# Bayesian Nonparametric Feature and Policy Learning for Decision-Making

## TL;DR

This paper introduces a Bayesian nonparametric model for learning decision-making behaviors from demonstrations, focusing on latent features and policies to understand and predict actions in complex environments.

## Contribution

It proposes a novel generative model that infers the number of features, their structure, and policies, enabling analysis and prediction of observed behaviors.

## Key findings

- Effective in learning latent features from demonstrations
- Accurately predicts actions in new states
- Demonstrates real-world applicability in driver behavior modeling

## Abstract

Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Therefore, we propose a generative model for the states and actions. Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the observed behavior. Further, our approach enables prediction of actions for new states. Simulations are used to assess the performance of the algorithm based upon this model. Moreover, the problem of learning a driver's behavior is investigated, demonstrating the performance of the proposed model in a real-world scenario.