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Main Authors: An, Zhiyu, Ding, Xianzhong, Du, Wan
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2402.13419
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author An, Zhiyu
Ding, Xianzhong
Du, Wan
author_facet An, Zhiyu
Ding, Xianzhong
Du, Wan
contents Recent years have seen an emerging interest in the trustworthiness of machine learning-based agents in the wild, especially in robotics, to provide safety assurance for the industry. Obtaining behavioral guarantees for these agents remains an important problem. In this work, we focus on guaranteeing a model-based planning agent reaches a goal state within a specific future time step. We show that there exists a lower bound for the reward at the goal state, such that if the said reward is below that bound, it is impossible to obtain such a guarantee. By extension, we show how to enforce preferences over multiple goals.
format Preprint
id arxiv_https___arxiv_org_abs_2402_13419
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Reward Bound for Behavioral Guarantee of Model-based Planning Agents
An, Zhiyu
Ding, Xianzhong
Du, Wan
Artificial Intelligence
Recent years have seen an emerging interest in the trustworthiness of machine learning-based agents in the wild, especially in robotics, to provide safety assurance for the industry. Obtaining behavioral guarantees for these agents remains an important problem. In this work, we focus on guaranteeing a model-based planning agent reaches a goal state within a specific future time step. We show that there exists a lower bound for the reward at the goal state, such that if the said reward is below that bound, it is impossible to obtain such a guarantee. By extension, we show how to enforce preferences over multiple goals.
title Reward Bound for Behavioral Guarantee of Model-based Planning Agents
topic Artificial Intelligence
url https://arxiv.org/abs/2402.13419