Modeling and Learning Intent-Driven Expert Behavior for Sequential Decision-Making Tasks

Core Concepts

This paper introduces IDIL, a novel imitation learning algorithm that can effectively model and learn intent-driven expert behavior in sequential decision-making tasks. IDIL is capable of capturing the diversity in expert behaviors arising from differences in their intents, even when the intents are unobservable.

Abstract

The paper introduces the problem of learning intent-driven expert behavior in sequential decision-making tasks. It proposes a novel algorithm called IDIL (Intent-Driven Imitation Learner) to address this problem.

Key highlights:

1. Expert behavior is often influenced by their intents, which are unobservable. Existing imitation learning approaches that assume behavior depends only on the observable task context are insufficient to capture this complexity.
2. IDIL learns an intent-aware model of expert behavior by iteratively estimating the expert's intent from heterogeneous demonstrations and then using it to learn an intent-aware policy.
3. IDIL builds upon recent advances in classical imitation learning to enable stable learning in tasks with large or continuous state spaces, unlike prior intent-aware methods that rely on adversarial training.
4. The paper provides theoretical analysis to derive sufficient conditions for the convergence of IDIL, and empirically evaluates its performance on a range of benchmark domains.
5. The experiments demonstrate that IDIL either matches or surpasses recent imitation learning baselines in terms of task performance, while also exhibiting superior intent inference capabilities and generating a broader spectrum of expert behaviors.

Stats

"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."

Quotes

"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."

Key Insights Distilled From

IDIL: Imitation Learning of Intent-Driven Expert Behavior

by Sangwon Seo,... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16989.pdf

Deeper Inquiries

How can IDIL be extended to handle continuous or high-dimensional action spaces, in addition to state spaces

To extend IDIL to handle continuous or high-dimensional action spaces, we can modify the policy learning step in the algorithm. Currently, IDIL learns the expert policy by matching the occupancy measure of the joint state-intent space. For continuous action spaces, we can use function approximation techniques such as neural networks to represent the policy. By parameterizing the policy with a neural network, IDIL can learn a continuous action distribution conditioned on the joint state-intent space. This allows IDIL to handle continuous or high-dimensional action spaces effectively.

What are the potential limitations of IDIL in terms of its ability to capture complex, multi-modal intent dynamics

One potential limitation of IDIL in capturing complex, multi-modal intent dynamics is the assumption of a fixed number of intents. In real-world scenarios, human intent can be multi-modal, meaning that experts may have varying intentions that cannot be captured by a fixed number of intent categories. IDIL's performance may be limited in cases where the number of intents is not known a priori or when experts exhibit diverse and evolving intents that do not fit into a predefined set of categories. Additionally, IDIL may struggle to capture subtle changes in intent that manifest as small variations in behavior, especially in tasks with high-dimensional state spaces where intent may not have a clear representation.

How can the insights from IDIL be leveraged to develop intent-aware reinforcement learning algorithms for human-AI collaboration tasks

The insights from IDIL can be leveraged to develop intent-aware reinforcement learning algorithms for human-AI collaboration tasks by incorporating intent inference and modeling into the reinforcement learning framework. By integrating intent prediction mechanisms similar to those used in IDIL, reinforcement learning agents can anticipate and adapt to human intentions during collaborative tasks. This can lead to more effective coordination between humans and AI systems, improving task performance and overall user experience. Additionally, by incorporating intent-awareness into reinforcement learning algorithms, agents can better understand and respond to human behavior, leading to more natural and efficient human-AI interactions.