Automated Feature Selection for Inverse Reinforcement Learning: A Method Using Polynomial Basis Functions

المفاهيم الأساسية

Efficient feature selection using polynomial basis functions enhances reward function learning in Inverse Reinforcement Learning.

الملخص

The content introduces a method for automated feature selection in Inverse Reinforcement Learning (IRL) using polynomial basis functions. It addresses the challenge of selecting relevant features to represent rewards, improving interpretability and generalization. The approach is detailed through algorithmic steps, highlighting the effectiveness of polynomial features and feature selection mechanisms. Experiments across different tasks demonstrate the method's success compared to baselines, achieving benchmark results with fewer features. The study discusses implications, potential refinements, and future applications.
Directory:

Abstract

IRL as imitation learning from expert demonstrations.
Reward representation as linear combination of features.

Introduction

Behavioral cloning vs. inverse reinforcement learning (IRL).
Benefits of IRL in circumventing manual reward design challenges.

Related Works

Evolution of IRL in continuous state spaces.
Challenges in feature selection for reward functions.

Method

Proposal to use polynomials as candidate features.
Feature selection based on trajectory probabilities and expectations.

Background

Definition of Reinforcement Learning (RL) and IRL.

Implementation Details

Python implementation details and hyperparameters used.

Results

Comparison of proposed method with baselines across tasks.
Performance evaluation through mean cumulative rewards and Wasserstein distance metrics.

Discussion & Conclusion

Effectiveness of polynomial basis functions for feature extraction in IRL.

الإحصائيات

"Code, data, and videos are available at https://sites.google.com/view/feature4irl."
"This work was supported by the Academy of Finland under grant 347199."

اقتباسات

"The primary contributions of the paper are showing that polynomial basis functions are effective as a candidate set of features."
"Our method attains comparable performance levels using significantly fewer features."
"Employing a more concise set of features enhances the generalization and robustness of inferred rewards."

الرؤى الأساسية المستخلصة من

Automated Feature Selection for Inverse Reinforcement Learning

by Daulet Baimu... في arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15079.pdf

استفسارات أعمق

How can this automated feature selection method be adapted to other domains beyond robotics

The automated feature selection method based on polynomial basis functions can be adapted to various domains beyond robotics by adjusting the types of features used and the specific tasks at hand. For instance, in healthcare applications, these methods could be utilized to extract relevant features from patient data for personalized treatment recommendations or disease diagnosis. By incorporating domain-specific knowledge and tailoring the feature selection process to suit the unique characteristics of each field, this approach can effectively identify key patterns and relationships within complex datasets. Moreover, in finance, such techniques could aid in analyzing market trends, risk assessment, or fraud detection by extracting essential features from financial data streams.

What are the potential drawbacks or limitations of relying solely on polynomial basis functions for feature extraction

While polynomial basis functions offer a flexible and effective way to capture complex relationships between states and rewards in Inverse Reinforcement Learning (IRL), there are potential drawbacks and limitations to relying solely on them for feature extraction. One limitation is that polynomials may struggle with capturing highly nonlinear relationships present in some environments or datasets. This could lead to suboptimal performance when trying to model intricate reward structures that cannot be adequately represented by polynomial functions alone. Additionally, using only polynomial basis functions might result in overfitting if the chosen degree is too high relative to the dataset size, leading to poor generalization performance on unseen data.

How might incorporating different types of basis functions impact the efficiency and accuracy of feature selection in IRL

Incorporating different types of basis functions alongside polynomial ones could enhance the efficiency and accuracy of feature selection in IRL by providing a more diverse set of tools for capturing varying patterns within data. By combining multiple types of basis functions such as Fourier series or radial-basis functions with polynomials, it becomes possible to represent a wider range of state-feature mappings accurately. This increased flexibility allows for better adaptation to different problem domains where certain types of features may perform better than others due to their inherent characteristics. Furthermore, leveraging a mix of basis functions can help mitigate issues related to underfitting or limited expressiveness that may arise when relying solely on one type of function for feature extraction.