AGPNet -- Autonomous Grading Policy Network
Chana Ross, Yakov Miron, Yuval Goldfracht, Dotan Di Castro

TL;DR
This paper introduces AGPNet, a hybrid reinforcement learning agent for autonomous dozer grading, which achieves human-level performance and generalizes well to real-world scenarios.
Contribution
It formalizes autonomous grading as a Markov Decision Process and develops a hybrid learning approach outperforming existing methods.
Findings
AGPNet reaches human-level performance.
The agent outperforms current state-of-the-art methods.
AGPNet generalizes to unseen real-world scenarios.
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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Taxonomy
TopicsFlood Risk Assessment and Management · Water Quality Monitoring Technologies
MethodsContrastive Learning
