AGPNet -- Autonomous Grading Policy Network

• URL: http://arxiv.org/abs/2112.10877v1
• Date: Mon, 20 Dec 2021 21:44:21 GMT
• Title: AGPNet -- Autonomous Grading Policy Network
• Authors: Chana Ross, Yakov Miron, Yuval Goldfracht, Dotan Di Castro
• Abstract summary: We formalize the problem as a Markov Decision Process and design a simulation which demonstrates agent-environment interactions. We use methods from reinforcement learning, behavior cloning and contrastive learning to train a hybrid policy. Our trained agent, AGPNet, reaches human-level performance and outperforms current state-of-the-art machine learning methods for the autonomous grading task.
• Score: 0.5232537118394002
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we establish heuristics and learning strategies for the autonomous control of a dozer grading an uneven area studded with sand piles. We formalize the problem as a Markov Decision Process, design a simulation which demonstrates agent-environment interactions and finally compare our simulator to a real dozer prototype. We use methods from reinforcement learning, behavior cloning and contrastive learning to train a hybrid policy. Our trained agent, AGPNet, reaches human-level performance and outperforms current state-of-the-art machine learning methods for the autonomous grading task. In addition, our agent is capable of generalizing from random scenarios to unseen real world problems.

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