDIL is capable of addressing sequential tasks with high-dimensional state representations, while sidestepping the complexities and drawbacks associated with adversarial training (a mainstay of related techniques). Our empirical results suggest that the models generated by IDIL either match or surpass those produced by recent imitation learning benchmarks in metrics of task performance. Moreover, as it creates a generative model, IDIL demonstrates superior performance in intent inference metrics, crucial for human-agent interactions, and aptly captures a broad spectrum of expert behaviors.