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Reinforcement Learning With Human Advice: A Survey.

Anis Najar¹, Mohamed Chetouani²

¹Laboratoire de Neurosciences Cognitives Computationnelles, INSERM U960, Paris, France.

Frontiers in Robotics and AI

|June 18, 2021

Summary

This summary is machine-generated.

This paper reviews methods for incorporating human advice into reinforcement learning (RL). It categorizes advice types and details interpretation and integration techniques for RL agents.

Keywords:

advice-taking systems human-robot interaction interactive machine learning reinforcement learning unlabeled teaching signals

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Area of Science:

Artificial Intelligence
Machine Learning
Reinforcement Learning

Background:

Reinforcement learning (RL) agents learn through trial and error.
Integrating human expertise can accelerate and improve RL agent performance.
Existing methods for human advice integration in RL are diverse and require systematic organization.

Purpose of the Study:

To provide a comprehensive overview of current methods for integrating human advice into reinforcement learning.
To propose a novel taxonomy for classifying different forms of human advice.
To review techniques for interpreting ambiguous or context-dependent advice.

Main Methods:

Literature review of existing human advice integration methods in RL.
Development of a taxonomy to categorize advice based on its form and characteristics.
Analysis of methods for interpreting advice with undetermined meaning.
Categorization of integration approaches based on how advice influences the learning process.

Main Results:

A structured taxonomy of human advice types in RL is presented.
Methods for interpreting advice, especially when meaning is not predefined, are described.
Various approaches for integrating advice into the RL agent's learning loop are reviewed.

Conclusions:

A systematic understanding of human advice integration in RL is crucial for advancing the field.
The proposed taxonomy offers a framework for analyzing and developing new integration methods.
Effective interpretation and integration of human advice can significantly enhance RL agent capabilities.